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6/9/2024

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lennlouis 2024-06-09 02:54:44 +08:00
parent c1d9ee6118
commit 6f6a1820ca
46 changed files with 530 additions and 32460 deletions
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MainWindow.ui
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@ -55,20 +55,6 @@
</property> </property>
</widget> </widget>
</item> </item>
<item>
<widget class="QPushButton" name="pushButton_4">
<property name="text">
<string>PushButton</string>
</property>
</widget>
</item>
<item>
<widget class="QPushButton" name="pushButton_5">
<property name="text">
<string>PushButton</string>
</property>
</widget>
</item>
</layout> </layout>
</item> </item>
<item> <item>
@ -77,110 +63,224 @@
<number>0</number> <number>0</number>
</property> </property>
<widget class="QWidget" name="page"> <widget class="QWidget" name="page">
<layout class="QVBoxLayout" name="verticalLayout_4"> <layout class="QVBoxLayout" name="verticalLayout_5">
<item> <item>
<widget class="QTableWidget" name="tableWidget_linear_regression"/> <widget class="QTableWidget" name="tableWidget_linear_regression"/>
</item> </item>
<item> <item>
<layout class="QVBoxLayout" name="verticalLayout_3"> <layout class="QHBoxLayout" name="horizontalLayout_5">
<item> <item>
<layout class="QHBoxLayout" name="horizontalLayout_3"> <layout class="QVBoxLayout" name="verticalLayout_4">
<property name="spacing">
<number>0</number>
</property>
<item> <item>
<widget class="QCheckBox" name="checkBox_linear_regression_data_cleaning"> <layout class="QHBoxLayout" name="horizontalLayout_3">
<property name="text"> <item>
<string>数据清洗</string> <widget class="QGroupBox" name="groupBox">
</property> <property name="title">
</widget> <string>标签</string>
</property>
<layout class="QGridLayout" name="gridLayout">
<item row="0" column="0">
<widget class="QLabel" name="label_linear_regression_original">
<property name="text">
<string>原始数据</string>
</property>
</widget>
</item>
<item row="0" column="1">
<widget class="QLineEdit" name="lineEdit_linear_regression_original">
<property name="sizePolicy">
<sizepolicy hsizetype="Minimum" vsizetype="Minimum">
<horstretch>0</horstretch>
<verstretch>0</verstretch>
</sizepolicy>
</property>
<property name="placeholderText">
<string>原始数据Title</string>
</property>
</widget>
</item>
<item row="1" column="0">
<widget class="QLabel" name="label_linear_regression_target">
<property name="text">
<string>目标数据</string>
</property>
</widget>
</item>
<item row="1" column="1">
<widget class="QLineEdit" name="lineEdit_linear_regression_target">
<property name="sizePolicy">
<sizepolicy hsizetype="Minimum" vsizetype="Fixed">
<horstretch>0</horstretch>
<verstretch>0</verstretch>
</sizepolicy>
</property>
<property name="placeholderText">
<string>目标数据Title</string>
</property>
</widget>
</item>
</layout>
</widget>
</item>
<item>
<widget class="QGroupBox" name="groupBox_2">
<property name="title">
<string>学习参数</string>
</property>
<layout class="QGridLayout" name="gridLayout_2">
<item row="0" column="0">
<widget class="QLabel" name="label_linear_regression_iter">
<property name="text">
<string>迭代次数</string>
</property>
</widget>
</item>
<item row="1" column="0">
<widget class="QLabel" name="label_linear_regression_rate">
<property name="text">
<string>学习率</string>
</property>
</widget>
</item>
<item row="0" column="1">
<widget class="QSpinBox" name="spinBox_linear_regression_iter">
<property name="minimum">
<number>1</number>
</property>
<property name="maximum">
<number>10000</number>
</property>
<property name="value">
<number>500</number>
</property>
</widget>
</item>
<item row="1" column="1">
<widget class="QDoubleSpinBox" name="doubleSpinBox_linear_regression_rate">
<property name="maximum">
<double>1.000000000000000</double>
</property>
<property name="singleStep">
<double>0.010000000000000</double>
</property>
<property name="value">
<double>0.030000000000000</double>
</property>
</widget>
</item>
</layout>
</widget>
</item>
<item>
<widget class="QGroupBox" name="groupBox_3">
<property name="title">
<string>特征变换</string>
</property>
<layout class="QGridLayout" name="gridLayout_3">
<item row="1" column="0">
<widget class="QLabel" name="label_linear_regression_ploynmial">
<property name="text">
<string>多项式变换</string>
</property>
</widget>
</item>
<item row="0" column="0">
<widget class="QLabel" name="label_linear_regression_sinusoid">
<property name="text">
<string>正弦变换</string>
</property>
</widget>
</item>
<item row="0" column="1">
<widget class="QSpinBox" name="spinBox_linear_regression_sinusoid"/>
</item>
<item row="1" column="1">
<widget class="QSpinBox" name="spinBox_linear_regression_ploynmial"/>
</item>
</layout>
</widget>
</item>
</layout>
</item> </item>
<item> <item>
<widget class="QCheckBox" name="checkBox_linear_regression_normalize"> <layout class="QHBoxLayout" name="horizontalLayout_4">
<property name="text"> <item>
<string>数据归一化</string> <widget class="QLabel" name="label_linear_regression_train_percent">
</property> <property name="text">
</widget> <string>学习数据比例</string>
</item> </property>
<item> </widget>
<widget class="QPushButton" name="pushButton_linear_regression_preview"> </item>
<property name="text"> <item>
<string>开始预处理</string> <widget class="QDoubleSpinBox" name="doubleSpinBox_linear_regression_train_percent">
</property> <property name="sizePolicy">
</widget> <sizepolicy hsizetype="Minimum" vsizetype="Fixed">
<horstretch>0</horstretch>
<verstretch>0</verstretch>
</sizepolicy>
</property>
<property name="minimum">
<double>0.100000000000000</double>
</property>
<property name="maximum">
<double>1.000000000000000</double>
</property>
<property name="singleStep">
<double>0.100000000000000</double>
</property>
<property name="value">
<double>0.800000000000000</double>
</property>
</widget>
</item>
<item>
<widget class="QPushButton" name="pushButton_linear_regression_begin">
<property name="text">
<string>开始拟合</string>
</property>
</widget>
</item>
<item>
<widget class="QProgressBar" name="progressBar_linear_regression">
<property name="value">
<number>24</number>
</property>
</widget>
</item>
</layout>
</item> </item>
</layout> </layout>
</item> </item>
<item> <item>
<widget class="QProgressBar" name="progressBar_linear_regression"> <widget class="QGroupBox" name="groupBox_4">
<property name="value"> <property name="title">
<number>24</number> <string>结果处理</string>
</property> </property>
<layout class="QVBoxLayout" name="verticalLayout_3">
<item>
<widget class="QPushButton" name="pushButton_linear_regression_preview">
<property name="text">
<string>开始预测</string>
</property>
</widget>
</item>
<item>
<widget class="QPushButton" name="pushButton_linear_regression_show">
<property name="text">
<string>显示结果</string>
</property>
</widget>
</item>
<item>
<widget class="QPushButton" name="pushButton_linear_regression_save">
<property name="text">
<string>保存数据</string>
</property>
</widget>
</item>
</layout>
</widget> </widget>
</item> </item>
<item>
<layout class="QGridLayout" name="gridLayout">
<item row="0" column="0">
<widget class="QLabel" name="label_linear_regression_original">
<property name="text">
<string>原始数据</string>
</property>
</widget>
</item>
<item row="0" column="1">
<widget class="QLineEdit" name="lineEdit"/>
</item>
<item row="0" column="2">
<widget class="QLabel" name="label_linear_regression_original_col">
<property name="text">
<string>列</string>
</property>
</widget>
</item>
<item row="1" column="0">
<widget class="QLabel" name="label_linear_regression_target">
<property name="text">
<string>目标数据</string>
</property>
</widget>
</item>
<item row="1" column="1">
<widget class="QLineEdit" name="lineEdit_linear_regression_target"/>
</item>
<item row="1" column="2">
<widget class="QLabel" name="label_linear_regression_target_col">
<property name="text">
<string>列</string>
</property>
</widget>
</item>
</layout>
</item>
<item>
<layout class="QHBoxLayout" name="horizontalLayout_4">
<item>
<widget class="QPushButton" name="pushButton_linear_regression_begin">
<property name="text">
<string>开始拟合</string>
</property>
</widget>
</item>
<item>
<widget class="QPushButton" name="pushButton_linear_regression_save">
<property name="text">
<string>保存数据</string>
</property>
</widget>
</item>
<item>
<widget class="QPushButton" name="pushButton_linear_regression_show">
<property name="text">
<string>显示结果</string>
</property>
</widget>
</item>
</layout>
</item>
</layout> </layout>
</item> </item>
</layout> </layout>
@ -201,7 +301,7 @@
<x>0</x> <x>0</x>
<y>0</y> <y>0</y>
<width>800</width> <width>800</width>
<height>23</height> <height>24</height>
</rect> </rect>
</property> </property>
<widget class="QMenu" name="menu_file"> <widget class="QMenu" name="menu_file">

104
MainWindows.py
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@ -1,11 +1,12 @@
import Ui_MainWindow import Ui_MainWindow
from PyQt5.QtCore import QFile, QFileInfo, pyqtSlot, pyqtSignal from PyQt5.QtCore import QFile, QFileInfo, pyqtSlot, pyqtSignal
from PyQt5 import QtGui, QtWidgets, QtCore from PyQt5 import QtGui, QtWidgets, QtCore
from PyQt5.QtWidgets import QPushButton, QTableWidgetItem from PyQt5.QtWidgets import QPushButton, QTableWidgetItem, QInputDialog, QLabel
from PyQt5.QtGui import QIcon, QPixmap from PyQt5.QtGui import QIcon, QPixmap
from data_set_read import Data_Read from data_set_read import Data_Read
import pandas as pd import pandas as pd
import openpyxl as pyx import openpyxl as pyx
from libdataanalysis.LinearRegression.linear_regression import LinearRegression
class MainWindows: class MainWindows:
def __init__(self) -> None: def __init__(self) -> None:
@ -20,16 +21,22 @@ class MainWindows:
self.MainWindow.setWindowTitle("Data Analysis By Python") self.MainWindow.setWindowTitle("Data Analysis By Python")
self.MainWindow.setWindowIcon(QIcon(QPixmap(":/src/image/icon.jpg"))) self.MainWindow.setWindowIcon(QIcon(QPixmap(":/src/image/icon.jpg")))
self.ui.progressBar_linear_regression.setValue(0) self.ui.progressBar_linear_regression.setValue(0)
self.statusLable = QLabel("准备完成！")
self.ui.statusbar.addWidget(self.statusLable, 1)
def file_object_init(self): def file_object_init(self):
self.ui.pushButton_filepath.clicked.connect(self.pushButton_filepath_clicked) self.ui.pushButton_filepath.clicked.connect(self.pushButton_filepath_clicked)
self.ui.pushButton_filepath_clear.clicked.connect(self.pushButton_filepath_clear_clicked) self.ui.pushButton_filepath_clear.clicked.connect(self.pushButton_filepath_clear_clicked)
self.ui.pushButton_linear_regression.clicked.connect(lambda checked: self.ui.stackedWidget.setCurrentIndex(0)) self.ui.pushButton_linear_regression.clicked.connect(lambda checked: self.ui.stackedWidget.setCurrentIndex(0))
self.ui.pushButton_Kmeans.clicked.connect(lambda checked: self.ui.stackedWidget.setCurrentIndex(1)) self.ui.pushButton_Kmeans.clicked.connect(lambda checked: self.ui.stackedWidget.setCurrentIndex(1))
self.ui.actionNumber_Head.triggered.connect(self.action_Number_Head_triggered)
self.ui.pushButton_linear_regression_begin.clicked.connect(self.pushButton_linear_regression_begin_clicked)
def pushButton_filepath_clicked(self): def pushButton_filepath_clicked(self):
file_path = QtWidgets.QFileDialog.getOpenFileName(self.MainWindow, "Open File", ".", "All Files(*);;csv Files(*.csv);;Old Excel Files(*.xls);;New Excel Files(*.xlsx)") file_path = QtWidgets.QFileDialog.getOpenFileName(self.MainWindow, "Open File", ".", "All Files(*);;csv Files(*.csv);;Old Excel Files(*.xls);;New Excel Files(*.xlsx)")
print(file_path[0]) if file_path[0] is None:
return
self.file_path = file_path[0]
info = QFileInfo(file_path[0]) info = QFileInfo(file_path[0])
suffix = info.suffix() suffix = info.suffix()
if suffix == "csv": if suffix == "csv":
@ -41,29 +48,88 @@ class MainWindows:
if self.ui.stackedWidget.currentIndex() == 0: if self.ui.stackedWidget.currentIndex() == 0:
self.tableWidget_linear_regression_show() self.tableWidget_linear_regression_show()
#data_show = self.data_read.get_head_data(self.head_num)
# self.tableWidget_linear_regression_show(data_show)
# if file_path is not None:
# self.ui.lineEdit_filepath.setText(file_path)
def tableWidget_linear_regression_show(self): def tableWidget_linear_regression_show(self):
# self.ui.tableWidget_linear_regression.clear()
# # self.ui.tableWidget_linear_regression.setRowCount(data_frame.shape[0])
# # self.ui.tableWidget_linear_regression.setColumnCount(data_frame.shape[1])
# # for row_index, row in data_frame.iterrows():
# # for col_index, data in enumerate(row):
# # item = QTableWidgetItem(str(data))
# # self.ui.tableWidget_linear_regression.setItem(row_index, col_index, item)
self.ui.tableWidget_linear_regression.clear() self.ui.tableWidget_linear_regression.clear()
workbook = pyx.load_workbook(self.ui.lineEdit_filepath.text()) data_file = QFileInfo(self.ui.lineEdit_filepath.text())
sheet = workbook.worksheets[0] suffix = data_file.suffix()
if suffix == "csv":
self.set_csv_headers(data_file.absoluteFilePath())
elif suffix == "xlsx" or suffix == "xls":
self.set_excel_headers(data_file.absoluteFilePath())
def set_excel_headers(self, file_path):
wb = pyx.load_workbook(file_path)
sheet = wb.active
self.ui.tableWidget_linear_regression.setRowCount(self.head_num) self.ui.tableWidget_linear_regression.setRowCount(self.head_num)
self.ui.tableWidget_linear_regression.setColumnCount(sheet.max_column) self.ui.tableWidget_linear_regression.setColumnCount(sheet.max_column)
for row in sheet.iter_rows(min_row=1, max_row=self.head_num, max_col=sheet.max_column, values_only=True): row_count = 0
print(row) if sheet.max_row < self.head_num:
max_row = sheet.max_row
else:
max_row = self.head_num
for row in sheet.iter_rows(min_row=1, max_row=max_row, max_col=sheet.max_column, values_only=True):
for col, data in enumerate(row, start=0): for col, data in enumerate(row, start=0):
item = QTableWidgetItem(str(data) if data is not None else "") item = QTableWidgetItem(str(data))
self.ui.tableWidget_linear_regression.setItem(row[0] - 1, col, item) # 注意行索引从0开始，但Excel从1开始 self.ui.tableWidget_linear_regression.setItem(row_count, col, item)
row_count += 1
wb.close()
self.statusLable.setText("加载完成！")
def set_csv_headers(self, file_path):
with open(file_path, "r") as file:
for i in range(0, self.head_num):
if i == 0:
line = file.readline()
line_stripped = line.rstrip()
data_list = line_stripped.split(",")
self.ui.tableWidget_linear_regression.setRowCount(self.head_num)
self.ui.tableWidget_linear_regression.setColumnCount(len(data_list))
for j in range(0, len(data_list)):
item = QTableWidgetItem(str(data_list[j]))
self.ui.tableWidget_linear_regression.setItem(i, j, item)
else:
line = file.readline()
line_stripped = line.rstrip()
data_list = line_stripped.split(",")
for j in range(0, len(data_list)):
item = QTableWidgetItem(str(data_list[j]))
self.ui.tableWidget_linear_regression.setItem(i, j, item)
file.close()
self.statusLable.setText("加载完成！")
def pushButton_filepath_clear_clicked(self): def pushButton_filepath_clear_clicked(self):
self.ui.lineEdit_filepath.clear() self.ui.lineEdit_filepath.clear()
def action_Number_Head_triggered(self):
nb_head = QtWidgets.QInputDialog.getInt(self.MainWindow, "Head Number", "设置头行数", 10, 1, 20, 1)
if nb_head[1] == True:
self.head_num = nb_head[0]
def pushButton_linear_regression_begin_clicked(self):
if self.ui.lineEdit_linear_regression_original.text is None or self.ui.lineEdit_linear_regression_target is None:
self.ui.statusbar.s
info = QFileInfo(self.file_path)
suffix = info.suffix()
if suffix == "csv":
data = pd.read_csv(self.file_path)
elif suffix == "xls" or suffix == "xlsx":
data = pd.read_excel(self.file_path)
x = data[self.ui.lineEdit_linear_regression_original.text().split(",")].values
y = data[self.ui.lineEdit_linear_regression_target.text()].values
num_iterations = self.ui.spinBox_linear_regression_iter.value()
learning_rate = self.ui.doubleSpinBox_linear_regression_rate.value()
polynomial_degress = self.ui.spinBox_linear_regression_ploynmial.value()
sinusoid_degress = self.ui.spinBox_linear_regression_sinusoid.value()
normalize_data = True
linear_regression = LinearRegression(x, y, polynomial_degress, sinusoid_degress, normalize_data)
(theta, cost_history) = linear_regression.train(
learning_rate,
num_iterations
)
msg = "开始损失: {:.2f},结束损失: {:.2f}".format(cost_history[0], cost_history[-1])
self.statusLable.setText(msg)

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data_show.ipynb → Test/data_show.ipynb
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@ -2,75 +2,35 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1,
"id": "initial_id", "id": "initial_id",
"metadata": { "metadata": {
"collapsed": true,
"ExecuteTime": { "ExecuteTime": {
"end_time": "2024-06-08T09:52:47.287637Z", "end_time": "2024-06-08T09:52:47.287637Z",
"start_time": "2024-06-08T09:52:46.348111Z" "start_time": "2024-06-08T09:52:46.348111Z"
} },
"collapsed": true
}, },
"outputs": [],
"source": [ "source": [
"import numpy as np\n", "import numpy as np\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import pandas as pd" "import pandas as pd"
], ]
"outputs": [],
"execution_count": 1
}, },
{ {
"cell_type": "code",
"execution_count": 2,
"id": "613252be66c5c97d",
"metadata": { "metadata": {
"ExecuteTime": { "ExecuteTime": {
"end_time": "2024-06-08T09:53:28.061215Z", "end_time": "2024-06-08T09:53:28.061215Z",
"start_time": "2024-06-08T09:53:28.039931Z" "start_time": "2024-06-08T09:53:28.039931Z"
} }
}, },
"cell_type": "code",
"source": [
"df = pd.read_csv(\"./data/world-happiness-report-2017.csv\")\n",
"df.head(10)"
],
"id": "613252be66c5c97d",
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [
" Country Happiness.Rank Happiness.Score Whisker.high Whisker.low \\\n",
"0 Norway 1 7.537 7.594445 7.479556 \n",
"1 Denmark 2 7.522 7.581728 7.462272 \n",
"2 Iceland 3 7.504 7.622030 7.385970 \n",
"3 Switzerland 4 7.494 7.561772 7.426227 \n",
"4 Finland 5 7.469 7.527542 7.410458 \n",
"5 Netherlands 6 7.377 7.427426 7.326574 \n",
"6 Canada 7 7.316 7.384403 7.247597 \n",
"7 New Zealand 8 7.314 7.379510 7.248490 \n",
"8 Sweden 9 7.284 7.344095 7.223905 \n",
"9 Australia 10 7.284 7.356651 7.211349 \n",
"\n",
" Economy..GDP.per.Capita. Family Health..Life.Expectancy. Freedom \\\n",
"0 1.616463 1.533524 0.796667 0.635423 \n",
"1 1.482383 1.551122 0.792566 0.626007 \n",
"2 1.480633 1.610574 0.833552 0.627163 \n",
"3 1.564980 1.516912 0.858131 0.620071 \n",
"4 1.443572 1.540247 0.809158 0.617951 \n",
"5 1.503945 1.428939 0.810696 0.585384 \n",
"6 1.479204 1.481349 0.834558 0.611101 \n",
"7 1.405706 1.548195 0.816760 0.614062 \n",
"8 1.494387 1.478162 0.830875 0.612924 \n",
"9 1.484415 1.510042 0.843887 0.601607 \n",
"\n",
" Generosity Trust..Government.Corruption. Dystopia.Residual \n",
"0 0.362012 0.315964 2.277027 \n",
"1 0.355280 0.400770 2.313707 \n",
"2 0.475540 0.153527 2.322715 \n",
"3 0.290549 0.367007 2.276716 \n",
"4 0.245483 0.382612 2.430182 \n",
"5 0.470490 0.282662 2.294804 \n",
"6 0.435540 0.287372 2.187264 \n",
"7 0.500005 0.382817 2.046456 \n",
"8 0.385399 0.384399 2.097538 \n",
"9 0.477699 0.301184 2.065211 "
],
"text/html": [ "text/html": [
"<div>\n", "<div>\n",
"<style scoped>\n", "<style scoped>\n",
@ -258,6 +218,43 @@
" </tbody>\n", " </tbody>\n",
"</table>\n", "</table>\n",
"</div>" "</div>"
],
"text/plain": [
" Country Happiness.Rank Happiness.Score Whisker.high Whisker.low \\\n",
"0 Norway 1 7.537 7.594445 7.479556 \n",
"1 Denmark 2 7.522 7.581728 7.462272 \n",
"2 Iceland 3 7.504 7.622030 7.385970 \n",
"3 Switzerland 4 7.494 7.561772 7.426227 \n",
"4 Finland 5 7.469 7.527542 7.410458 \n",
"5 Netherlands 6 7.377 7.427426 7.326574 \n",
"6 Canada 7 7.316 7.384403 7.247597 \n",
"7 New Zealand 8 7.314 7.379510 7.248490 \n",
"8 Sweden 9 7.284 7.344095 7.223905 \n",
"9 Australia 10 7.284 7.356651 7.211349 \n",
"\n",
" Economy..GDP.per.Capita. Family Health..Life.Expectancy. Freedom \\\n",
"0 1.616463 1.533524 0.796667 0.635423 \n",
"1 1.482383 1.551122 0.792566 0.626007 \n",
"2 1.480633 1.610574 0.833552 0.627163 \n",
"3 1.564980 1.516912 0.858131 0.620071 \n",
"4 1.443572 1.540247 0.809158 0.617951 \n",
"5 1.503945 1.428939 0.810696 0.585384 \n",
"6 1.479204 1.481349 0.834558 0.611101 \n",
"7 1.405706 1.548195 0.816760 0.614062 \n",
"8 1.494387 1.478162 0.830875 0.612924 \n",
"9 1.484415 1.510042 0.843887 0.601607 \n",
"\n",
" Generosity Trust..Government.Corruption. Dystopia.Residual \n",
"0 0.362012 0.315964 2.277027 \n",
"1 0.355280 0.400770 2.313707 \n",
"2 0.475540 0.153527 2.322715 \n",
"3 0.290549 0.367007 2.276716 \n",
"4 0.245483 0.382612 2.430182 \n",
"5 0.470490 0.282662 2.294804 \n",
"6 0.435540 0.287372 2.187264 \n",
"7 0.500005 0.382817 2.046456 \n",
"8 0.385399 0.384399 2.097538 \n",
"9 0.477699 0.301184 2.065211 "
] ]
}, },
"execution_count": 2, "execution_count": 2,
@ -265,15 +262,83 @@
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"execution_count": 2 "source": [
"df = pd.read_csv(\"./data/world-happiness-report-2017.csv\")\n",
"df.head(10)"
]
}, },
{ {
"metadata": {},
"cell_type": "code", "cell_type": "code",
"execution_count": 4,
"id": "3065eaa0832b900b",
"metadata": {},
"outputs": [], "outputs": [],
"execution_count": null, "source": [
"source": "", "import openpyxl as pyx\n",
"id": "3065eaa0832b900b" "wb = pyx.load_workbook(\"./data.xlsx\")\n",
"sheet = wb.active"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "b80ffab6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(<Cell 'Sheet1'.A1>, <Cell 'Sheet1'.B1>, <Cell 'Sheet1'.C1>, <Cell 'Sheet1'.D1>, <Cell 'Sheet1'.E1>, <Cell 'Sheet1'.F1>, <Cell 'Sheet1'.G1>, <Cell 'Sheet1'.H1>)\n"
]
}
],
"source": [
"rows = sheet.rows\n",
"for index in rows:\n",
" print(index)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "693e6a84",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'class']\n"
]
}
],
"source": [
"with open(\"../data/iris.csv\") as file:\n",
" first_line = file.readline()\n",
"first_line = first_line.rstrip()\n",
"title_list = first_line.split(\",\")\n",
"print(title_list)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "6de1fc87",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['1111']\n"
]
}
],
"source": [
"str = \"1111\"\n",
"print(str.split(\",\"))"
]
} }
], ],
"metadata": { "metadata": {
@ -285,14 +350,14 @@
"language_info": { "language_info": {
"codemirror_mode": { "codemirror_mode": {
"name": "ipython", "name": "ipython",
"version": 2 "version": 3
}, },
"file_extension": ".py", "file_extension": ".py",
"mimetype": "text/x-python", "mimetype": "text/x-python",
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython2", "pygments_lexer": "ipython3",
"version": "2.7.6" "version": "3.11.4"
} }
}, },
"nbformat": 4, "nbformat": 4,

176
Ui_MainWindow.py
View File

@ -1,6 +1,6 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'MainWindow.ui' # Form implementation generated from reading ui file '/Users/lennlouis/lenn_ws/python_ws/pyqt_data_analysis/MainWindow.ui'
# #
# Created by: PyQt5 UI code generator 5.15.9 # Created by: PyQt5 UI code generator 5.15.9
# #
@ -43,77 +43,133 @@ class Ui_MainWindow(object):
self.pushButton_Kmeans = QtWidgets.QPushButton(self.widget) self.pushButton_Kmeans = QtWidgets.QPushButton(self.widget)
self.pushButton_Kmeans.setObjectName("pushButton_Kmeans") self.pushButton_Kmeans.setObjectName("pushButton_Kmeans")
self.horizontalLayout_2.addWidget(self.pushButton_Kmeans) self.horizontalLayout_2.addWidget(self.pushButton_Kmeans)
self.pushButton_4 = QtWidgets.QPushButton(self.widget)
self.pushButton_4.setObjectName("pushButton_4")
self.horizontalLayout_2.addWidget(self.pushButton_4)
self.pushButton_5 = QtWidgets.QPushButton(self.widget)
self.pushButton_5.setObjectName("pushButton_5")
self.horizontalLayout_2.addWidget(self.pushButton_5)
self.verticalLayout.addLayout(self.horizontalLayout_2) self.verticalLayout.addLayout(self.horizontalLayout_2)
self.stackedWidget = QtWidgets.QStackedWidget(self.widget) self.stackedWidget = QtWidgets.QStackedWidget(self.widget)
self.stackedWidget.setObjectName("stackedWidget") self.stackedWidget.setObjectName("stackedWidget")
self.page = QtWidgets.QWidget() self.page = QtWidgets.QWidget()
self.page.setObjectName("page") self.page.setObjectName("page")
self.verticalLayout_4 = QtWidgets.QVBoxLayout(self.page) self.verticalLayout_5 = QtWidgets.QVBoxLayout(self.page)
self.verticalLayout_4.setObjectName("verticalLayout_4") self.verticalLayout_5.setObjectName("verticalLayout_5")
self.tableWidget_linear_regression = QtWidgets.QTableWidget(self.page) self.tableWidget_linear_regression = QtWidgets.QTableWidget(self.page)
self.tableWidget_linear_regression.setObjectName("tableWidget_linear_regression") self.tableWidget_linear_regression.setObjectName("tableWidget_linear_regression")
self.tableWidget_linear_regression.setColumnCount(0) self.tableWidget_linear_regression.setColumnCount(0)
self.tableWidget_linear_regression.setRowCount(0) self.tableWidget_linear_regression.setRowCount(0)
self.verticalLayout_4.addWidget(self.tableWidget_linear_regression) self.verticalLayout_5.addWidget(self.tableWidget_linear_regression)
self.verticalLayout_3 = QtWidgets.QVBoxLayout() self.horizontalLayout_5 = QtWidgets.QHBoxLayout()
self.verticalLayout_3.setObjectName("verticalLayout_3") self.horizontalLayout_5.setObjectName("horizontalLayout_5")
self.verticalLayout_4 = QtWidgets.QVBoxLayout()
self.verticalLayout_4.setObjectName("verticalLayout_4")
self.horizontalLayout_3 = QtWidgets.QHBoxLayout() self.horizontalLayout_3 = QtWidgets.QHBoxLayout()
self.horizontalLayout_3.setSpacing(0)
self.horizontalLayout_3.setObjectName("horizontalLayout_3") self.horizontalLayout_3.setObjectName("horizontalLayout_3")
self.checkBox_linear_regression_data_cleaning = QtWidgets.QCheckBox(self.page) self.groupBox = QtWidgets.QGroupBox(self.page)
self.checkBox_linear_regression_data_cleaning.setObjectName("checkBox_linear_regression_data_cleaning") self.groupBox.setObjectName("groupBox")
self.horizontalLayout_3.addWidget(self.checkBox_linear_regression_data_cleaning) self.gridLayout = QtWidgets.QGridLayout(self.groupBox)
self.checkBox_linear_regression_normalize = QtWidgets.QCheckBox(self.page)
self.checkBox_linear_regression_normalize.setObjectName("checkBox_linear_regression_normalize")
self.horizontalLayout_3.addWidget(self.checkBox_linear_regression_normalize)
self.pushButton_linear_regression_preview = QtWidgets.QPushButton(self.page)
self.pushButton_linear_regression_preview.setObjectName("pushButton_linear_regression_preview")
self.horizontalLayout_3.addWidget(self.pushButton_linear_regression_preview)
self.verticalLayout_3.addLayout(self.horizontalLayout_3)
self.progressBar_linear_regression = QtWidgets.QProgressBar(self.page)
self.progressBar_linear_regression.setProperty("value", 24)
self.progressBar_linear_regression.setObjectName("progressBar_linear_regression")
self.verticalLayout_3.addWidget(self.progressBar_linear_regression)
self.gridLayout = QtWidgets.QGridLayout()
self.gridLayout.setObjectName("gridLayout") self.gridLayout.setObjectName("gridLayout")
self.label_linear_regression_original = QtWidgets.QLabel(self.page) self.label_linear_regression_original = QtWidgets.QLabel(self.groupBox)
self.label_linear_regression_original.setObjectName("label_linear_regression_original") self.label_linear_regression_original.setObjectName("label_linear_regression_original")
self.gridLayout.addWidget(self.label_linear_regression_original, 0, 0, 1, 1) self.gridLayout.addWidget(self.label_linear_regression_original, 0, 0, 1, 1)
self.lineEdit = QtWidgets.QLineEdit(self.page) self.lineEdit_linear_regression_original = QtWidgets.QLineEdit(self.groupBox)
self.lineEdit.setObjectName("lineEdit") sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Minimum)
self.gridLayout.addWidget(self.lineEdit, 0, 1, 1, 1) sizePolicy.setHorizontalStretch(0)
self.label_linear_regression_original_col = QtWidgets.QLabel(self.page) sizePolicy.setVerticalStretch(0)
self.label_linear_regression_original_col.setObjectName("label_linear_regression_original_col") sizePolicy.setHeightForWidth(self.lineEdit_linear_regression_original.sizePolicy().hasHeightForWidth())
self.gridLayout.addWidget(self.label_linear_regression_original_col, 0, 2, 1, 1) self.lineEdit_linear_regression_original.setSizePolicy(sizePolicy)
self.label_linear_regression_target = QtWidgets.QLabel(self.page) self.lineEdit_linear_regression_original.setObjectName("lineEdit_linear_regression_original")
self.gridLayout.addWidget(self.lineEdit_linear_regression_original, 0, 1, 1, 1)
self.label_linear_regression_target = QtWidgets.QLabel(self.groupBox)
self.label_linear_regression_target.setObjectName("label_linear_regression_target") self.label_linear_regression_target.setObjectName("label_linear_regression_target")
self.gridLayout.addWidget(self.label_linear_regression_target, 1, 0, 1, 1) self.gridLayout.addWidget(self.label_linear_regression_target, 1, 0, 1, 1)
self.lineEdit_linear_regression_target = QtWidgets.QLineEdit(self.page) self.lineEdit_linear_regression_target = QtWidgets.QLineEdit(self.groupBox)
sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
sizePolicy.setHeightForWidth(self.lineEdit_linear_regression_target.sizePolicy().hasHeightForWidth())
self.lineEdit_linear_regression_target.setSizePolicy(sizePolicy)
self.lineEdit_linear_regression_target.setObjectName("lineEdit_linear_regression_target") self.lineEdit_linear_regression_target.setObjectName("lineEdit_linear_regression_target")
self.gridLayout.addWidget(self.lineEdit_linear_regression_target, 1, 1, 1, 1) self.gridLayout.addWidget(self.lineEdit_linear_regression_target, 1, 1, 1, 1)
self.label_linear_regression_target_col = QtWidgets.QLabel(self.page) self.horizontalLayout_3.addWidget(self.groupBox)
self.label_linear_regression_target_col.setObjectName("label_linear_regression_target_col") self.groupBox_2 = QtWidgets.QGroupBox(self.page)
self.gridLayout.addWidget(self.label_linear_regression_target_col, 1, 2, 1, 1) self.groupBox_2.setObjectName("groupBox_2")
self.verticalLayout_3.addLayout(self.gridLayout) self.gridLayout_2 = QtWidgets.QGridLayout(self.groupBox_2)
self.gridLayout_2.setObjectName("gridLayout_2")
self.label_linear_regression_iter = QtWidgets.QLabel(self.groupBox_2)
self.label_linear_regression_iter.setObjectName("label_linear_regression_iter")
self.gridLayout_2.addWidget(self.label_linear_regression_iter, 0, 0, 1, 1)
self.label_linear_regression_rate = QtWidgets.QLabel(self.groupBox_2)
self.label_linear_regression_rate.setObjectName("label_linear_regression_rate")
self.gridLayout_2.addWidget(self.label_linear_regression_rate, 1, 0, 1, 1)
self.spinBox_linear_regression_iter = QtWidgets.QSpinBox(self.groupBox_2)
self.spinBox_linear_regression_iter.setMinimum(1)
self.spinBox_linear_regression_iter.setMaximum(10000)
self.spinBox_linear_regression_iter.setProperty("value", 500)
self.spinBox_linear_regression_iter.setObjectName("spinBox_linear_regression_iter")
self.gridLayout_2.addWidget(self.spinBox_linear_regression_iter, 0, 1, 1, 1)
self.doubleSpinBox_linear_regression_rate = QtWidgets.QDoubleSpinBox(self.groupBox_2)
self.doubleSpinBox_linear_regression_rate.setMaximum(1.0)
self.doubleSpinBox_linear_regression_rate.setSingleStep(0.01)
self.doubleSpinBox_linear_regression_rate.setProperty("value", 0.03)
self.doubleSpinBox_linear_regression_rate.setObjectName("doubleSpinBox_linear_regression_rate")
self.gridLayout_2.addWidget(self.doubleSpinBox_linear_regression_rate, 1, 1, 1, 1)
self.horizontalLayout_3.addWidget(self.groupBox_2)
self.groupBox_3 = QtWidgets.QGroupBox(self.page)
self.groupBox_3.setObjectName("groupBox_3")
self.gridLayout_3 = QtWidgets.QGridLayout(self.groupBox_3)
self.gridLayout_3.setObjectName("gridLayout_3")
self.label_linear_regression_ploynmial = QtWidgets.QLabel(self.groupBox_3)
self.label_linear_regression_ploynmial.setObjectName("label_linear_regression_ploynmial")
self.gridLayout_3.addWidget(self.label_linear_regression_ploynmial, 1, 0, 1, 1)
self.label_linear_regression_sinusoid = QtWidgets.QLabel(self.groupBox_3)
self.label_linear_regression_sinusoid.setObjectName("label_linear_regression_sinusoid")
self.gridLayout_3.addWidget(self.label_linear_regression_sinusoid, 0, 0, 1, 1)
self.spinBox_linear_regression_sinusoid = QtWidgets.QSpinBox(self.groupBox_3)
self.spinBox_linear_regression_sinusoid.setObjectName("spinBox_linear_regression_sinusoid")
self.gridLayout_3.addWidget(self.spinBox_linear_regression_sinusoid, 0, 1, 1, 1)
self.spinBox_linear_regression_ploynmial = QtWidgets.QSpinBox(self.groupBox_3)
self.spinBox_linear_regression_ploynmial.setObjectName("spinBox_linear_regression_ploynmial")
self.gridLayout_3.addWidget(self.spinBox_linear_regression_ploynmial, 1, 1, 1, 1)
self.horizontalLayout_3.addWidget(self.groupBox_3)
self.verticalLayout_4.addLayout(self.horizontalLayout_3)
self.horizontalLayout_4 = QtWidgets.QHBoxLayout() self.horizontalLayout_4 = QtWidgets.QHBoxLayout()
self.horizontalLayout_4.setObjectName("horizontalLayout_4") self.horizontalLayout_4.setObjectName("horizontalLayout_4")
self.label_linear_regression_train_percent = QtWidgets.QLabel(self.page)
self.label_linear_regression_train_percent.setObjectName("label_linear_regression_train_percent")
self.horizontalLayout_4.addWidget(self.label_linear_regression_train_percent)
self.doubleSpinBox_linear_regression_train_percent = QtWidgets.QDoubleSpinBox(self.page)
sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Minimum, QtWidgets.QSizePolicy.Fixed)
sizePolicy.setHorizontalStretch(0)
sizePolicy.setVerticalStretch(0)
sizePolicy.setHeightForWidth(self.doubleSpinBox_linear_regression_train_percent.sizePolicy().hasHeightForWidth())
self.doubleSpinBox_linear_regression_train_percent.setSizePolicy(sizePolicy)
self.doubleSpinBox_linear_regression_train_percent.setMinimum(0.1)
self.doubleSpinBox_linear_regression_train_percent.setMaximum(1.0)
self.doubleSpinBox_linear_regression_train_percent.setSingleStep(0.1)
self.doubleSpinBox_linear_regression_train_percent.setProperty("value", 0.8)
self.doubleSpinBox_linear_regression_train_percent.setObjectName("doubleSpinBox_linear_regression_train_percent")
self.horizontalLayout_4.addWidget(self.doubleSpinBox_linear_regression_train_percent)
self.pushButton_linear_regression_begin = QtWidgets.QPushButton(self.page) self.pushButton_linear_regression_begin = QtWidgets.QPushButton(self.page)
self.pushButton_linear_regression_begin.setObjectName("pushButton_linear_regression_begin") self.pushButton_linear_regression_begin.setObjectName("pushButton_linear_regression_begin")
self.horizontalLayout_4.addWidget(self.pushButton_linear_regression_begin) self.horizontalLayout_4.addWidget(self.pushButton_linear_regression_begin)
self.pushButton_linear_regression_save = QtWidgets.QPushButton(self.page) self.progressBar_linear_regression = QtWidgets.QProgressBar(self.page)
self.pushButton_linear_regression_save.setObjectName("pushButton_linear_regression_save") self.progressBar_linear_regression.setProperty("value", 24)
self.horizontalLayout_4.addWidget(self.pushButton_linear_regression_save) self.progressBar_linear_regression.setObjectName("progressBar_linear_regression")
self.pushButton_linear_regression_show = QtWidgets.QPushButton(self.page) self.horizontalLayout_4.addWidget(self.progressBar_linear_regression)
self.verticalLayout_4.addLayout(self.horizontalLayout_4)
self.horizontalLayout_5.addLayout(self.verticalLayout_4)
self.groupBox_4 = QtWidgets.QGroupBox(self.page)
self.groupBox_4.setObjectName("groupBox_4")
self.verticalLayout_3 = QtWidgets.QVBoxLayout(self.groupBox_4)
self.verticalLayout_3.setObjectName("verticalLayout_3")
self.pushButton_linear_regression_preview = QtWidgets.QPushButton(self.groupBox_4)
self.pushButton_linear_regression_preview.setObjectName("pushButton_linear_regression_preview")
self.verticalLayout_3.addWidget(self.pushButton_linear_regression_preview)
self.pushButton_linear_regression_show = QtWidgets.QPushButton(self.groupBox_4)
self.pushButton_linear_regression_show.setObjectName("pushButton_linear_regression_show") self.pushButton_linear_regression_show.setObjectName("pushButton_linear_regression_show")
self.horizontalLayout_4.addWidget(self.pushButton_linear_regression_show) self.verticalLayout_3.addWidget(self.pushButton_linear_regression_show)
self.verticalLayout_3.addLayout(self.horizontalLayout_4) self.pushButton_linear_regression_save = QtWidgets.QPushButton(self.groupBox_4)
self.verticalLayout_4.addLayout(self.verticalLayout_3) self.pushButton_linear_regression_save.setObjectName("pushButton_linear_regression_save")
self.verticalLayout_3.addWidget(self.pushButton_linear_regression_save)
self.horizontalLayout_5.addWidget(self.groupBox_4)
self.verticalLayout_5.addLayout(self.horizontalLayout_5)
self.stackedWidget.addWidget(self.page) self.stackedWidget.addWidget(self.page)
self.page_2 = QtWidgets.QWidget() self.page_2 = QtWidgets.QWidget()
self.page_2.setObjectName("page_2") self.page_2.setObjectName("page_2")
@ -128,7 +184,7 @@ class Ui_MainWindow(object):
self.verticalLayout_2.addWidget(self.widget) self.verticalLayout_2.addWidget(self.widget)
MainWindow.setCentralWidget(self.centralwidget) MainWindow.setCentralWidget(self.centralwidget)
self.menubar = QtWidgets.QMenuBar(MainWindow) self.menubar = QtWidgets.QMenuBar(MainWindow)
self.menubar.setGeometry(QtCore.QRect(0, 0, 800, 23)) self.menubar.setGeometry(QtCore.QRect(0, 0, 800, 24))
self.menubar.setObjectName("menubar") self.menubar.setObjectName("menubar")
self.menu_file = QtWidgets.QMenu(self.menubar) self.menu_file = QtWidgets.QMenu(self.menubar)
self.menu_file.setObjectName("menu_file") self.menu_file.setObjectName("menu_file")
@ -173,18 +229,23 @@ class Ui_MainWindow(object):
self.pushButton_filepath_clear.setText(_translate("MainWindow", "清除")) self.pushButton_filepath_clear.setText(_translate("MainWindow", "清除"))
self.pushButton_linear_regression.setText(_translate("MainWindow", "线性回归")) self.pushButton_linear_regression.setText(_translate("MainWindow", "线性回归"))
self.pushButton_Kmeans.setText(_translate("MainWindow", "聚类算法")) self.pushButton_Kmeans.setText(_translate("MainWindow", "聚类算法"))
self.pushButton_4.setText(_translate("MainWindow", "PushButton")) self.groupBox.setTitle(_translate("MainWindow", "标签"))
self.pushButton_5.setText(_translate("MainWindow", "PushButton"))
self.checkBox_linear_regression_data_cleaning.setText(_translate("MainWindow", "数据清洗"))
self.checkBox_linear_regression_normalize.setText(_translate("MainWindow", "数据归一化"))
self.pushButton_linear_regression_preview.setText(_translate("MainWindow", "开始预处理"))
self.label_linear_regression_original.setText(_translate("MainWindow", "原始数据")) self.label_linear_regression_original.setText(_translate("MainWindow", "原始数据"))
self.label_linear_regression_original_col.setText(_translate("MainWindow", "列")) self.lineEdit_linear_regression_original.setPlaceholderText(_translate("MainWindow", "原始数据Title"))
self.label_linear_regression_target.setText(_translate("MainWindow", "目标数据")) self.label_linear_regression_target.setText(_translate("MainWindow", "目标数据"))
self.label_linear_regression_target_col.setText(_translate("MainWindow", "列")) self.lineEdit_linear_regression_target.setPlaceholderText(_translate("MainWindow", "目标数据Title"))
self.groupBox_2.setTitle(_translate("MainWindow", "学习参数"))
self.label_linear_regression_iter.setText(_translate("MainWindow", "迭代次数"))
self.label_linear_regression_rate.setText(_translate("MainWindow", "学习率"))
self.groupBox_3.setTitle(_translate("MainWindow", "特征变换"))
self.label_linear_regression_ploynmial.setText(_translate("MainWindow", "多项式变换"))
self.label_linear_regression_sinusoid.setText(_translate("MainWindow", "正弦变换"))
self.label_linear_regression_train_percent.setText(_translate("MainWindow", "学习数据比例"))
self.pushButton_linear_regression_begin.setText(_translate("MainWindow", "开始拟合")) self.pushButton_linear_regression_begin.setText(_translate("MainWindow", "开始拟合"))
self.pushButton_linear_regression_save.setText(_translate("MainWindow", "保存数据")) self.groupBox_4.setTitle(_translate("MainWindow", "结果处理"))
self.pushButton_linear_regression_preview.setText(_translate("MainWindow", "开始预测"))
self.pushButton_linear_regression_show.setText(_translate("MainWindow", "显示结果")) self.pushButton_linear_regression_show.setText(_translate("MainWindow", "显示结果"))
self.pushButton_linear_regression_save.setText(_translate("MainWindow", "保存数据"))
self.menu_file.setTitle(_translate("MainWindow", "文件")) self.menu_file.setTitle(_translate("MainWindow", "文件"))
self.menu_about.setTitle(_translate("MainWindow", "关于")) self.menu_about.setTitle(_translate("MainWindow", "关于"))
self.menu.setTitle(_translate("MainWindow", "设置")) self.menu.setTitle(_translate("MainWindow", "设置"))
@ -194,4 +255,3 @@ class Ui_MainWindow(object):
self.actionAbout.setText(_translate("MainWindow", "About")) self.actionAbout.setText(_translate("MainWindow", "About"))
self.actionExit.setText(_translate("MainWindow", "Exit")) self.actionExit.setText(_translate("MainWindow", "Exit"))
self.actionNumber_Head.setText(_translate("MainWindow", "Number Head")) self.actionNumber_Head.setText(_translate("MainWindow", "Number Head"))

93
data_analysis/linear_regression.py
View File

@ -1,93 +0,0 @@
import numpy as np
from utils.features import prepare_for_training
class LinearRegression:
def __init__(self,data,labels,polynomial_degree = 0,sinusoid_degree = 0,normalize_data=True):
"""
1.对数据进行预处理操作
2.先得到所有的特征个数
3.初始化参数矩阵
"""
(data_processed,
features_mean,
features_deviation) = prepare_for_training(data, polynomial_degree, sinusoid_degree,normalize_data=True)
self.data = data_processed
self.labels = labels
self.features_mean = features_mean
self.features_deviation = features_deviation
self.polynomial_degree = polynomial_degree
self.sinusoid_degree = sinusoid_degree
self.normalize_data = normalize_data
num_features = self.data.shape[1]
self.theta = np.zeros((num_features,1))
def train(self,alpha,num_iterations = 500):
"""
训练模块，执行梯度下降
"""
cost_history = self.gradient_descent(alpha,num_iterations)
return self.theta,cost_history
def gradient_descent(self,alpha,num_iterations):
"""
实际迭代模块，会迭代num_iterations次
"""
cost_history = []
for _ in range(num_iterations):
self.gradient_step(alpha)
cost_history.append(self.cost_function(self.data,self.labels))
return cost_history
def gradient_step(self,alpha):
"""
梯度下降参数更新计算方法，注意是矩阵运算
"""
num_examples = self.data.shape[0]
prediction = LinearRegression.hypothesis(self.data,self.theta)
delta = prediction - self.labels
theta = self.theta
theta = theta - alpha*(1/num_examples)*(np.dot(delta.T,self.data)).T
self.theta = theta
def cost_function(self,data,labels):
"""
损失计算方法
"""
num_examples = data.shape[0]
delta = LinearRegression.hypothesis(self.data,self.theta) - labels
cost = (1/2)*np.dot(delta.T,delta)/num_examples
return cost[0][0]
@staticmethod
def hypothesis(data,theta):
predictions = np.dot(data,theta)
return predictions
def get_cost(self,data,labels):
data_processed = prepare_for_training(data,
self.polynomial_degree,
self.sinusoid_degree,
self.normalize_data
)[0]
return self.cost_function(data_processed,labels)
def predict(self,data):
"""
用训练的参数模型，与预测得到回归值结果
"""
data_processed = prepare_for_training(data,
self.polynomial_degree,
self.sinusoid_degree,
self.normalize_data
)[0]
predictions = LinearRegression.hypothesis(data_processed,self.theta)
return predictions

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@ -1,151 +0,0 @@
sepal_length,sepal_width,petal_length,petal_width,class
5.1,3.5,1.4,0.2,SETOSA
4.9,3.0,1.4,0.2,SETOSA
4.7,3.2,1.3,0.2,SETOSA
4.6,3.1,1.5,0.2,SETOSA
5.0,3.6,1.4,0.2,SETOSA
5.4,3.9,1.7,0.4,SETOSA
4.6,3.4,1.4,0.3,SETOSA
5.0,3.4,1.5,0.2,SETOSA
4.4,2.9,1.4,0.2,SETOSA
4.9,3.1,1.5,0.1,SETOSA
5.4,3.7,1.5,0.2,SETOSA
4.8,3.4,1.6,0.2,SETOSA
4.8,3.0,1.4,0.1,SETOSA
4.3,3.0,1.1,0.1,SETOSA
5.8,4.0,1.2,0.2,SETOSA
5.7,4.4,1.5,0.4,SETOSA
5.4,3.9,1.3,0.4,SETOSA
5.1,3.5,1.4,0.3,SETOSA
5.7,3.8,1.7,0.3,SETOSA
5.1,3.8,1.5,0.3,SETOSA
5.4,3.4,1.7,0.2,SETOSA
5.1,3.7,1.5,0.4,SETOSA
4.6,3.6,1.0,0.2,SETOSA
5.1,3.3,1.7,0.5,SETOSA
4.8,3.4,1.9,0.2,SETOSA
5.0,3.0,1.6,0.2,SETOSA
5.0,3.4,1.6,0.4,SETOSA
5.2,3.5,1.5,0.2,SETOSA
5.2,3.4,1.4,0.2,SETOSA
4.7,3.2,1.6,0.2,SETOSA
4.8,3.1,1.6,0.2,SETOSA
5.4,3.4,1.5,0.4,SETOSA
5.2,4.1,1.5,0.1,SETOSA
5.5,4.2,1.4,0.2,SETOSA
4.9,3.1,1.5,0.1,SETOSA
5.0,3.2,1.2,0.2,SETOSA
5.5,3.5,1.3,0.2,SETOSA
4.9,3.1,1.5,0.1,SETOSA
4.4,3.0,1.3,0.2,SETOSA
5.1,3.4,1.5,0.2,SETOSA
5.0,3.5,1.3,0.3,SETOSA
4.5,2.3,1.3,0.3,SETOSA
4.4,3.2,1.3,0.2,SETOSA
5.0,3.5,1.6,0.6,SETOSA
5.1,3.8,1.9,0.4,SETOSA
4.8,3.0,1.4,0.3,SETOSA
5.1,3.8,1.6,0.2,SETOSA
4.6,3.2,1.4,0.2,SETOSA
5.3,3.7,1.5,0.2,SETOSA
5.0,3.3,1.4,0.2,SETOSA
7.0,3.2,4.7,1.4,VERSICOLOR
6.4,3.2,4.5,1.5,VERSICOLOR
6.9,3.1,4.9,1.5,VERSICOLOR
5.5,2.3,4.0,1.3,VERSICOLOR
6.5,2.8,4.6,1.5,VERSICOLOR
5.7,2.8,4.5,1.3,VERSICOLOR
6.3,3.3,4.7,1.6,VERSICOLOR
4.9,2.4,3.3,1.0,VERSICOLOR
6.6,2.9,4.6,1.3,VERSICOLOR
5.2,2.7,3.9,1.4,VERSICOLOR
5.0,2.0,3.5,1.0,VERSICOLOR
5.9,3.0,4.2,1.5,VERSICOLOR
6.0,2.2,4.0,1.0,VERSICOLOR
6.1,2.9,4.7,1.4,VERSICOLOR
5.6,2.9,3.6,1.3,VERSICOLOR
6.7,3.1,4.4,1.4,VERSICOLOR
5.6,3.0,4.5,1.5,VERSICOLOR
5.8,2.7,4.1,1.0,VERSICOLOR
6.2,2.2,4.5,1.5,VERSICOLOR
5.6,2.5,3.9,1.1,VERSICOLOR
5.9,3.2,4.8,1.8,VERSICOLOR
6.1,2.8,4.0,1.3,VERSICOLOR
6.3,2.5,4.9,1.5,VERSICOLOR
6.1,2.8,4.7,1.2,VERSICOLOR
6.4,2.9,4.3,1.3,VERSICOLOR
6.6,3.0,4.4,1.4,VERSICOLOR
6.8,2.8,4.8,1.4,VERSICOLOR
6.7,3.0,5.0,1.7,VERSICOLOR
6.0,2.9,4.5,1.5,VERSICOLOR
5.7,2.6,3.5,1.0,VERSICOLOR
5.5,2.4,3.8,1.1,VERSICOLOR
5.5,2.4,3.7,1.0,VERSICOLOR
5.8,2.7,3.9,1.2,VERSICOLOR
6.0,2.7,5.1,1.6,VERSICOLOR
5.4,3.0,4.5,1.5,VERSICOLOR
6.0,3.4,4.5,1.6,VERSICOLOR
6.7,3.1,4.7,1.5,VERSICOLOR
6.3,2.3,4.4,1.3,VERSICOLOR
5.6,3.0,4.1,1.3,VERSICOLOR
5.5,2.5,4.0,1.3,VERSICOLOR
5.5,2.6,4.4,1.2,VERSICOLOR
6.1,3.0,4.6,1.4,VERSICOLOR
5.8,2.6,4.0,1.2,VERSICOLOR
5.0,2.3,3.3,1.0,VERSICOLOR
5.6,2.7,4.2,1.3,VERSICOLOR
5.7,3.0,4.2,1.2,VERSICOLOR
5.7,2.9,4.2,1.3,VERSICOLOR
6.2,2.9,4.3,1.3,VERSICOLOR
5.1,2.5,3.0,1.1,VERSICOLOR
5.7,2.8,4.1,1.3,VERSICOLOR
6.3,3.3,6.0,2.5,VIRGINICA
5.8,2.7,5.1,1.9,VIRGINICA
7.1,3.0,5.9,2.1,VIRGINICA
6.3,2.9,5.6,1.8,VIRGINICA
6.5,3.0,5.8,2.2,VIRGINICA
7.6,3.0,6.6,2.1,VIRGINICA
4.9,2.5,4.5,1.7,VIRGINICA
7.3,2.9,6.3,1.8,VIRGINICA
6.7,2.5,5.8,1.8,VIRGINICA
7.2,3.6,6.1,2.5,VIRGINICA
6.5,3.2,5.1,2.0,VIRGINICA
6.4,2.7,5.3,1.9,VIRGINICA
6.8,3.0,5.5,2.1,VIRGINICA
5.7,2.5,5.0,2.0,VIRGINICA
5.8,2.8,5.1,2.4,VIRGINICA
6.4,3.2,5.3,2.3,VIRGINICA
6.5,3.0,5.5,1.8,VIRGINICA
7.7,3.8,6.7,2.2,VIRGINICA
7.7,2.6,6.9,2.3,VIRGINICA
6.0,2.2,5.0,1.5,VIRGINICA
6.9,3.2,5.7,2.3,VIRGINICA
5.6,2.8,4.9,2.0,VIRGINICA
7.7,2.8,6.7,2.0,VIRGINICA
6.3,2.7,4.9,1.8,VIRGINICA
6.7,3.3,5.7,2.1,VIRGINICA
7.2,3.2,6.0,1.8,VIRGINICA
6.2,2.8,4.8,1.8,VIRGINICA
6.1,3.0,4.9,1.8,VIRGINICA
6.4,2.8,5.6,2.1,VIRGINICA
7.2,3.0,5.8,1.6,VIRGINICA
7.4,2.8,6.1,1.9,VIRGINICA
7.9,3.8,6.4,2.0,VIRGINICA
6.4,2.8,5.6,2.2,VIRGINICA
6.3,2.8,5.1,1.5,VIRGINICA
6.1,2.6,5.6,1.4,VIRGINICA
7.7,3.0,6.1,2.3,VIRGINICA
6.3,3.4,5.6,2.4,VIRGINICA
6.4,3.1,5.5,1.8,VIRGINICA
6.0,3.0,4.8,1.8,VIRGINICA
6.9,3.1,5.4,2.1,VIRGINICA
6.7,3.1,5.6,2.4,VIRGINICA
6.9,3.1,5.1,2.3,VIRGINICA
5.8,2.7,5.1,1.9,VIRGINICA
6.8,3.2,5.9,2.3,VIRGINICA
6.7,3.3,5.7,2.5,VIRGINICA
6.7,3.0,5.2,2.3,VIRGINICA
6.3,2.5,5.0,1.9,VIRGINICA
6.5,3.0,5.2,2.0,VIRGINICA
6.2,3.4,5.4,2.3,VIRGINICA
5.9,3.0,5.1,1.8,VIRGINICA
1 sepal_length sepal_width petal_length petal_width class
2 5.1 3.5 1.4 0.2 SETOSA
3 4.9 3.0 1.4 0.2 SETOSA
4 4.7 3.2 1.3 0.2 SETOSA
5 4.6 3.1 1.5 0.2 SETOSA
6 5.0 3.6 1.4 0.2 SETOSA
7 5.4 3.9 1.7 0.4 SETOSA
8 4.6 3.4 1.4 0.3 SETOSA
9 5.0 3.4 1.5 0.2 SETOSA
10 4.4 2.9 1.4 0.2 SETOSA
11 4.9 3.1 1.5 0.1 SETOSA
12 5.4 3.7 1.5 0.2 SETOSA
13 4.8 3.4 1.6 0.2 SETOSA
14 4.8 3.0 1.4 0.1 SETOSA
15 4.3 3.0 1.1 0.1 SETOSA
16 5.8 4.0 1.2 0.2 SETOSA
17 5.7 4.4 1.5 0.4 SETOSA
18 5.4 3.9 1.3 0.4 SETOSA
19 5.1 3.5 1.4 0.3 SETOSA
20 5.7 3.8 1.7 0.3 SETOSA
21 5.1 3.8 1.5 0.3 SETOSA
22 5.4 3.4 1.7 0.2 SETOSA
23 5.1 3.7 1.5 0.4 SETOSA
24 4.6 3.6 1.0 0.2 SETOSA
25 5.1 3.3 1.7 0.5 SETOSA
26 4.8 3.4 1.9 0.2 SETOSA
27 5.0 3.0 1.6 0.2 SETOSA
28 5.0 3.4 1.6 0.4 SETOSA
29 5.2 3.5 1.5 0.2 SETOSA
30 5.2 3.4 1.4 0.2 SETOSA
31 4.7 3.2 1.6 0.2 SETOSA
32 4.8 3.1 1.6 0.2 SETOSA
33 5.4 3.4 1.5 0.4 SETOSA
34 5.2 4.1 1.5 0.1 SETOSA
35 5.5 4.2 1.4 0.2 SETOSA
36 4.9 3.1 1.5 0.1 SETOSA
37 5.0 3.2 1.2 0.2 SETOSA
38 5.5 3.5 1.3 0.2 SETOSA
39 4.9 3.1 1.5 0.1 SETOSA
40 4.4 3.0 1.3 0.2 SETOSA
41 5.1 3.4 1.5 0.2 SETOSA
42 5.0 3.5 1.3 0.3 SETOSA
43 4.5 2.3 1.3 0.3 SETOSA
44 4.4 3.2 1.3 0.2 SETOSA
45 5.0 3.5 1.6 0.6 SETOSA
46 5.1 3.8 1.9 0.4 SETOSA
47 4.8 3.0 1.4 0.3 SETOSA
48 5.1 3.8 1.6 0.2 SETOSA
49 4.6 3.2 1.4 0.2 SETOSA
50 5.3 3.7 1.5 0.2 SETOSA
51 5.0 3.3 1.4 0.2 SETOSA
52 7.0 3.2 4.7 1.4 VERSICOLOR
53 6.4 3.2 4.5 1.5 VERSICOLOR
54 6.9 3.1 4.9 1.5 VERSICOLOR
55 5.5 2.3 4.0 1.3 VERSICOLOR
56 6.5 2.8 4.6 1.5 VERSICOLOR
57 5.7 2.8 4.5 1.3 VERSICOLOR
58 6.3 3.3 4.7 1.6 VERSICOLOR
59 4.9 2.4 3.3 1.0 VERSICOLOR
60 6.6 2.9 4.6 1.3 VERSICOLOR
61 5.2 2.7 3.9 1.4 VERSICOLOR
62 5.0 2.0 3.5 1.0 VERSICOLOR
63 5.9 3.0 4.2 1.5 VERSICOLOR
64 6.0 2.2 4.0 1.0 VERSICOLOR
65 6.1 2.9 4.7 1.4 VERSICOLOR
66 5.6 2.9 3.6 1.3 VERSICOLOR
67 6.7 3.1 4.4 1.4 VERSICOLOR
68 5.6 3.0 4.5 1.5 VERSICOLOR
69 5.8 2.7 4.1 1.0 VERSICOLOR
70 6.2 2.2 4.5 1.5 VERSICOLOR
71 5.6 2.5 3.9 1.1 VERSICOLOR
72 5.9 3.2 4.8 1.8 VERSICOLOR
73 6.1 2.8 4.0 1.3 VERSICOLOR
74 6.3 2.5 4.9 1.5 VERSICOLOR
75 6.1 2.8 4.7 1.2 VERSICOLOR
76 6.4 2.9 4.3 1.3 VERSICOLOR
77 6.6 3.0 4.4 1.4 VERSICOLOR
78 6.8 2.8 4.8 1.4 VERSICOLOR
79 6.7 3.0 5.0 1.7 VERSICOLOR
80 6.0 2.9 4.5 1.5 VERSICOLOR
81 5.7 2.6 3.5 1.0 VERSICOLOR
82 5.5 2.4 3.8 1.1 VERSICOLOR
83 5.5 2.4 3.7 1.0 VERSICOLOR
84 5.8 2.7 3.9 1.2 VERSICOLOR
85 6.0 2.7 5.1 1.6 VERSICOLOR
86 5.4 3.0 4.5 1.5 VERSICOLOR
87 6.0 3.4 4.5 1.6 VERSICOLOR
88 6.7 3.1 4.7 1.5 VERSICOLOR
89 6.3 2.3 4.4 1.3 VERSICOLOR
90 5.6 3.0 4.1 1.3 VERSICOLOR
91 5.5 2.5 4.0 1.3 VERSICOLOR
92 5.5 2.6 4.4 1.2 VERSICOLOR
93 6.1 3.0 4.6 1.4 VERSICOLOR
94 5.8 2.6 4.0 1.2 VERSICOLOR
95 5.0 2.3 3.3 1.0 VERSICOLOR
96 5.6 2.7 4.2 1.3 VERSICOLOR
97 5.7 3.0 4.2 1.2 VERSICOLOR
98 5.7 2.9 4.2 1.3 VERSICOLOR
99 6.2 2.9 4.3 1.3 VERSICOLOR
100 5.1 2.5 3.0 1.1 VERSICOLOR
101 5.7 2.8 4.1 1.3 VERSICOLOR
102 6.3 3.3 6.0 2.5 VIRGINICA
103 5.8 2.7 5.1 1.9 VIRGINICA
104 7.1 3.0 5.9 2.1 VIRGINICA
105 6.3 2.9 5.6 1.8 VIRGINICA
106 6.5 3.0 5.8 2.2 VIRGINICA
107 7.6 3.0 6.6 2.1 VIRGINICA
108 4.9 2.5 4.5 1.7 VIRGINICA
109 7.3 2.9 6.3 1.8 VIRGINICA
110 6.7 2.5 5.8 1.8 VIRGINICA
111 7.2 3.6 6.1 2.5 VIRGINICA
112 6.5 3.2 5.1 2.0 VIRGINICA
113 6.4 2.7 5.3 1.9 VIRGINICA
114 6.8 3.0 5.5 2.1 VIRGINICA
115 5.7 2.5 5.0 2.0 VIRGINICA
116 5.8 2.8 5.1 2.4 VIRGINICA
117 6.4 3.2 5.3 2.3 VIRGINICA
118 6.5 3.0 5.5 1.8 VIRGINICA
119 7.7 3.8 6.7 2.2 VIRGINICA
120 7.7 2.6 6.9 2.3 VIRGINICA
121 6.0 2.2 5.0 1.5 VIRGINICA
122 6.9 3.2 5.7 2.3 VIRGINICA
123 5.6 2.8 4.9 2.0 VIRGINICA
124 7.7 2.8 6.7 2.0 VIRGINICA
125 6.3 2.7 4.9 1.8 VIRGINICA
126 6.7 3.3 5.7 2.1 VIRGINICA
127 7.2 3.2 6.0 1.8 VIRGINICA
128 6.2 2.8 4.8 1.8 VIRGINICA
129 6.1 3.0 4.9 1.8 VIRGINICA
130 6.4 2.8 5.6 2.1 VIRGINICA
131 7.2 3.0 5.8 1.6 VIRGINICA
132 7.4 2.8 6.1 1.9 VIRGINICA
133 7.9 3.8 6.4 2.0 VIRGINICA
134 6.4 2.8 5.6 2.2 VIRGINICA
135 6.3 2.8 5.1 1.5 VIRGINICA
136 6.1 2.6 5.6 1.4 VIRGINICA
137 7.7 3.0 6.1 2.3 VIRGINICA
138 6.3 3.4 5.6 2.4 VIRGINICA
139 6.4 3.1 5.5 1.8 VIRGINICA
140 6.0 3.0 4.8 1.8 VIRGINICA
141 6.9 3.1 5.4 2.1 VIRGINICA
142 6.7 3.1 5.6 2.4 VIRGINICA
143 6.9 3.1 5.1 2.3 VIRGINICA
144 5.8 2.7 5.1 1.9 VIRGINICA
145 6.8 3.2 5.9 2.3 VIRGINICA
146 6.7 3.3 5.7 2.5 VIRGINICA
147 6.7 3.0 5.2 2.3 VIRGINICA
148 6.3 2.5 5.0 1.9 VIRGINICA
149 6.5 3.0 5.2 2.0 VIRGINICA
150 6.2 3.4 5.4 2.3 VIRGINICA
151 5.9 3.0 5.1 1.8 VIRGINICA

119
libdataanalysis/2-线性回归代码实现/data/microchips-tests.csv
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@ -1,119 +0,0 @@
param_1,param_2,validity
0.051267,0.69956,1
-0.092742,0.68494,1
-0.21371,0.69225,1
-0.375,0.50219,1
-0.51325,0.46564,1
-0.52477,0.2098,1
-0.39804,0.034357,1
-0.30588,-0.19225,1
0.016705,-0.40424,1
0.13191,-0.51389,1
0.38537,-0.56506,1
0.52938,-0.5212,1
0.63882,-0.24342,1
0.73675,-0.18494,1
0.54666,0.48757,1
0.322,0.5826,1
0.16647,0.53874,1
-0.046659,0.81652,1
-0.17339,0.69956,1
-0.47869,0.63377,1
-0.60541,0.59722,1
-0.62846,0.33406,1
-0.59389,0.005117,1
-0.42108,-0.27266,1
-0.11578,-0.39693,1
0.20104,-0.60161,1
0.46601,-0.53582,1
0.67339,-0.53582,1
-0.13882,0.54605,1
-0.29435,0.77997,1
-0.26555,0.96272,1
-0.16187,0.8019,1
-0.17339,0.64839,1
-0.28283,0.47295,1
-0.36348,0.31213,1
-0.30012,0.027047,1
-0.23675,-0.21418,1
-0.06394,-0.18494,1
0.062788,-0.16301,1
0.22984,-0.41155,1
0.2932,-0.2288,1
0.48329,-0.18494,1
0.64459,-0.14108,1
0.46025,0.012427,1
0.6273,0.15863,1
0.57546,0.26827,1
0.72523,0.44371,1
0.22408,0.52412,1
0.44297,0.67032,1
0.322,0.69225,1
0.13767,0.57529,1
-0.0063364,0.39985,1
-0.092742,0.55336,1
-0.20795,0.35599,1
-0.20795,0.17325,1
-0.43836,0.21711,1
-0.21947,-0.016813,1
-0.13882,-0.27266,1
0.18376,0.93348,0
0.22408,0.77997,0
0.29896,0.61915,0
0.50634,0.75804,0
0.61578,0.7288,0
0.60426,0.59722,0
0.76555,0.50219,0
0.92684,0.3633,0
0.82316,0.27558,0
0.96141,0.085526,0
0.93836,0.012427,0
0.86348,-0.082602,0
0.89804,-0.20687,0
0.85196,-0.36769,0
0.82892,-0.5212,0
0.79435,-0.55775,0
0.59274,-0.7405,0
0.51786,-0.5943,0
0.46601,-0.41886,0
0.35081,-0.57968,0
0.28744,-0.76974,0
0.085829,-0.75512,0
0.14919,-0.57968,0
-0.13306,-0.4481,0
-0.40956,-0.41155,0
-0.39228,-0.25804,0
-0.74366,-0.25804,0
-0.69758,0.041667,0
-0.75518,0.2902,0
-0.69758,0.68494,0
-0.4038,0.70687,0
-0.38076,0.91886,0
-0.50749,0.90424,0
-0.54781,0.70687,0
0.10311,0.77997,0
0.057028,0.91886,0
-0.10426,0.99196,0
-0.081221,1.1089,0
0.28744,1.087,0
0.39689,0.82383,0
0.63882,0.88962,0
0.82316,0.66301,0
0.67339,0.64108,0
1.0709,0.10015,0
-0.046659,-0.57968,0
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10001
libdataanalysis/2-线性回归代码实现/data/mnist-demo.csv
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File diff suppressed because it is too large Load Diff

251
libdataanalysis/2-线性回归代码实现/data/non-linear-regression-x-y.csv
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308
libdataanalysis/2-线性回归代码实现/data/server-operational-params.csv
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14.54724604324321,15.6333944514067,0
14.15258077112493,14.53622696521789,0
14.12648161131633,15.34467591276852,0
14.26324658304056,14.98556918087115,0
14.77324331862399,15.25299473774317,0
14.20969933686442,16.14572569071713,0
13.260655152992,15.48016214411599,0
14.25273350867239,15.03134360663839,0
12.92124446791387,13.19321540142361,0
13.852431292546,13.33213110580615,0
13.96856800302965,13.19821236714215,0
13.25206981975186,15.36846390294601,0
13.70449633962696,13.21431301976872,0
14.5087472134072,15.46051652161006,0
15.69042695638351,16.48168851978138,0
12.95598191982515,12.43703005897334,0
13.59312604041728,14.84189902611636,0
15.12874638631439,17.14981222613881,0
14.26705036670259,15.67551973639503,0
15.6614505451442,14.81146451457414,0
14.33962672797097,15.49202297710026,0
14.2761765458781,14.70590693250814,0
14.86049072335336,15.59000779027686,0
14.10414479623351,15.1805045637764,0
15.98828286381979,15.62105187028486,0
13.47473582792461,15.59307141917535,0
13.77637601475249,14.99194426684731,0
12.82770875129005,15.67136906874635,0
13.67165486007913,15.11954159126301,0
15.38704283906103,15.56936935237784,0
15.54320933642332,15.51543150058866,0
13.85306094119846,15.60672436869602,0
13.62525245784644,14.45209462876985,0
15.0157784412311,14.91664093008973,0
13.83645753449745,15.24940725360926,0
14.22694438547307,14.3479843622948,0
13.23742625416296,14.61058751286003,0
13.38482919115422,14.7331933025011,0
13.87130103241151,14.97399468636979,0
12.39445846815594,14.64448216946588,0
14.32186557845068,14.52890629439163,0
15.82965092460402,15.71619455432355,0
15.80177302202355,16.01808914480403,0
14.69751200330076,14.11198748714029,0
14.70598656653535,16.46040295414171,0
13.59156859810395,14.91975097196414,0
12.29984538869378,14.77119467910275,0
13.3990474777037,16.11912910518291,0
15.13112869806696,15.90031130320181,0
15.38581197702793,15.71453967469415,0
15.45487421920634,15.4404224240544,0
13.74951530855867,15.26803135994583,0
15.69914333094722,16.05595814533895,0
14.80580490719942,14.33258926354469,0
15.17222942648117,16.70624397729834,0
11.24915511828765,15.13295896107001,0
13.88773906521638,14.48548132472444,0
15.3258701791002,16.58524064023295,0
12.97517063349011,15.1605677140184,0
14.07427780835002,17.21973519125371,0
14.1820256369139,17.83351945487566,0
12.23970014041095,14.72866833837743,0
14.82555960703615,15.94500684833057,0
13.09763368416417,16.23036500469445,0
13.85758877756093,15.03526838191721,0
15.52502523459987,16.78653607805479,0
15.31499528329094,14.56835427536349,0
14.03034873517879,15.6633618769716,0
14.42312994571211,14.94109334872472,0
13.63615118835241,14.96411634434718,0
14.53477942776931,13.35611764012331,0
14.61566223678644,14.15241034694619,0
13.08085544352481,14.0284594118694,0
14.93928677902786,14.54933745884242,0
16.0271266262212,15.70965830468461,0
14.31925037139242,15.11762658185582,0
14.86153307492049,14.28458412390706,0
14.01432032507764,16.77971266133154,0
13.40765469906171,14.60041190939531,0
13.0795973186072,14.19389917316378,0
12.68820688788819,13.81109597020173,0
14.19232756586644,15.36498178724437,0
14.86589365075524,14.47138789706538,0
13.39350297747264,14.34389892642248,0
13.58659142682796,14.39148496395445,0
13.10219289551651,14.3760326021477,0
14.54176555566262,16.37233995317341,0
14.25602703003231,15.0423494965284,0
16.18754760471493,16.36145253974863,0
13.63292362573135,13.62886893815872,0
14.65349334618363,14.97649220824924,0
12.61911799757794,16.77214314245786,0
13.03427729514449,14.25689090988086,0
10.85940051666349,14.47914434225415,0
12.93486070587027,14.60746677979927,0
13.9922676551586,14.96212808248882,0
12.57248704338531,15.1972734968139,0
15.68266703007037,16.22123922102406,0
13.2125815156299,14.3518273677709,0
13.98975002194823,14.52445650352669,0
13.4662664096024,13.65765529406475,0
13.13166385488746,15.79882584075226,0
14.35439254719252,15.02329268379058,0
13.55329410888779,13.73218768633878,0
12.98628429130503,14.80983707085099,0
14.37264883162727,14.95148191190331,0
13.58869050224715,15.19778174710474,0
12.26002251889708,15.61364103922988,0
13.66602493759934,16.44517365387813,0
14.34554567080519,15.44883765222099,0
14.60667497581217,15.77655361118647,0
14.15369523977195,16.57440586446113,0
14.04899502017924,14.39078838248393,0
14.06857464220482,14.62364257375797,0
15.88890082127304,16.33705609429303,0
13.97601419894874,15.84206442894244,0
10.88221341356124,13.46166188373757,0
13.90920312008345,14.97657577218348,0
12.36776146202978,15.14204982137499,0
15.16765639256333,15.51933856946829,0
15.3376951724287,14.23319145087297,0
13.55057689653119,15.73044061233337,0
13.57918656724497,15.47264441338775,0
14.24479089854792,15.0850911865811,0
15.33086296717245,15.71142599198902,0
15.91714892779239,15.15651432878437,0
13.85421253890297,15.32125758133508,0
14.08736591098981,14.30728373787297,0
12.63610997338858,15.65066101888946,0
14.36282756033598,13.87195409310256,0
14.50066606012271,14.61759024545319,0
13.96984547008964,16.17341605305203,0
15.13133128099397,15.28924849061305,0
15.15300231315136,14.01362830007739,0
13.31011939341444,14.39060274697614,0
14.25712172586539,14.29705004451436,0
13.71613134707139,13.52733470384027,0
15.70094057818437,15.99611428697285,0
13.38943515399727,14.36513422537798,0
14.14088666467278,13.97440554314796,0
14.84487049785213,14.01695105963744,0
12.70489590338878,14.27293037161499,0
14.95353525235777,14.73218902472499,0
14.28114117782965,14.61262377516035,0
13.06799073973982,14.83286345035982,0
13.60279699846308,12.20295198971654,0
12.68816488185228,15.81141680713469,0
13.88291727981215,14.11808370066965,0
14.016482216113,14.33509982485053,0
15.36576550135049,15.82610475260424,0
13.57764756126836,14.88045533202498,0
13.3918924208501,14.34497756139911,0
13.69362090262048,15.92189939882443,0
12.87853442397187,13.20174479842375,0
13.69916365173765,15.41800069841461,0
14.01609081001448,15.82165925226776,0
14.5899650464961,16.38090675134464,0
15.00784342040606,15.50954333819685,0
14.05950746445452,13.75788684204651,0
14.46114683681014,13.34425721343066,0
14.64474777063343,15.03905866347516,0
13.85478898285457,15.86614260965412,0
14.2814175097121,14.02340696081207,0
14.93304554162803,14.32639552072927,0
13.7693080678919,16.51310530416839,0
13.44404345182867,15.07922662749323,0
14.0317928593353,14.40986664465888,0
13.81946840229293,15.58676798397279,0
16.50656640573653,15.22029747467542,0
12.20423230665472,14.32106064914233,0
14.8819298948981,16.36162230554352,0
15.16030999546341,15.14972042192441,0
11.78759609450762,14.55034168613148,0
12.88388298331717,14.57250347912669,0
13.62023705917705,16.42369250161395,0
14.53049363223479,15.44664319460541,0
12.64616608049998,15.10838775257841,0
15.54763373107359,16.43238820991158,0
14.4007699774828,15.21258204276164,0
15.21058389990948,14.93547994178749,0
15.06173440367518,15.11740665636805,0
14.86214589875373,14.70177771082854,0
15.40451989437227,15.34490711864667,0
13.79430574831448,14.68727111247282,0
14.63390271757003,16.30082803685785,0
12.45687580804446,15.54617986485219,0
13.99759772841731,16.73594542008409,0
12.93253733568772,12.62389976814524,0
13.70345190616539,14.71480993356161,0
13.12395594125503,15.44848980937747,0
13.81691009423219,14.09233539217894,0
13.02489337092878,14.25050251544228,0
14.53425534561566,15.76596516545384,0
13.25186260458783,16.3225231885698,0
13.23657554891477,15.33696609589177,0
12.1297131595538,12.66688846478064,0
14.3808873556303,16.03087164666765,0
15.98239721601976,15.52399453253037,0
13.75107909980303,13.64320737566979,0
13.35730012174231,13.42431786138274,0
13.08559089708043,14.86775905977197,0
13.6117330216296,14.86806413838196,0
15.1776173709485,14.15354188009321,0
14.15456588767872,15.28746897631645,0
13.22531906267953,13.9598546965538,0
13.94151500958564,14.76023193066396,0
15.39066478902675,15.71412823472551,0
13.17642606705518,13.67395694240669,0
13.38689005901117,14.66536821990745,0
15.15888821036137,14.78211270885843,0
14.55599224830758,14.04946255637684,0
14.62692885570043,14.29592015439668,0
13.28624407169681,15.6581260669439,0
13.8154823515179,14.1716943145893,0
14.3109896419094,16.25419059506493,0
13.53597112272297,15.77020127180871,0
14.80103055297733,13.81813140471321,0
13.77274485542839,14.64955360893938,0
13.76510156692244,15.02311286948475,0
14.05349835921094,13.93946896423697,0
15.30905390162218,16.04190604522437,0
13.15523771144825,16.9212211680188,0
12.69940390796505,13.99916733869651,0
14.3679922537568,16.75782353966251,0
13.2632541853177,14.09898705600851,0
11.91253508924009,14.61325734486844,0
13.37000592461161,15.18268143261131,0
15.99450697482097,15.4532938283601,0
14.15764860588238,13.77083846575649,0
14.96982662482653,15.59222552688896,0
14.75068711060737,15.46889187883478,0
13.33027919659259,14.34699591207669,0
13.05002153442813,14.68726188711367,0
13.77642646984253,14.23618563920568,0
15.17426585206286,15.5095749119089,0
14.21251759323552,15.08270517066944,0
13.82089482923982,15.61146315929325,0
14.12355955034152,14.95509753853501,0
14.54752171050364,14.85861945287413,0
14.09944359402792,16.03131199865159,0
14.57730180008498,14.25667659137451,0
14.52331832390665,14.2300499886642,0
14.30044704017983,15.26643299159799,0
14.55839285912062,15.48691913661183,0
14.22494186934392,15.86117827216267,0
12.04029344338111,13.34483350304919,0
13.07931049306772,9.347878119065356,1
21.7271340215587,4.126232224310076,1
12.4766288158932,14.4593696654036,1
19.5825727723877,10.4116189967773,1
23.33986752737173,16.29887355272053,1
18.2611884383863,17.9783089957873,1
4.752612823293772,24.35040724802435,1
1 Latency (ms) Throughput (mb/s) Anomaly
2 13.04681516870484 14.7411524132184 0
3 13.4085201853932 13.76326960024047 0
4 14.19591481245491 15.85318112982812 0
5 14.91470076531303 16.17425986715807 0
6 13.5766996051752 14.04284943755652 0
7 13.92240250750028 13.40646893666083 0
8 12.82213163903098 14.22318782380161 0
9 15.6763661470048 15.89169137219994 0
10 16.16287532482238 16.20299807446642 0
11 12.66645094909174 14.8990837351338 1
12 13.98454962300191 12.95800821585463 0
13 14.06146043109355 14.54908874282629 0
14 13.38988671215899 15.56202141787754 0
15 13.39350474623341 15.62698794188875 0
16 13.97900926099814 13.28061494266342 0
17 14.16791258723419 14.46583828507579 0
18 13.96176145283657 14.75182421254904 0
19 14.45899735355037 15.07018562997125 0
20 14.58476371878708 15.82743423785702 0
21 12.07427073619131 13.06711089796514 0
22 13.54912940444922 15.53827676982062 0
23 13.98625041879221 14.78776303583677 0
24 14.96991942049244 16.51830493015889 0
25 14.2557659665841 15.29427277420701 0
26 15.33425000108006 16.12469988952639 0
27 15.63504869777692 16.49094476663806 0
28 13.62081291712303 15.45947525058772 0
29 14.81548484709227 15.33956526603583 0
30 14.59318972857327 14.61238105671215 0
31 14.48906754712418 15.64087368177291 0
32 15.52704801171451 14.63568031226173 0
33 13.97506707358789 14.76531532927648 0
34 12.95364954381841 14.82328512087584 0
35 12.88787444214799 15.07607810133002 0
36 16.02178960565569 16.25746991816081 0
37 14.9262927071427 16.29725072434191 0
38 12.46559400363085 14.18321211753596 0
39 14.08466278107714 14.44192203204038 0
40 14.53717522545769 14.24224248113181 0
41 14.22250851601845 15.42386187610343 0
42 14.51908495978717 13.99871698993444 0
43 13.11971433616167 14.66081845898369 0
44 14.5108889424642 15.30465148682366 0
45 14.18262426407451 15.3938896849634 0
46 14.71651844926282 15.73369667477785 0
47 13.83454699853918 16.17138034441191 0
48 16.00076179182642 14.69232970320203 0
49 14.12702715242892 15.91462774747984 0
50 13.84578546855034 14.34139348861173 0
51 15.41426110064101 16.24243182463628 1
52 13.25273726696165 15.00861363933526 0
53 13.66840226015763 14.35886035673854 0
54 13.77534773921765 14.73808512203812 0
55 14.12582342640922 14.92980922624493 0
56 14.54724604324321 15.6333944514067 0
57 14.15258077112493 14.53622696521789 0
58 14.12648161131633 15.34467591276852 0
59 14.26324658304056 14.98556918087115 0
60 14.77324331862399 15.25299473774317 0
61 14.20969933686442 16.14572569071713 0
62 13.260655152992 15.48016214411599 0
63 14.25273350867239 15.03134360663839 0
64 12.92124446791387 13.19321540142361 0
65 13.852431292546 13.33213110580615 0
66 13.96856800302965 13.19821236714215 0
67 13.25206981975186 15.36846390294601 0
68 13.70449633962696 13.21431301976872 0
69 14.5087472134072 15.46051652161006 0
70 15.69042695638351 16.48168851978138 0
71 12.95598191982515 12.43703005897334 0
72 13.59312604041728 14.84189902611636 0
73 15.12874638631439 17.14981222613881 0
74 14.26705036670259 15.67551973639503 0
75 15.6614505451442 14.81146451457414 0
76 14.33962672797097 15.49202297710026 0
77 14.2761765458781 14.70590693250814 0
78 14.86049072335336 15.59000779027686 0
79 14.10414479623351 15.1805045637764 0
80 15.98828286381979 15.62105187028486 0
81 13.47473582792461 15.59307141917535 0
82 13.77637601475249 14.99194426684731 0
83 12.82770875129005 15.67136906874635 0
84 13.67165486007913 15.11954159126301 0
85 15.38704283906103 15.56936935237784 0
86 15.54320933642332 15.51543150058866 0
87 13.85306094119846 15.60672436869602 0
88 13.62525245784644 14.45209462876985 0
89 15.0157784412311 14.91664093008973 0
90 13.83645753449745 15.24940725360926 0
91 14.22694438547307 14.3479843622948 0
92 13.23742625416296 14.61058751286003 0
93 13.38482919115422 14.7331933025011 0
94 13.87130103241151 14.97399468636979 0
95 12.39445846815594 14.64448216946588 0
96 14.32186557845068 14.52890629439163 0
97 15.82965092460402 15.71619455432355 0
98 15.80177302202355 16.01808914480403 0
99 14.69751200330076 14.11198748714029 0
100 14.70598656653535 16.46040295414171 0
101 13.59156859810395 14.91975097196414 0
102 12.29984538869378 14.77119467910275 0
103 13.3990474777037 16.11912910518291 0
104 15.13112869806696 15.90031130320181 0
105 15.38581197702793 15.71453967469415 0
106 15.45487421920634 15.4404224240544 0
107 13.74951530855867 15.26803135994583 0
108 15.69914333094722 16.05595814533895 0
109 14.80580490719942 14.33258926354469 0
110 15.17222942648117 16.70624397729834 0
111 11.24915511828765 15.13295896107001 0
112 13.88773906521638 14.48548132472444 0
113 15.3258701791002 16.58524064023295 0
114 12.97517063349011 15.1605677140184 0
115 14.07427780835002 17.21973519125371 0
116 14.1820256369139 17.83351945487566 0
117 12.23970014041095 14.72866833837743 0
118 14.82555960703615 15.94500684833057 0
119 13.09763368416417 16.23036500469445 0
120 13.85758877756093 15.03526838191721 0
121 15.52502523459987 16.78653607805479 0
122 15.31499528329094 14.56835427536349 0
123 14.03034873517879 15.6633618769716 0
124 14.42312994571211 14.94109334872472 0
125 13.63615118835241 14.96411634434718 0
126 14.53477942776931 13.35611764012331 0
127 14.61566223678644 14.15241034694619 0
128 13.08085544352481 14.0284594118694 0
129 14.93928677902786 14.54933745884242 0
130 16.0271266262212 15.70965830468461 0
131 14.31925037139242 15.11762658185582 0
132 14.86153307492049 14.28458412390706 0
133 14.01432032507764 16.77971266133154 0
134 13.40765469906171 14.60041190939531 0
135 13.0795973186072 14.19389917316378 0
136 12.68820688788819 13.81109597020173 0
137 14.19232756586644 15.36498178724437 0
138 14.86589365075524 14.47138789706538 0
139 13.39350297747264 14.34389892642248 0
140 13.58659142682796 14.39148496395445 0
141 13.10219289551651 14.3760326021477 0
142 14.54176555566262 16.37233995317341 0
143 14.25602703003231 15.0423494965284 0
144 16.18754760471493 16.36145253974863 0
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308 4.752612823293772 24.35040724802435 1

156
libdataanalysis/2-线性回归代码实现/data/world-happiness-report-2017.csv
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@ -1,156 +0,0 @@
"Country","Happiness.Rank","Happiness.Score","Whisker.high","Whisker.low","Economy..GDP.per.Capita.","Family","Health..Life.Expectancy.","Freedom","Generosity","Trust..Government.Corruption.","Dystopia.Residual"
"Norway",1,7.53700017929077,7.59444482058287,7.47955553799868,1.61646318435669,1.53352355957031,0.796666502952576,0.635422587394714,0.36201223731041,0.315963834524155,2.27702665328979
"Denmark",2,7.52199983596802,7.58172806486487,7.46227160707116,1.48238301277161,1.55112159252167,0.792565524578094,0.626006722450256,0.355280488729477,0.40077006816864,2.31370735168457
"Iceland",3,7.50400018692017,7.62203047305346,7.38596990078688,1.480633020401,1.6105740070343,0.833552122116089,0.627162635326385,0.475540220737457,0.153526559472084,2.32271528244019
"Switzerland",4,7.49399995803833,7.56177242040634,7.42622749567032,1.56497955322266,1.51691174507141,0.858131289482117,0.620070576667786,0.290549278259277,0.367007285356522,2.2767162322998
"Finland",5,7.4689998626709,7.52754207581282,7.41045764952898,1.44357192516327,1.5402467250824,0.80915766954422,0.617950856685638,0.24548277258873,0.38261154294014,2.4301815032959
"Netherlands",6,7.3769998550415,7.42742584124207,7.32657386884093,1.50394463539124,1.42893922328949,0.810696125030518,0.585384488105774,0.470489829778671,0.282661825418472,2.29480409622192
"Canada",7,7.31599998474121,7.38440283536911,7.24759713411331,1.47920441627502,1.48134899139404,0.83455765247345,0.611100912094116,0.435539722442627,0.287371516227722,2.18726444244385
"New Zealand",8,7.31400012969971,7.3795104418695,7.24848981752992,1.40570604801178,1.54819512367249,0.816759705543518,0.614062130451202,0.500005125999451,0.382816702127457,2.0464563369751
"Sweden",9,7.28399991989136,7.34409487739205,7.22390496239066,1.49438726902008,1.47816216945648,0.830875158309937,0.612924098968506,0.385399252176285,0.384398728609085,2.09753799438477
"Australia",10,7.28399991989136,7.35665122494102,7.2113486148417,1.484414935112,1.51004195213318,0.84388679265976,0.601607382297516,0.477699249982834,0.301183730363846,2.06521081924438
"Israel",11,7.21299982070923,7.27985325649381,7.14614638492465,1.37538242340088,1.37628996372223,0.83840399980545,0.405988603830338,0.330082654953003,0.0852421000599861,2.80175733566284
"Costa Rica",12,7.0789999961853,7.16811166629195,6.98988832607865,1.10970628261566,1.41640365123749,0.759509265422821,0.580131649971008,0.214613229036331,0.100106589496136,2.89863920211792
"Austria",13,7.00600004196167,7.07066981211305,6.94133027181029,1.48709726333618,1.4599449634552,0.815328419208527,0.567766189575195,0.316472321748734,0.221060365438461,2.1385064125061
"United States",14,6.99300003051758,7.07465674757957,6.91134331345558,1.54625928401947,1.41992056369781,0.77428662776947,0.505740523338318,0.392578780651093,0.135638788342476,2.2181134223938
"Ireland",15,6.97700023651123,7.04335166752338,6.91064880549908,1.53570663928986,1.55823111534119,0.80978262424469,0.573110342025757,0.42785832285881,0.29838815331459,1.77386903762817
"Germany",16,6.95100021362305,7.00538156926632,6.89661885797977,1.48792338371277,1.47252035140991,0.798950731754303,0.562511384487152,0.336269170045853,0.276731938123703,2.01576995849609
"Belgium",17,6.89099979400635,6.95582075044513,6.82617883756757,1.46378076076508,1.46231269836426,0.818091869354248,0.539770722389221,0.231503337621689,0.251343131065369,2.12421035766602
"Luxembourg",18,6.86299991607666,6.92368609987199,6.80231373228133,1.74194359779358,1.45758366584778,0.845089495182037,0.59662789106369,0.283180981874466,0.31883442401886,1.61951208114624
"United Kingdom",19,6.71400022506714,6.78379176110029,6.64420868903399,1.44163393974304,1.49646008014679,0.805335938930511,0.508190035820007,0.492774158716202,0.265428066253662,1.70414352416992
"Chile",20,6.65199995040894,6.73925056010485,6.56474934071302,1.25278460979462,1.28402495384216,0.819479703903198,0.376895278692245,0.326662421226501,0.0822879821062088,2.50958585739136
"United Arab Emirates",21,6.64799976348877,6.72204730376601,6.57395222321153,1.62634336948395,1.26641023159027,0.726798236370087,0.60834527015686,0.3609419465065,0.324489563703537,1.734703540802
"Brazil",22,6.63500022888184,6.72546950161457,6.5445309561491,1.10735321044922,1.43130600452423,0.616552352905273,0.437453746795654,0.16234989464283,0.111092761158943,2.76926708221436
"Czech Republic",23,6.60900020599365,6.68386246263981,6.5341379493475,1.35268235206604,1.43388521671295,0.754444003105164,0.490946173667908,0.0881067588925362,0.0368729270994663,2.45186185836792
"Argentina",24,6.59899997711182,6.69008508607745,6.50791486814618,1.18529546260834,1.44045114517212,0.695137083530426,0.494519203901291,0.109457060694695,0.059739887714386,2.61400532722473
"Mexico",25,6.57800006866455,6.67114890769124,6.48485122963786,1.15318381786346,1.210862159729,0.709978997707367,0.412730008363724,0.120990432798862,0.132774114608765,2.83715486526489
"Singapore",26,6.57200002670288,6.63672306910157,6.50727698430419,1.69227766990662,1.35381436347961,0.949492394924164,0.549840569496155,0.345965981483459,0.46430778503418,1.21636199951172
"Malta",27,6.52699995040894,6.59839677289128,6.45560312792659,1.34327983856201,1.48841166496277,0.821944236755371,0.588767051696777,0.574730575084686,0.153066068887711,1.55686283111572
"Uruguay",28,6.4539999961853,6.54590621769428,6.36209377467632,1.21755969524384,1.41222786903381,0.719216823577881,0.57939225435257,0.175096929073334,0.178061872720718,2.17240953445435
"Guatemala",29,6.4539999961853,6.56687397271395,6.34112601965666,0.872001945972443,1.25558519363403,0.540239989757538,0.531310617923737,0.283488392829895,0.0772232785820961,2.89389109611511
"Panama",30,6.4520001411438,6.55713071614504,6.34686956614256,1.23374843597412,1.37319254875183,0.706156134605408,0.550026834011078,0.21055693924427,0.070983923971653,2.30719995498657
"France",31,6.44199991226196,6.51576780244708,6.36823202207685,1.43092346191406,1.38777685165405,0.844465851783752,0.470222115516663,0.129762306809425,0.172502428293228,2.00595474243164
"Thailand",32,6.42399978637695,6.50911685571074,6.33888271704316,1.12786877155304,1.42579245567322,0.647239029407501,0.580200731754303,0.572123110294342,0.0316127352416515,2.03950834274292
"Taiwan Province of China",33,6.42199993133545,6.49459602192044,6.34940384075046,1.43362653255463,1.38456535339355,0.793984234333038,0.361466586589813,0.258360475301743,0.0638292357325554,2.1266074180603
"Spain",34,6.40299987792969,6.4710548453033,6.33494491055608,1.38439786434174,1.53209090232849,0.888960599899292,0.408781230449677,0.190133571624756,0.0709140971302986,1.92775774002075
"Qatar",35,6.375,6.56847681432962,6.18152318567038,1.87076568603516,1.27429687976837,0.710098087787628,0.604130983352661,0.330473870038986,0.439299255609512,1.1454644203186
"Colombia",36,6.35699987411499,6.45202005416155,6.26197969406843,1.07062232494354,1.4021829366684,0.595027923583984,0.477487415075302,0.149014472961426,0.0466687418520451,2.61606812477112
"Saudi Arabia",37,6.3439998626709,6.44416661202908,6.24383311331272,1.53062355518341,1.28667759895325,0.590148329734802,0.449750572443008,0.147616013884544,0.27343225479126,2.0654296875
"Trinidad and Tobago",38,6.16800022125244,6.38153389066458,5.95446655184031,1.36135590076447,1.3802285194397,0.519983291625977,0.518630743026733,0.325296461582184,0.00896481610834599,2.05324745178223
"Kuwait",39,6.10500001907349,6.1919569888711,6.01804304927588,1.63295245170593,1.25969874858856,0.632105708122253,0.496337592601776,0.228289797902107,0.215159550309181,1.64042520523071
"Slovakia",40,6.09800004959106,6.1773484121263,6.01865168705583,1.32539355754852,1.50505924224854,0.712732911109924,0.295817464590073,0.136544480919838,0.0242108516395092,2.09777665138245
"Bahrain",41,6.08699989318848,6.17898906782269,5.99501071855426,1.48841226100922,1.32311046123505,0.653133034706116,0.536746919155121,0.172668486833572,0.257042169570923,1.65614938735962
"Malaysia",42,6.08400011062622,6.17997963652015,5.98802058473229,1.29121541976929,1.28464603424072,0.618784427642822,0.402264982461929,0.416608929634094,0.0656007081270218,2.00444889068604
"Nicaragua",43,6.07100009918213,6.18658360034227,5.95541659802198,0.737299203872681,1.28721570968628,0.653095960617065,0.447551846504211,0.301674216985703,0.130687981843948,2.51393055915833
"Ecuador",44,6.00799989700317,6.10584767535329,5.91015211865306,1.00082039833069,1.28616881370544,0.685636222362518,0.4551981985569,0.150112465023994,0.140134647488594,2.29035258293152
"El Salvador",45,6.00299978256226,6.108635122329,5.89736444279552,0.909784495830536,1.18212509155273,0.596018552780151,0.432452529668808,0.0782579854130745,0.0899809598922729,2.7145938873291
"Poland",46,5.97300004959106,6.05390834122896,5.89209175795317,1.29178786277771,1.44571197032928,0.699475347995758,0.520342111587524,0.158465966582298,0.0593078061938286,1.79772281646729
"Uzbekistan",47,5.97100019454956,6.06553757295012,5.876462816149,0.786441087722778,1.54896914958954,0.498272627592087,0.658248662948608,0.415983647108078,0.246528223156929,1.81691360473633
"Italy",48,5.96400022506714,6.04273690596223,5.88526354417205,1.39506661891937,1.44492328166962,0.853144347667694,0.256450712680817,0.17278964817524,0.0280280914157629,1.81331205368042
"Russia",49,5.96299982070923,6.03027490749955,5.89572473391891,1.28177809715271,1.46928238868713,0.547349333763123,0.373783111572266,0.0522638224065304,0.0329628810286522,2.20560741424561
"Belize",50,5.95599985122681,6.19724231779575,5.71475738465786,0.907975316047668,1.08141779899597,0.450191766023636,0.547509372234344,0.240015640854836,0.0965810716152191,2.63195562362671
"Japan",51,5.92000007629395,5.99071944460273,5.84928070798516,1.41691517829895,1.43633782863617,0.913475871086121,0.505625545978546,0.12057276815176,0.163760736584663,1.36322355270386
"Lithuania",52,5.90199995040894,5.98266964137554,5.82133025944233,1.31458234786987,1.47351610660553,0.62894994020462,0.234231784939766,0.010164656676352,0.0118656428530812,2.22844052314758
"Algeria",53,5.87200021743774,5.97828643366694,5.76571400120854,1.09186446666718,1.1462174654007,0.617584645748138,0.233335807919502,0.0694366469979286,0.146096110343933,2.56760382652283
"Latvia",54,5.84999990463257,5.92026353821158,5.77973627105355,1.26074862480164,1.40471494197845,0.638566970825195,0.325707912445068,0.153074786067009,0.0738427266478539,1.99365520477295
"South Korea",55,5.83799982070923,5.92255902826786,5.7534406131506,1.40167844295502,1.12827444076538,0.900214076042175,0.257921665906906,0.206674367189407,0.0632826685905457,1.88037800788879
"Moldova",56,5.83799982070923,5.90837083846331,5.76762880295515,0.728870630264282,1.25182557106018,0.589465200901031,0.240729048848152,0.208779126405716,0.0100912861526012,2.80780839920044
"Romania",57,5.82499980926514,5.91969415679574,5.73030546173453,1.21768391132355,1.15009129047394,0.685158312320709,0.457003742456436,0.133519917726517,0.00438790069893003,2.17683148384094
"Bolivia",58,5.82299995422363,5.9039769025147,5.74202300593257,0.833756566047668,1.22761905193329,0.473630249500275,0.558732926845551,0.22556072473526,0.0604777261614799,2.44327902793884
"Turkmenistan",59,5.82200002670288,5.88518087550998,5.75881917789578,1.13077676296234,1.49314916133881,0.437726080417633,0.41827192902565,0.24992498755455,0.259270340204239,1.83290982246399
"Kazakhstan",60,5.81899976730347,5.90364177465439,5.73435775995255,1.28455626964569,1.38436901569366,0.606041550636292,0.437454283237457,0.201964423060417,0.119282886385918,1.78489255905151
"North Cyprus",61,5.80999994277954,5.89736646488309,5.72263342067599,1.3469113111496,1.18630337715149,0.834647238254547,0.471203625202179,0.266845703125,0.155353352427483,1.54915761947632
"Slovenia",62,5.75799989700317,5.84222516000271,5.67377463400364,1.3412059545517,1.45251882076263,0.790828227996826,0.572575807571411,0.242649093270302,0.0451289787888527,1.31331729888916
"Peru",63,5.71500015258789,5.81194677859545,5.61805352658033,1.03522527217865,1.21877038478851,0.630166113376617,0.450002878904343,0.126819714903831,0.0470490865409374,2.20726943016052
"Mauritius",64,5.62900018692017,5.72986219167709,5.52813818216324,1.18939554691315,1.20956099033356,0.638007462024689,0.491247326135635,0.360933750867844,0.0421815551817417,1.6975839138031
"Cyprus",65,5.62099981307983,5.71469269931316,5.5273069268465,1.35593807697296,1.13136327266693,0.84471470117569,0.355111539363861,0.271254301071167,0.0412379764020443,1.62124919891357
"Estonia",66,5.61100006103516,5.68813987419009,5.53386024788022,1.32087934017181,1.47667109966278,0.695168316364288,0.479131430387497,0.0988908112049103,0.183248922228813,1.35750865936279
"Belarus",67,5.56899976730347,5.64611424401402,5.49188529059291,1.15655755996704,1.44494521617889,0.637714266777039,0.295400261878967,0.15513750910759,0.156313821673393,1.72323298454285
"Libya",68,5.52500009536743,5.67695380687714,5.37304638385773,1.10180306434631,1.35756433010101,0.520169019699097,0.465733230113983,0.152073666453362,0.0926102101802826,1.83501124382019
"Turkey",69,5.5,5.59486496329308,5.40513503670692,1.19827437400818,1.33775317668915,0.637605607509613,0.300740599632263,0.0466930419206619,0.0996715798974037,1.87927794456482
"Paraguay",70,5.49300003051758,5.57738126963377,5.40861879140139,0.932537317276001,1.50728487968445,0.579250693321228,0.473507791757584,0.224150657653809,0.091065913438797,1.6853334903717
"Hong Kong S.A.R., China",71,5.47200012207031,5.54959417313337,5.39440607100725,1.55167484283447,1.26279091835022,0.943062424659729,0.490968644618988,0.374465793371201,0.293933749198914,0.554633140563965
"Philippines",72,5.42999982833862,5.54533505424857,5.31466460242867,0.85769921541214,1.25391757488251,0.468009054660797,0.585214674472809,0.193513423204422,0.0993318930268288,1.97260475158691
"Serbia",73,5.39499998092651,5.49156965613365,5.29843030571938,1.06931757926941,1.25818979740143,0.65078467130661,0.208715528249741,0.220125883817673,0.0409037806093693,1.94708442687988
"Jordan",74,5.33599996566772,5.44841002240777,5.22358990892768,0.991012394428253,1.23908889293671,0.604590058326721,0.418421149253845,0.172170460224152,0.11980327218771,1.79117655754089
"Hungary",75,5.32399988174438,5.40303970918059,5.24496005430818,1.2860119342804,1.34313309192657,0.687763452529907,0.175863519310951,0.0784016624093056,0.0366369374096394,1.71645927429199
"Jamaica",76,5.31099987030029,5.58139872848988,5.04060101211071,0.925579309463501,1.36821806430817,0.641022384166718,0.474307239055634,0.233818337321281,0.0552677810192108,1.61232566833496
"Croatia",77,5.29300022125244,5.39177720457315,5.19422323793173,1.22255623340607,0.96798300743103,0.701288521289825,0.255772292613983,0.248002976179123,0.0431031100451946,1.85449242591858
"Kosovo",78,5.27899980545044,5.36484799548984,5.19315161541104,0.951484382152557,1.13785350322723,0.541452050209045,0.260287940502167,0.319931447505951,0.0574716180562973,2.01054072380066
"China",79,5.27299976348877,5.31927808977663,5.2267214372009,1.08116579055786,1.16083741188049,0.741415500640869,0.472787708044052,0.0288068410009146,0.0227942746132612,1.76493859291077
"Pakistan",80,5.26900005340576,5.35998364135623,5.17801646545529,0.72688353061676,0.672690689563751,0.402047783136368,0.23521526157856,0.315446019172668,0.124348066747189,2.79248929023743
"Indonesia",81,5.26200008392334,5.35288859814405,5.17111156970263,0.995538592338562,1.27444469928741,0.492345720529556,0.443323463201523,0.611704587936401,0.0153171354904771,1.42947697639465
"Venezuela",82,5.25,5.3700319455564,5.1299680544436,1.12843120098114,1.43133759498596,0.617144227027893,0.153997123241425,0.0650196298956871,0.0644911229610443,1.78946375846863
"Montenegro",83,5.23699998855591,5.34104444056749,5.13295553654432,1.12112903594971,1.23837649822235,0.667464673519135,0.194989055395126,0.197911024093628,0.0881741940975189,1.72919154167175
"Morocco",84,5.2350001335144,5.31834096476436,5.15165930226445,0.878114581108093,0.774864435195923,0.59771066904068,0.408158332109451,0.0322099551558495,0.0877631828188896,2.45618939399719
"Azerbaijan",85,5.23400020599365,5.29928653523326,5.16871387675405,1.15360176563263,1.15240025520325,0.540775775909424,0.398155838251114,0.0452693402767181,0.180987507104874,1.76248168945312
"Dominican Republic",86,5.23000001907349,5.34906088516116,5.11093915298581,1.07937383651733,1.40241670608521,0.574873745441437,0.55258983373642,0.186967849731445,0.113945253193378,1.31946516036987
"Greece",87,5.22700023651123,5.3252461694181,5.12875430360436,1.28948748111725,1.23941457271576,0.810198903083801,0.0957312509417534,0,0.04328977689147,1.74922156333923
"Lebanon",88,5.22499990463257,5.31888228848577,5.13111752077937,1.07498753070831,1.12962424755096,0.735081076622009,0.288515985012054,0.264450758695602,0.037513829767704,1.69507384300232
"Portugal",89,5.19500017166138,5.28504173308611,5.10495861023665,1.3151752948761,1.36704301834106,0.795843541622162,0.498465299606323,0.0951027125120163,0.0158694516867399,1.10768270492554
"Bosnia and Herzegovina",90,5.18200016021729,5.27633568674326,5.08766463369131,0.982409417629242,1.0693359375,0.705186307430267,0.204403176903725,0.328867495059967,0,1.89217257499695
"Honduras",91,5.18100023269653,5.30158279687166,5.0604176685214,0.730573117733002,1.14394497871399,0.582569479942322,0.348079860210419,0.236188873648643,0.0733454525470734,2.06581115722656
"Macedonia",92,5.17500019073486,5.27217263966799,5.07782774180174,1.06457793712616,1.20789301395416,0.644948184490204,0.325905978679657,0.25376096367836,0.0602777935564518,1.6174693107605
"Somalia",93,5.15100002288818,5.24248370990157,5.0595163358748,0.0226431842893362,0.721151351928711,0.113989137113094,0.602126955986023,0.291631311178207,0.282410323619843,3.11748456954956
"Vietnam",94,5.07399988174438,5.14728076457977,5.000718998909,0.788547575473785,1.27749133110046,0.652168989181519,0.571055591106415,0.234968051314354,0.0876332372426987,1.46231865882874
"Nigeria",95,5.07399988174438,5.20950013548136,4.93849962800741,0.783756256103516,1.21577048301697,0.0569157302379608,0.394952565431595,0.230947196483612,0.0261215660721064,2.36539053916931
"Tajikistan",96,5.04099988937378,5.11142559587956,4.970574182868,0.524713635444641,1.27146327495575,0.529235124588013,0.471566706895828,0.248997643589973,0.146377146244049,1.84904932975769
"Bhutan",97,5.01100015640259,5.07933456212282,4.94266575068235,0.885416388511658,1.34012651443481,0.495879292488098,0.501537680625916,0.474054545164108,0.173380389809608,1.14018440246582
"Kyrgyzstan",98,5.00400018692017,5.08991990312934,4.91808047071099,0.596220076084137,1.39423859119415,0.553457796573639,0.454943388700485,0.42858037352562,0.0394391790032387,1.53672313690186
"Nepal",99,4.96199989318848,5.06735607936978,4.85664370700717,0.479820191860199,1.17928326129913,0.504130780696869,0.440305948257446,0.394096165895462,0.0729755461215973,1.8912410736084
"Mongolia",100,4.95499992370605,5.0216795091331,4.88832033827901,1.02723586559296,1.4930112361908,0.557783484458923,0.394143968820572,0.338464230298996,0.0329022891819477,1.11129236221313
"South Africa",101,4.8289999961853,4.92943518772721,4.72856480464339,1.05469870567322,1.38478863239288,0.187080070376396,0.479246735572815,0.139362379908562,0.0725094974040985,1.51090860366821
"Tunisia",102,4.80499982833862,4.88436700701714,4.72563264966011,1.00726580619812,0.868351459503174,0.613212049007416,0.289680689573288,0.0496933571994305,0.0867231488227844,1.89025115966797
"Palestinian Territories",103,4.77500009536743,4.88184834256768,4.66815184816718,0.716249227523804,1.15564715862274,0.565666973590851,0.25471106171608,0.114173173904419,0.0892826020717621,1.8788902759552
"Egypt",104,4.7350001335144,4.82513378962874,4.64486647740006,0.989701807498932,0.997471392154694,0.520187258720398,0.282110154628754,0.128631442785263,0.114381365478039,1.70216107368469
"Bulgaria",105,4.71400022506714,4.80369470641017,4.62430574372411,1.1614590883255,1.43437945842743,0.708217680454254,0.289231717586517,0.113177694380283,0.0110515309497714,0.996139287948608
"Sierra Leone",106,4.70900011062622,4.85064333498478,4.56735688626766,0.36842092871666,0.984136044979095,0.00556475389748812,0.318697690963745,0.293040901422501,0.0710951760411263,2.66845989227295
"Cameroon",107,4.69500017166138,4.79654085725546,4.5934594860673,0.564305365085602,0.946018218994141,0.132892116904259,0.430388748645782,0.236298456788063,0.0513066314160824,2.3336455821991
"Iran",108,4.69199991226196,4.79822470769286,4.58577511683106,1.15687310695648,0.711551249027252,0.639333188533783,0.249322608113289,0.387242913246155,0.048761073499918,1.49873495101929
"Albania",109,4.64400005340576,4.75246400639415,4.53553610041738,0.996192753314972,0.803685247898102,0.731159746646881,0.381498634815216,0.201312944293022,0.0398642159998417,1.49044156074524
"Bangladesh",110,4.60799980163574,4.68982165828347,4.52617794498801,0.586682975292206,0.735131740570068,0.533241033554077,0.478356659412384,0.172255352139473,0.123717859387398,1.97873616218567
"Namibia",111,4.57399988174438,4.77035474091768,4.37764502257109,0.964434325695038,1.0984708070755,0.33861181139946,0.520303547382355,0.0771337449550629,0.0931469723582268,1.4818902015686
"Kenya",112,4.55299997329712,4.65569159060717,4.45030835598707,0.560479462146759,1.06795072555542,0.309988349676132,0.452763766050339,0.444860309362411,0.0646413192152977,1.6519021987915
"Mozambique",113,4.55000019073486,4.77410232633352,4.3258980551362,0.234305649995804,0.870701014995575,0.106654435396194,0.480791091918945,0.322228103876114,0.179436385631561,2.35565090179443
"Myanmar",114,4.54500007629395,4.61473994642496,4.47526020616293,0.367110550403595,1.12323594093323,0.397522568702698,0.514492034912109,0.838075160980225,0.188816204667091,1.11529040336609
"Senegal",115,4.53499984741211,4.6016037812829,4.46839591354132,0.479309022426605,1.17969191074371,0.409362852573395,0.377922266721725,0.183468893170357,0.115460447967052,1.78964614868164
"Zambia",116,4.51399993896484,4.64410550147295,4.38389437645674,0.636406779289246,1.00318729877472,0.257835894823074,0.461603492498398,0.249580144882202,0.0782135501503944,1.82670545578003
"Iraq",117,4.49700021743774,4.62259140968323,4.37140902519226,1.10271048545837,0.978613197803497,0.501180469989777,0.288555532693863,0.19963726401329,0.107215754687786,1.31890726089478
"Gabon",118,4.46500015258789,4.5573617656529,4.37263853952289,1.1982102394104,1.1556202173233,0.356578588485718,0.312328577041626,0.0437853783369064,0.0760467872023582,1.32291626930237
"Ethiopia",119,4.46000003814697,4.54272867664695,4.377271399647,0.339233845472336,0.86466920375824,0.353409707546234,0.408842742443085,0.312650740146637,0.165455713868141,2.01574373245239
"Sri Lanka",120,4.44000005722046,4.55344719231129,4.32655292212963,1.00985014438629,1.25997638702393,0.625130832195282,0.561213254928589,0.490863561630249,0.0736539661884308,0.419389247894287
"Armenia",121,4.37599992752075,4.46673461228609,4.28526524275541,0.900596737861633,1.00748372077942,0.637524425983429,0.198303267359734,0.0834880918264389,0.0266744215041399,1.5214991569519
"India",122,4.31500005722046,4.37152201749384,4.25847809694707,0.792221248149872,0.754372596740723,0.455427616834641,0.469987004995346,0.231538489460945,0.0922268852591515,1.5191171169281
"Mauritania",123,4.29199981689453,4.37716361626983,4.20683601751924,0.648457288742065,1.2720308303833,0.285349279642105,0.0960980430245399,0.201870024204254,0.136957004666328,1.65163731575012
"Congo (Brazzaville)",124,4.29099988937378,4.41005350500345,4.17194627374411,0.808964252471924,0.832044363021851,0.28995743393898,0.435025870800018,0.120852127671242,0.0796181336045265,1.72413563728333
"Georgia",125,4.28599977493286,4.37493396580219,4.19706558406353,0.950612664222717,0.57061493396759,0.649546980857849,0.309410035610199,0.0540088154375553,0.251666635274887,1.50013780593872
"Congo (Kinshasa)",126,4.28000020980835,4.35781083270907,4.20218958690763,0.0921023488044739,1.22902345657349,0.191407024860382,0.235961347818375,0.246455833315849,0.0602413564920425,2.22495865821838
"Mali",127,4.19000005722046,4.26967071101069,4.11032940343022,0.476180493831635,1.28147339820862,0.169365674257278,0.306613743305206,0.183354198932648,0.104970246553421,1.66819095611572
"Ivory Coast",128,4.17999982833862,4.27518256321549,4.08481709346175,0.603048920631409,0.904780030250549,0.0486421696841717,0.447706192731857,0.201237469911575,0.130061775445938,1.84496426582336
"Cambodia",129,4.16800022125244,4.27851781353354,4.05748262897134,0.601765096187592,1.00623834133148,0.429783403873444,0.633375823497772,0.385922968387604,0.0681059509515762,1.04294109344482
"Sudan",130,4.13899993896484,4.34574716508389,3.9322527128458,0.65951669216156,1.21400856971741,0.290920823812485,0.0149958552792668,0.182317450642586,0.089847519993782,1.68706583976746
"Ghana",131,4.11999988555908,4.22270720854402,4.01729256257415,0.667224824428558,0.873664736747742,0.295637726783752,0.423026293516159,0.256923943758011,0.0253363698720932,1.57786750793457
"Ukraine",132,4.09600019454956,4.18541010454297,4.00659028455615,0.89465194940567,1.39453756809235,0.575903952121735,0.122974775731564,0.270061463117599,0.0230294708162546,0.814382314682007
"Uganda",133,4.08099985122681,4.19579996705055,3.96619973540306,0.381430715322495,1.12982773780823,0.217632606625557,0.443185955286026,0.325766056776047,0.057069718837738,1.526362657547
"Burkina Faso",134,4.03200006484985,4.12405906438828,3.93994106531143,0.3502277135849,1.04328000545502,0.215844258666039,0.324367851018906,0.250864684581757,0.120328105986118,1.72721290588379
"Niger",135,4.02799987792969,4.11194681972265,3.94405293613672,0.161925330758095,0.993025004863739,0.26850500702858,0.36365869641304,0.228673845529556,0.138572946190834,1.87398338317871
"Malawi",136,3.97000002861023,4.07747881740332,3.86252123981714,0.233442038297653,0.512568831443787,0.315089583396912,0.466914653778076,0.287170469760895,0.0727116540074348,2.08178615570068
"Chad",137,3.93600010871887,4.0347115239501,3.83728869348764,0.438012987375259,0.953855872154236,0.0411347150802612,0.16234202682972,0.216113850474358,0.0535818822681904,2.07123804092407
"Zimbabwe",138,3.875,3.97869964271784,3.77130035728216,0.375846534967422,1.08309590816498,0.196763753890991,0.336384207010269,0.189143493771553,0.0953753814101219,1.59797024726868
"Lesotho",139,3.80800008773804,4.04434397548437,3.5716561999917,0.521021246910095,1.19009518623352,0,0.390661299228668,0.157497271895409,0.119094640016556,1.42983531951904
"Angola",140,3.79500007629395,3.95164193540812,3.63835821717978,0.858428180217743,1.10441195964813,0.0498686656355858,0,0.097926490008831,0.0697203353047371,1.61448240280151
"Afghanistan",141,3.79399991035461,3.87366141527891,3.71433840543032,0.401477217674255,0.581543326377869,0.180746778845787,0.106179520487785,0.311870932579041,0.0611578300595284,2.15080118179321
"Botswana",142,3.76600003242493,3.87412266626954,3.65787739858031,1.12209415435791,1.22155499458313,0.341755509376526,0.505196332931519,0.0993484482169151,0.0985831990838051,0.3779137134552
"Benin",143,3.65700006484985,3.74578355133533,3.56821657836437,0.431085407733917,0.435299843549728,0.209930211305618,0.425962775945663,0.207948461174965,0.0609290152788162,1.88563096523285
"Madagascar",144,3.64400005340576,3.71431910589337,3.57368100091815,0.305808693170547,0.913020372390747,0.375223308801651,0.189196765422821,0.208732530474663,0.0672319754958153,1.58461260795593
"Haiti",145,3.6029999256134,3.73471479773521,3.47128505349159,0.368610262870789,0.640449821949005,0.277321130037308,0.0303698573261499,0.489203780889511,0.0998721495270729,1.69716763496399
"Yemen",146,3.59299993515015,3.69275031983852,3.49324955046177,0.591683447360992,0.93538224697113,0.310080915689468,0.249463722109795,0.104125209152699,0.0567674227058887,1.34560060501099
"South Sudan",147,3.59100008010864,3.72553858578205,3.45646157443523,0.39724862575531,0.601323127746582,0.163486003875732,0.147062435746193,0.285670816898346,0.116793513298035,1.87956738471985
"Liberia",148,3.53299999237061,3.65375626087189,3.41224372386932,0.119041793048382,0.872117936611176,0.229918196797371,0.332881182432175,0.26654988527298,0.0389482490718365,1.67328596115112
"Guinea",149,3.50699996948242,3.58442812889814,3.4295718100667,0.244549930095673,0.791244685649872,0.194129139184952,0.348587512969971,0.264815092086792,0.110937617719173,1.55231189727783
"Togo",150,3.49499988555908,3.59403811171651,3.39596165940166,0.305444717407227,0.431882530450821,0.247105568647385,0.38042613863945,0.196896150708199,0.0956650152802467,1.83722925186157
"Rwanda",151,3.47099995613098,3.54303023353219,3.39896967872977,0.368745893239975,0.945707023143768,0.326424807310104,0.581843852996826,0.252756029367447,0.455220013856888,0.540061235427856
"Syria",152,3.46199989318848,3.66366855680943,3.26033122956753,0.777153134346008,0.396102607250214,0.50053334236145,0.0815394446253777,0.493663728237152,0.151347130537033,1.06157350540161
"Tanzania",153,3.34899997711182,3.46142975538969,3.23657019883394,0.511135876178741,1.04198980331421,0.364509284496307,0.390017777681351,0.354256361722946,0.0660351067781448,0.621130466461182
"Burundi",154,2.90499997138977,3.07469033300877,2.73530960977077,0.091622568666935,0.629793584346771,0.151610791683197,0.0599007532000542,0.204435184597969,0.0841479450464249,1.68302416801453
"Central African Republic",155,2.69300007820129,2.86488426923752,2.52111588716507,0,0,0.0187726859003305,0.270842045545578,0.280876487493515,0.0565650761127472,2.06600475311279
1 Country Happiness.Rank Happiness.Score Whisker.high Whisker.low Economy..GDP.per.Capita. Family Health..Life.Expectancy. Freedom Generosity Trust..Government.Corruption. Dystopia.Residual
2 Norway 1 7.53700017929077 7.59444482058287 7.47955553799868 1.61646318435669 1.53352355957031 0.796666502952576 0.635422587394714 0.36201223731041 0.315963834524155 2.27702665328979
3 Denmark 2 7.52199983596802 7.58172806486487 7.46227160707116 1.48238301277161 1.55112159252167 0.792565524578094 0.626006722450256 0.355280488729477 0.40077006816864 2.31370735168457
4 Iceland 3 7.50400018692017 7.62203047305346 7.38596990078688 1.480633020401 1.6105740070343 0.833552122116089 0.627162635326385 0.475540220737457 0.153526559472084 2.32271528244019
5 Switzerland 4 7.49399995803833 7.56177242040634 7.42622749567032 1.56497955322266 1.51691174507141 0.858131289482117 0.620070576667786 0.290549278259277 0.367007285356522 2.2767162322998
6 Finland 5 7.4689998626709 7.52754207581282 7.41045764952898 1.44357192516327 1.5402467250824 0.80915766954422 0.617950856685638 0.24548277258873 0.38261154294014 2.4301815032959
7 Netherlands 6 7.3769998550415 7.42742584124207 7.32657386884093 1.50394463539124 1.42893922328949 0.810696125030518 0.585384488105774 0.470489829778671 0.282661825418472 2.29480409622192
8 Canada 7 7.31599998474121 7.38440283536911 7.24759713411331 1.47920441627502 1.48134899139404 0.83455765247345 0.611100912094116 0.435539722442627 0.287371516227722 2.18726444244385
9 New Zealand 8 7.31400012969971 7.3795104418695 7.24848981752992 1.40570604801178 1.54819512367249 0.816759705543518 0.614062130451202 0.500005125999451 0.382816702127457 2.0464563369751
10 Sweden 9 7.28399991989136 7.34409487739205 7.22390496239066 1.49438726902008 1.47816216945648 0.830875158309937 0.612924098968506 0.385399252176285 0.384398728609085 2.09753799438477
11 Australia 10 7.28399991989136 7.35665122494102 7.2113486148417 1.484414935112 1.51004195213318 0.84388679265976 0.601607382297516 0.477699249982834 0.301183730363846 2.06521081924438
12 Israel 11 7.21299982070923 7.27985325649381 7.14614638492465 1.37538242340088 1.37628996372223 0.83840399980545 0.405988603830338 0.330082654953003 0.0852421000599861 2.80175733566284
13 Costa Rica 12 7.0789999961853 7.16811166629195 6.98988832607865 1.10970628261566 1.41640365123749 0.759509265422821 0.580131649971008 0.214613229036331 0.100106589496136 2.89863920211792
14 Austria 13 7.00600004196167 7.07066981211305 6.94133027181029 1.48709726333618 1.4599449634552 0.815328419208527 0.567766189575195 0.316472321748734 0.221060365438461 2.1385064125061
15 United States 14 6.99300003051758 7.07465674757957 6.91134331345558 1.54625928401947 1.41992056369781 0.77428662776947 0.505740523338318 0.392578780651093 0.135638788342476 2.2181134223938
16 Ireland 15 6.97700023651123 7.04335166752338 6.91064880549908 1.53570663928986 1.55823111534119 0.80978262424469 0.573110342025757 0.42785832285881 0.29838815331459 1.77386903762817
17 Germany 16 6.95100021362305 7.00538156926632 6.89661885797977 1.48792338371277 1.47252035140991 0.798950731754303 0.562511384487152 0.336269170045853 0.276731938123703 2.01576995849609
18 Belgium 17 6.89099979400635 6.95582075044513 6.82617883756757 1.46378076076508 1.46231269836426 0.818091869354248 0.539770722389221 0.231503337621689 0.251343131065369 2.12421035766602
19 Luxembourg 18 6.86299991607666 6.92368609987199 6.80231373228133 1.74194359779358 1.45758366584778 0.845089495182037 0.59662789106369 0.283180981874466 0.31883442401886 1.61951208114624
20 United Kingdom 19 6.71400022506714 6.78379176110029 6.64420868903399 1.44163393974304 1.49646008014679 0.805335938930511 0.508190035820007 0.492774158716202 0.265428066253662 1.70414352416992
21 Chile 20 6.65199995040894 6.73925056010485 6.56474934071302 1.25278460979462 1.28402495384216 0.819479703903198 0.376895278692245 0.326662421226501 0.0822879821062088 2.50958585739136
22 United Arab Emirates 21 6.64799976348877 6.72204730376601 6.57395222321153 1.62634336948395 1.26641023159027 0.726798236370087 0.60834527015686 0.3609419465065 0.324489563703537 1.734703540802
23 Brazil 22 6.63500022888184 6.72546950161457 6.5445309561491 1.10735321044922 1.43130600452423 0.616552352905273 0.437453746795654 0.16234989464283 0.111092761158943 2.76926708221436
24 Czech Republic 23 6.60900020599365 6.68386246263981 6.5341379493475 1.35268235206604 1.43388521671295 0.754444003105164 0.490946173667908 0.0881067588925362 0.0368729270994663 2.45186185836792
25 Argentina 24 6.59899997711182 6.69008508607745 6.50791486814618 1.18529546260834 1.44045114517212 0.695137083530426 0.494519203901291 0.109457060694695 0.059739887714386 2.61400532722473
26 Mexico 25 6.57800006866455 6.67114890769124 6.48485122963786 1.15318381786346 1.210862159729 0.709978997707367 0.412730008363724 0.120990432798862 0.132774114608765 2.83715486526489
27 Singapore 26 6.57200002670288 6.63672306910157 6.50727698430419 1.69227766990662 1.35381436347961 0.949492394924164 0.549840569496155 0.345965981483459 0.46430778503418 1.21636199951172
28 Malta 27 6.52699995040894 6.59839677289128 6.45560312792659 1.34327983856201 1.48841166496277 0.821944236755371 0.588767051696777 0.574730575084686 0.153066068887711 1.55686283111572
29 Uruguay 28 6.4539999961853 6.54590621769428 6.36209377467632 1.21755969524384 1.41222786903381 0.719216823577881 0.57939225435257 0.175096929073334 0.178061872720718 2.17240953445435
30 Guatemala 29 6.4539999961853 6.56687397271395 6.34112601965666 0.872001945972443 1.25558519363403 0.540239989757538 0.531310617923737 0.283488392829895 0.0772232785820961 2.89389109611511
31 Panama 30 6.4520001411438 6.55713071614504 6.34686956614256 1.23374843597412 1.37319254875183 0.706156134605408 0.550026834011078 0.21055693924427 0.070983923971653 2.30719995498657
32 France 31 6.44199991226196 6.51576780244708 6.36823202207685 1.43092346191406 1.38777685165405 0.844465851783752 0.470222115516663 0.129762306809425 0.172502428293228 2.00595474243164
33 Thailand 32 6.42399978637695 6.50911685571074 6.33888271704316 1.12786877155304 1.42579245567322 0.647239029407501 0.580200731754303 0.572123110294342 0.0316127352416515 2.03950834274292
34 Taiwan Province of China 33 6.42199993133545 6.49459602192044 6.34940384075046 1.43362653255463 1.38456535339355 0.793984234333038 0.361466586589813 0.258360475301743 0.0638292357325554 2.1266074180603
35 Spain 34 6.40299987792969 6.4710548453033 6.33494491055608 1.38439786434174 1.53209090232849 0.888960599899292 0.408781230449677 0.190133571624756 0.0709140971302986 1.92775774002075
36 Qatar 35 6.375 6.56847681432962 6.18152318567038 1.87076568603516 1.27429687976837 0.710098087787628 0.604130983352661 0.330473870038986 0.439299255609512 1.1454644203186
37 Colombia 36 6.35699987411499 6.45202005416155 6.26197969406843 1.07062232494354 1.4021829366684 0.595027923583984 0.477487415075302 0.149014472961426 0.0466687418520451 2.61606812477112
38 Saudi Arabia 37 6.3439998626709 6.44416661202908 6.24383311331272 1.53062355518341 1.28667759895325 0.590148329734802 0.449750572443008 0.147616013884544 0.27343225479126 2.0654296875
39 Trinidad and Tobago 38 6.16800022125244 6.38153389066458 5.95446655184031 1.36135590076447 1.3802285194397 0.519983291625977 0.518630743026733 0.325296461582184 0.00896481610834599 2.05324745178223
40 Kuwait 39 6.10500001907349 6.1919569888711 6.01804304927588 1.63295245170593 1.25969874858856 0.632105708122253 0.496337592601776 0.228289797902107 0.215159550309181 1.64042520523071
41 Slovakia 40 6.09800004959106 6.1773484121263 6.01865168705583 1.32539355754852 1.50505924224854 0.712732911109924 0.295817464590073 0.136544480919838 0.0242108516395092 2.09777665138245
42 Bahrain 41 6.08699989318848 6.17898906782269 5.99501071855426 1.48841226100922 1.32311046123505 0.653133034706116 0.536746919155121 0.172668486833572 0.257042169570923 1.65614938735962
43 Malaysia 42 6.08400011062622 6.17997963652015 5.98802058473229 1.29121541976929 1.28464603424072 0.618784427642822 0.402264982461929 0.416608929634094 0.0656007081270218 2.00444889068604
44 Nicaragua 43 6.07100009918213 6.18658360034227 5.95541659802198 0.737299203872681 1.28721570968628 0.653095960617065 0.447551846504211 0.301674216985703 0.130687981843948 2.51393055915833
45 Ecuador 44 6.00799989700317 6.10584767535329 5.91015211865306 1.00082039833069 1.28616881370544 0.685636222362518 0.4551981985569 0.150112465023994 0.140134647488594 2.29035258293152
46 El Salvador 45 6.00299978256226 6.108635122329 5.89736444279552 0.909784495830536 1.18212509155273 0.596018552780151 0.432452529668808 0.0782579854130745 0.0899809598922729 2.7145938873291
47 Poland 46 5.97300004959106 6.05390834122896 5.89209175795317 1.29178786277771 1.44571197032928 0.699475347995758 0.520342111587524 0.158465966582298 0.0593078061938286 1.79772281646729
48 Uzbekistan 47 5.97100019454956 6.06553757295012 5.876462816149 0.786441087722778 1.54896914958954 0.498272627592087 0.658248662948608 0.415983647108078 0.246528223156929 1.81691360473633
49 Italy 48 5.96400022506714 6.04273690596223 5.88526354417205 1.39506661891937 1.44492328166962 0.853144347667694 0.256450712680817 0.17278964817524 0.0280280914157629 1.81331205368042
50 Russia 49 5.96299982070923 6.03027490749955 5.89572473391891 1.28177809715271 1.46928238868713 0.547349333763123 0.373783111572266 0.0522638224065304 0.0329628810286522 2.20560741424561
51 Belize 50 5.95599985122681 6.19724231779575 5.71475738465786 0.907975316047668 1.08141779899597 0.450191766023636 0.547509372234344 0.240015640854836 0.0965810716152191 2.63195562362671
52 Japan 51 5.92000007629395 5.99071944460273 5.84928070798516 1.41691517829895 1.43633782863617 0.913475871086121 0.505625545978546 0.12057276815176 0.163760736584663 1.36322355270386
53 Lithuania 52 5.90199995040894 5.98266964137554 5.82133025944233 1.31458234786987 1.47351610660553 0.62894994020462 0.234231784939766 0.010164656676352 0.0118656428530812 2.22844052314758
54 Algeria 53 5.87200021743774 5.97828643366694 5.76571400120854 1.09186446666718 1.1462174654007 0.617584645748138 0.233335807919502 0.0694366469979286 0.146096110343933 2.56760382652283
55 Latvia 54 5.84999990463257 5.92026353821158 5.77973627105355 1.26074862480164 1.40471494197845 0.638566970825195 0.325707912445068 0.153074786067009 0.0738427266478539 1.99365520477295
56 South Korea 55 5.83799982070923 5.92255902826786 5.7534406131506 1.40167844295502 1.12827444076538 0.900214076042175 0.257921665906906 0.206674367189407 0.0632826685905457 1.88037800788879
57 Moldova 56 5.83799982070923 5.90837083846331 5.76762880295515 0.728870630264282 1.25182557106018 0.589465200901031 0.240729048848152 0.208779126405716 0.0100912861526012 2.80780839920044
58 Romania 57 5.82499980926514 5.91969415679574 5.73030546173453 1.21768391132355 1.15009129047394 0.685158312320709 0.457003742456436 0.133519917726517 0.00438790069893003 2.17683148384094
59 Bolivia 58 5.82299995422363 5.9039769025147 5.74202300593257 0.833756566047668 1.22761905193329 0.473630249500275 0.558732926845551 0.22556072473526 0.0604777261614799 2.44327902793884
60 Turkmenistan 59 5.82200002670288 5.88518087550998 5.75881917789578 1.13077676296234 1.49314916133881 0.437726080417633 0.41827192902565 0.24992498755455 0.259270340204239 1.83290982246399
61 Kazakhstan 60 5.81899976730347 5.90364177465439 5.73435775995255 1.28455626964569 1.38436901569366 0.606041550636292 0.437454283237457 0.201964423060417 0.119282886385918 1.78489255905151
62 North Cyprus 61 5.80999994277954 5.89736646488309 5.72263342067599 1.3469113111496 1.18630337715149 0.834647238254547 0.471203625202179 0.266845703125 0.155353352427483 1.54915761947632
63 Slovenia 62 5.75799989700317 5.84222516000271 5.67377463400364 1.3412059545517 1.45251882076263 0.790828227996826 0.572575807571411 0.242649093270302 0.0451289787888527 1.31331729888916
64 Peru 63 5.71500015258789 5.81194677859545 5.61805352658033 1.03522527217865 1.21877038478851 0.630166113376617 0.450002878904343 0.126819714903831 0.0470490865409374 2.20726943016052
65 Mauritius 64 5.62900018692017 5.72986219167709 5.52813818216324 1.18939554691315 1.20956099033356 0.638007462024689 0.491247326135635 0.360933750867844 0.0421815551817417 1.6975839138031
66 Cyprus 65 5.62099981307983 5.71469269931316 5.5273069268465 1.35593807697296 1.13136327266693 0.84471470117569 0.355111539363861 0.271254301071167 0.0412379764020443 1.62124919891357
67 Estonia 66 5.61100006103516 5.68813987419009 5.53386024788022 1.32087934017181 1.47667109966278 0.695168316364288 0.479131430387497 0.0988908112049103 0.183248922228813 1.35750865936279
68 Belarus 67 5.56899976730347 5.64611424401402 5.49188529059291 1.15655755996704 1.44494521617889 0.637714266777039 0.295400261878967 0.15513750910759 0.156313821673393 1.72323298454285
69 Libya 68 5.52500009536743 5.67695380687714 5.37304638385773 1.10180306434631 1.35756433010101 0.520169019699097 0.465733230113983 0.152073666453362 0.0926102101802826 1.83501124382019
70 Turkey 69 5.5 5.59486496329308 5.40513503670692 1.19827437400818 1.33775317668915 0.637605607509613 0.300740599632263 0.0466930419206619 0.0996715798974037 1.87927794456482
71 Paraguay 70 5.49300003051758 5.57738126963377 5.40861879140139 0.932537317276001 1.50728487968445 0.579250693321228 0.473507791757584 0.224150657653809 0.091065913438797 1.6853334903717
72 Hong Kong S.A.R., China 71 5.47200012207031 5.54959417313337 5.39440607100725 1.55167484283447 1.26279091835022 0.943062424659729 0.490968644618988 0.374465793371201 0.293933749198914 0.554633140563965
73 Philippines 72 5.42999982833862 5.54533505424857 5.31466460242867 0.85769921541214 1.25391757488251 0.468009054660797 0.585214674472809 0.193513423204422 0.0993318930268288 1.97260475158691
74 Serbia 73 5.39499998092651 5.49156965613365 5.29843030571938 1.06931757926941 1.25818979740143 0.65078467130661 0.208715528249741 0.220125883817673 0.0409037806093693 1.94708442687988
75 Jordan 74 5.33599996566772 5.44841002240777 5.22358990892768 0.991012394428253 1.23908889293671 0.604590058326721 0.418421149253845 0.172170460224152 0.11980327218771 1.79117655754089
76 Hungary 75 5.32399988174438 5.40303970918059 5.24496005430818 1.2860119342804 1.34313309192657 0.687763452529907 0.175863519310951 0.0784016624093056 0.0366369374096394 1.71645927429199
77 Jamaica 76 5.31099987030029 5.58139872848988 5.04060101211071 0.925579309463501 1.36821806430817 0.641022384166718 0.474307239055634 0.233818337321281 0.0552677810192108 1.61232566833496
78 Croatia 77 5.29300022125244 5.39177720457315 5.19422323793173 1.22255623340607 0.96798300743103 0.701288521289825 0.255772292613983 0.248002976179123 0.0431031100451946 1.85449242591858
79 Kosovo 78 5.27899980545044 5.36484799548984 5.19315161541104 0.951484382152557 1.13785350322723 0.541452050209045 0.260287940502167 0.319931447505951 0.0574716180562973 2.01054072380066
80 China 79 5.27299976348877 5.31927808977663 5.2267214372009 1.08116579055786 1.16083741188049 0.741415500640869 0.472787708044052 0.0288068410009146 0.0227942746132612 1.76493859291077
81 Pakistan 80 5.26900005340576 5.35998364135623 5.17801646545529 0.72688353061676 0.672690689563751 0.402047783136368 0.23521526157856 0.315446019172668 0.124348066747189 2.79248929023743
82 Indonesia 81 5.26200008392334 5.35288859814405 5.17111156970263 0.995538592338562 1.27444469928741 0.492345720529556 0.443323463201523 0.611704587936401 0.0153171354904771 1.42947697639465
83 Venezuela 82 5.25 5.3700319455564 5.1299680544436 1.12843120098114 1.43133759498596 0.617144227027893 0.153997123241425 0.0650196298956871 0.0644911229610443 1.78946375846863
84 Montenegro 83 5.23699998855591 5.34104444056749 5.13295553654432 1.12112903594971 1.23837649822235 0.667464673519135 0.194989055395126 0.197911024093628 0.0881741940975189 1.72919154167175
85 Morocco 84 5.2350001335144 5.31834096476436 5.15165930226445 0.878114581108093 0.774864435195923 0.59771066904068 0.408158332109451 0.0322099551558495 0.0877631828188896 2.45618939399719
86 Azerbaijan 85 5.23400020599365 5.29928653523326 5.16871387675405 1.15360176563263 1.15240025520325 0.540775775909424 0.398155838251114 0.0452693402767181 0.180987507104874 1.76248168945312
87 Dominican Republic 86 5.23000001907349 5.34906088516116 5.11093915298581 1.07937383651733 1.40241670608521 0.574873745441437 0.55258983373642 0.186967849731445 0.113945253193378 1.31946516036987
88 Greece 87 5.22700023651123 5.3252461694181 5.12875430360436 1.28948748111725 1.23941457271576 0.810198903083801 0.0957312509417534 0 0.04328977689147 1.74922156333923
89 Lebanon 88 5.22499990463257 5.31888228848577 5.13111752077937 1.07498753070831 1.12962424755096 0.735081076622009 0.288515985012054 0.264450758695602 0.037513829767704 1.69507384300232
90 Portugal 89 5.19500017166138 5.28504173308611 5.10495861023665 1.3151752948761 1.36704301834106 0.795843541622162 0.498465299606323 0.0951027125120163 0.0158694516867399 1.10768270492554
91 Bosnia and Herzegovina 90 5.18200016021729 5.27633568674326 5.08766463369131 0.982409417629242 1.0693359375 0.705186307430267 0.204403176903725 0.328867495059967 0 1.89217257499695
92 Honduras 91 5.18100023269653 5.30158279687166 5.0604176685214 0.730573117733002 1.14394497871399 0.582569479942322 0.348079860210419 0.236188873648643 0.0733454525470734 2.06581115722656
93 Macedonia 92 5.17500019073486 5.27217263966799 5.07782774180174 1.06457793712616 1.20789301395416 0.644948184490204 0.325905978679657 0.25376096367836 0.0602777935564518 1.6174693107605
94 Somalia 93 5.15100002288818 5.24248370990157 5.0595163358748 0.0226431842893362 0.721151351928711 0.113989137113094 0.602126955986023 0.291631311178207 0.282410323619843 3.11748456954956
95 Vietnam 94 5.07399988174438 5.14728076457977 5.000718998909 0.788547575473785 1.27749133110046 0.652168989181519 0.571055591106415 0.234968051314354 0.0876332372426987 1.46231865882874
96 Nigeria 95 5.07399988174438 5.20950013548136 4.93849962800741 0.783756256103516 1.21577048301697 0.0569157302379608 0.394952565431595 0.230947196483612 0.0261215660721064 2.36539053916931
97 Tajikistan 96 5.04099988937378 5.11142559587956 4.970574182868 0.524713635444641 1.27146327495575 0.529235124588013 0.471566706895828 0.248997643589973 0.146377146244049 1.84904932975769
98 Bhutan 97 5.01100015640259 5.07933456212282 4.94266575068235 0.885416388511658 1.34012651443481 0.495879292488098 0.501537680625916 0.474054545164108 0.173380389809608 1.14018440246582
99 Kyrgyzstan 98 5.00400018692017 5.08991990312934 4.91808047071099 0.596220076084137 1.39423859119415 0.553457796573639 0.454943388700485 0.42858037352562 0.0394391790032387 1.53672313690186
100 Nepal 99 4.96199989318848 5.06735607936978 4.85664370700717 0.479820191860199 1.17928326129913 0.504130780696869 0.440305948257446 0.394096165895462 0.0729755461215973 1.8912410736084
101 Mongolia 100 4.95499992370605 5.0216795091331 4.88832033827901 1.02723586559296 1.4930112361908 0.557783484458923 0.394143968820572 0.338464230298996 0.0329022891819477 1.11129236221313
102 South Africa 101 4.8289999961853 4.92943518772721 4.72856480464339 1.05469870567322 1.38478863239288 0.187080070376396 0.479246735572815 0.139362379908562 0.0725094974040985 1.51090860366821
103 Tunisia 102 4.80499982833862 4.88436700701714 4.72563264966011 1.00726580619812 0.868351459503174 0.613212049007416 0.289680689573288 0.0496933571994305 0.0867231488227844 1.89025115966797
104 Palestinian Territories 103 4.77500009536743 4.88184834256768 4.66815184816718 0.716249227523804 1.15564715862274 0.565666973590851 0.25471106171608 0.114173173904419 0.0892826020717621 1.8788902759552
105 Egypt 104 4.7350001335144 4.82513378962874 4.64486647740006 0.989701807498932 0.997471392154694 0.520187258720398 0.282110154628754 0.128631442785263 0.114381365478039 1.70216107368469
106 Bulgaria 105 4.71400022506714 4.80369470641017 4.62430574372411 1.1614590883255 1.43437945842743 0.708217680454254 0.289231717586517 0.113177694380283 0.0110515309497714 0.996139287948608
107 Sierra Leone 106 4.70900011062622 4.85064333498478 4.56735688626766 0.36842092871666 0.984136044979095 0.00556475389748812 0.318697690963745 0.293040901422501 0.0710951760411263 2.66845989227295
108 Cameroon 107 4.69500017166138 4.79654085725546 4.5934594860673 0.564305365085602 0.946018218994141 0.132892116904259 0.430388748645782 0.236298456788063 0.0513066314160824 2.3336455821991
109 Iran 108 4.69199991226196 4.79822470769286 4.58577511683106 1.15687310695648 0.711551249027252 0.639333188533783 0.249322608113289 0.387242913246155 0.048761073499918 1.49873495101929
110 Albania 109 4.64400005340576 4.75246400639415 4.53553610041738 0.996192753314972 0.803685247898102 0.731159746646881 0.381498634815216 0.201312944293022 0.0398642159998417 1.49044156074524
111 Bangladesh 110 4.60799980163574 4.68982165828347 4.52617794498801 0.586682975292206 0.735131740570068 0.533241033554077 0.478356659412384 0.172255352139473 0.123717859387398 1.97873616218567
112 Namibia 111 4.57399988174438 4.77035474091768 4.37764502257109 0.964434325695038 1.0984708070755 0.33861181139946 0.520303547382355 0.0771337449550629 0.0931469723582268 1.4818902015686
113 Kenya 112 4.55299997329712 4.65569159060717 4.45030835598707 0.560479462146759 1.06795072555542 0.309988349676132 0.452763766050339 0.444860309362411 0.0646413192152977 1.6519021987915
114 Mozambique 113 4.55000019073486 4.77410232633352 4.3258980551362 0.234305649995804 0.870701014995575 0.106654435396194 0.480791091918945 0.322228103876114 0.179436385631561 2.35565090179443
115 Myanmar 114 4.54500007629395 4.61473994642496 4.47526020616293 0.367110550403595 1.12323594093323 0.397522568702698 0.514492034912109 0.838075160980225 0.188816204667091 1.11529040336609
116 Senegal 115 4.53499984741211 4.6016037812829 4.46839591354132 0.479309022426605 1.17969191074371 0.409362852573395 0.377922266721725 0.183468893170357 0.115460447967052 1.78964614868164
117 Zambia 116 4.51399993896484 4.64410550147295 4.38389437645674 0.636406779289246 1.00318729877472 0.257835894823074 0.461603492498398 0.249580144882202 0.0782135501503944 1.82670545578003
118 Iraq 117 4.49700021743774 4.62259140968323 4.37140902519226 1.10271048545837 0.978613197803497 0.501180469989777 0.288555532693863 0.19963726401329 0.107215754687786 1.31890726089478
119 Gabon 118 4.46500015258789 4.5573617656529 4.37263853952289 1.1982102394104 1.1556202173233 0.356578588485718 0.312328577041626 0.0437853783369064 0.0760467872023582 1.32291626930237
120 Ethiopia 119 4.46000003814697 4.54272867664695 4.377271399647 0.339233845472336 0.86466920375824 0.353409707546234 0.408842742443085 0.312650740146637 0.165455713868141 2.01574373245239
121 Sri Lanka 120 4.44000005722046 4.55344719231129 4.32655292212963 1.00985014438629 1.25997638702393 0.625130832195282 0.561213254928589 0.490863561630249 0.0736539661884308 0.419389247894287
122 Armenia 121 4.37599992752075 4.46673461228609 4.28526524275541 0.900596737861633 1.00748372077942 0.637524425983429 0.198303267359734 0.0834880918264389 0.0266744215041399 1.5214991569519
123 India 122 4.31500005722046 4.37152201749384 4.25847809694707 0.792221248149872 0.754372596740723 0.455427616834641 0.469987004995346 0.231538489460945 0.0922268852591515 1.5191171169281
124 Mauritania 123 4.29199981689453 4.37716361626983 4.20683601751924 0.648457288742065 1.2720308303833 0.285349279642105 0.0960980430245399 0.201870024204254 0.136957004666328 1.65163731575012
125 Congo (Brazzaville) 124 4.29099988937378 4.41005350500345 4.17194627374411 0.808964252471924 0.832044363021851 0.28995743393898 0.435025870800018 0.120852127671242 0.0796181336045265 1.72413563728333
126 Georgia 125 4.28599977493286 4.37493396580219 4.19706558406353 0.950612664222717 0.57061493396759 0.649546980857849 0.309410035610199 0.0540088154375553 0.251666635274887 1.50013780593872
127 Congo (Kinshasa) 126 4.28000020980835 4.35781083270907 4.20218958690763 0.0921023488044739 1.22902345657349 0.191407024860382 0.235961347818375 0.246455833315849 0.0602413564920425 2.22495865821838
128 Mali 127 4.19000005722046 4.26967071101069 4.11032940343022 0.476180493831635 1.28147339820862 0.169365674257278 0.306613743305206 0.183354198932648 0.104970246553421 1.66819095611572
129 Ivory Coast 128 4.17999982833862 4.27518256321549 4.08481709346175 0.603048920631409 0.904780030250549 0.0486421696841717 0.447706192731857 0.201237469911575 0.130061775445938 1.84496426582336
130 Cambodia 129 4.16800022125244 4.27851781353354 4.05748262897134 0.601765096187592 1.00623834133148 0.429783403873444 0.633375823497772 0.385922968387604 0.0681059509515762 1.04294109344482
131 Sudan 130 4.13899993896484 4.34574716508389 3.9322527128458 0.65951669216156 1.21400856971741 0.290920823812485 0.0149958552792668 0.182317450642586 0.089847519993782 1.68706583976746
132 Ghana 131 4.11999988555908 4.22270720854402 4.01729256257415 0.667224824428558 0.873664736747742 0.295637726783752 0.423026293516159 0.256923943758011 0.0253363698720932 1.57786750793457
133 Ukraine 132 4.09600019454956 4.18541010454297 4.00659028455615 0.89465194940567 1.39453756809235 0.575903952121735 0.122974775731564 0.270061463117599 0.0230294708162546 0.814382314682007
134 Uganda 133 4.08099985122681 4.19579996705055 3.96619973540306 0.381430715322495 1.12982773780823 0.217632606625557 0.443185955286026 0.325766056776047 0.057069718837738 1.526362657547
135 Burkina Faso 134 4.03200006484985 4.12405906438828 3.93994106531143 0.3502277135849 1.04328000545502 0.215844258666039 0.324367851018906 0.250864684581757 0.120328105986118 1.72721290588379
136 Niger 135 4.02799987792969 4.11194681972265 3.94405293613672 0.161925330758095 0.993025004863739 0.26850500702858 0.36365869641304 0.228673845529556 0.138572946190834 1.87398338317871
137 Malawi 136 3.97000002861023 4.07747881740332 3.86252123981714 0.233442038297653 0.512568831443787 0.315089583396912 0.466914653778076 0.287170469760895 0.0727116540074348 2.08178615570068
138 Chad 137 3.93600010871887 4.0347115239501 3.83728869348764 0.438012987375259 0.953855872154236 0.0411347150802612 0.16234202682972 0.216113850474358 0.0535818822681904 2.07123804092407
139 Zimbabwe 138 3.875 3.97869964271784 3.77130035728216 0.375846534967422 1.08309590816498 0.196763753890991 0.336384207010269 0.189143493771553 0.0953753814101219 1.59797024726868
140 Lesotho 139 3.80800008773804 4.04434397548437 3.5716561999917 0.521021246910095 1.19009518623352 0 0.390661299228668 0.157497271895409 0.119094640016556 1.42983531951904
141 Angola 140 3.79500007629395 3.95164193540812 3.63835821717978 0.858428180217743 1.10441195964813 0.0498686656355858 0 0.097926490008831 0.0697203353047371 1.61448240280151
142 Afghanistan 141 3.79399991035461 3.87366141527891 3.71433840543032 0.401477217674255 0.581543326377869 0.180746778845787 0.106179520487785 0.311870932579041 0.0611578300595284 2.15080118179321
143 Botswana 142 3.76600003242493 3.87412266626954 3.65787739858031 1.12209415435791 1.22155499458313 0.341755509376526 0.505196332931519 0.0993484482169151 0.0985831990838051 0.3779137134552
144 Benin 143 3.65700006484985 3.74578355133533 3.56821657836437 0.431085407733917 0.435299843549728 0.209930211305618 0.425962775945663 0.207948461174965 0.0609290152788162 1.88563096523285
145 Madagascar 144 3.64400005340576 3.71431910589337 3.57368100091815 0.305808693170547 0.913020372390747 0.375223308801651 0.189196765422821 0.208732530474663 0.0672319754958153 1.58461260795593
146 Haiti 145 3.6029999256134 3.73471479773521 3.47128505349159 0.368610262870789 0.640449821949005 0.277321130037308 0.0303698573261499 0.489203780889511 0.0998721495270729 1.69716763496399
147 Yemen 146 3.59299993515015 3.69275031983852 3.49324955046177 0.591683447360992 0.93538224697113 0.310080915689468 0.249463722109795 0.104125209152699 0.0567674227058887 1.34560060501099
148 South Sudan 147 3.59100008010864 3.72553858578205 3.45646157443523 0.39724862575531 0.601323127746582 0.163486003875732 0.147062435746193 0.285670816898346 0.116793513298035 1.87956738471985
149 Liberia 148 3.53299999237061 3.65375626087189 3.41224372386932 0.119041793048382 0.872117936611176 0.229918196797371 0.332881182432175 0.26654988527298 0.0389482490718365 1.67328596115112
150 Guinea 149 3.50699996948242 3.58442812889814 3.4295718100667 0.244549930095673 0.791244685649872 0.194129139184952 0.348587512969971 0.264815092086792 0.110937617719173 1.55231189727783
151 Togo 150 3.49499988555908 3.59403811171651 3.39596165940166 0.305444717407227 0.431882530450821 0.247105568647385 0.38042613863945 0.196896150708199 0.0956650152802467 1.83722925186157
152 Rwanda 151 3.47099995613098 3.54303023353219 3.39896967872977 0.368745893239975 0.945707023143768 0.326424807310104 0.581843852996826 0.252756029367447 0.455220013856888 0.540061235427856
153 Syria 152 3.46199989318848 3.66366855680943 3.26033122956753 0.777153134346008 0.396102607250214 0.50053334236145 0.0815394446253777 0.493663728237152 0.151347130537033 1.06157350540161
154 Tanzania 153 3.34899997711182 3.46142975538969 3.23657019883394 0.511135876178741 1.04198980331421 0.364509284496307 0.390017777681351 0.354256361722946 0.0660351067781448 0.621130466461182
155 Burundi 154 2.90499997138977 3.07469033300877 2.73530960977077 0.091622568666935 0.629793584346771 0.151610791683197 0.0599007532000542 0.204435184597969 0.0841479450464249 1.68302416801453
156 Central African Republic 155 2.69300007820129 2.86488426923752 2.52111588716507 0 0 0.0187726859003305 0.270842045545578 0.280876487493515 0.0565650761127472 2.06600475311279

5001
libdataanalysis/8-Kmeans代码实现/data/fashion-mnist-demo.csv
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151
libdataanalysis/8-Kmeans代码实现/data/iris.csv
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sepal_length,sepal_width,petal_length,petal_width,class
5.1,3.5,1.4,0.2,SETOSA
4.9,3.0,1.4,0.2,SETOSA
4.7,3.2,1.3,0.2,SETOSA
4.6,3.1,1.5,0.2,SETOSA
5.0,3.6,1.4,0.2,SETOSA
5.4,3.9,1.7,0.4,SETOSA
4.6,3.4,1.4,0.3,SETOSA
5.0,3.4,1.5,0.2,SETOSA
4.4,2.9,1.4,0.2,SETOSA
4.9,3.1,1.5,0.1,SETOSA
5.4,3.7,1.5,0.2,SETOSA
4.8,3.4,1.6,0.2,SETOSA
4.8,3.0,1.4,0.1,SETOSA
4.3,3.0,1.1,0.1,SETOSA
5.8,4.0,1.2,0.2,SETOSA
5.7,4.4,1.5,0.4,SETOSA
5.4,3.9,1.3,0.4,SETOSA
5.1,3.5,1.4,0.3,SETOSA
5.7,3.8,1.7,0.3,SETOSA
5.1,3.8,1.5,0.3,SETOSA
5.4,3.4,1.7,0.2,SETOSA
5.1,3.7,1.5,0.4,SETOSA
4.6,3.6,1.0,0.2,SETOSA
5.1,3.3,1.7,0.5,SETOSA
4.8,3.4,1.9,0.2,SETOSA
5.0,3.0,1.6,0.2,SETOSA
5.0,3.4,1.6,0.4,SETOSA
5.2,3.5,1.5,0.2,SETOSA
5.2,3.4,1.4,0.2,SETOSA
4.7,3.2,1.6,0.2,SETOSA
4.8,3.1,1.6,0.2,SETOSA
5.4,3.4,1.5,0.4,SETOSA
5.2,4.1,1.5,0.1,SETOSA
5.5,4.2,1.4,0.2,SETOSA
4.9,3.1,1.5,0.1,SETOSA
5.0,3.2,1.2,0.2,SETOSA
5.5,3.5,1.3,0.2,SETOSA
4.9,3.1,1.5,0.1,SETOSA
4.4,3.0,1.3,0.2,SETOSA
5.1,3.4,1.5,0.2,SETOSA
5.0,3.5,1.3,0.3,SETOSA
4.5,2.3,1.3,0.3,SETOSA
4.4,3.2,1.3,0.2,SETOSA
5.0,3.5,1.6,0.6,SETOSA
5.1,3.8,1.9,0.4,SETOSA
4.8,3.0,1.4,0.3,SETOSA
5.1,3.8,1.6,0.2,SETOSA
4.6,3.2,1.4,0.2,SETOSA
5.3,3.7,1.5,0.2,SETOSA
5.0,3.3,1.4,0.2,SETOSA
7.0,3.2,4.7,1.4,VERSICOLOR
6.4,3.2,4.5,1.5,VERSICOLOR
6.9,3.1,4.9,1.5,VERSICOLOR
5.5,2.3,4.0,1.3,VERSICOLOR
6.5,2.8,4.6,1.5,VERSICOLOR
5.7,2.8,4.5,1.3,VERSICOLOR
6.3,3.3,4.7,1.6,VERSICOLOR
4.9,2.4,3.3,1.0,VERSICOLOR
6.6,2.9,4.6,1.3,VERSICOLOR
5.2,2.7,3.9,1.4,VERSICOLOR
5.0,2.0,3.5,1.0,VERSICOLOR
5.9,3.0,4.2,1.5,VERSICOLOR
6.0,2.2,4.0,1.0,VERSICOLOR
6.1,2.9,4.7,1.4,VERSICOLOR
5.6,2.9,3.6,1.3,VERSICOLOR
6.7,3.1,4.4,1.4,VERSICOLOR
5.6,3.0,4.5,1.5,VERSICOLOR
5.8,2.7,4.1,1.0,VERSICOLOR
6.2,2.2,4.5,1.5,VERSICOLOR
5.6,2.5,3.9,1.1,VERSICOLOR
5.9,3.2,4.8,1.8,VERSICOLOR
6.1,2.8,4.0,1.3,VERSICOLOR
6.3,2.5,4.9,1.5,VERSICOLOR
6.1,2.8,4.7,1.2,VERSICOLOR
6.4,2.9,4.3,1.3,VERSICOLOR
6.6,3.0,4.4,1.4,VERSICOLOR
6.8,2.8,4.8,1.4,VERSICOLOR
6.7,3.0,5.0,1.7,VERSICOLOR
6.0,2.9,4.5,1.5,VERSICOLOR
5.7,2.6,3.5,1.0,VERSICOLOR
5.5,2.4,3.8,1.1,VERSICOLOR
5.5,2.4,3.7,1.0,VERSICOLOR
5.8,2.7,3.9,1.2,VERSICOLOR
6.0,2.7,5.1,1.6,VERSICOLOR
5.4,3.0,4.5,1.5,VERSICOLOR
6.0,3.4,4.5,1.6,VERSICOLOR
6.7,3.1,4.7,1.5,VERSICOLOR
6.3,2.3,4.4,1.3,VERSICOLOR
5.6,3.0,4.1,1.3,VERSICOLOR
5.5,2.5,4.0,1.3,VERSICOLOR
5.5,2.6,4.4,1.2,VERSICOLOR
6.1,3.0,4.6,1.4,VERSICOLOR
5.8,2.6,4.0,1.2,VERSICOLOR
5.0,2.3,3.3,1.0,VERSICOLOR
5.6,2.7,4.2,1.3,VERSICOLOR
5.7,3.0,4.2,1.2,VERSICOLOR
5.7,2.9,4.2,1.3,VERSICOLOR
6.2,2.9,4.3,1.3,VERSICOLOR
5.1,2.5,3.0,1.1,VERSICOLOR
5.7,2.8,4.1,1.3,VERSICOLOR
6.3,3.3,6.0,2.5,VIRGINICA
5.8,2.7,5.1,1.9,VIRGINICA
7.1,3.0,5.9,2.1,VIRGINICA
6.3,2.9,5.6,1.8,VIRGINICA
6.5,3.0,5.8,2.2,VIRGINICA
7.6,3.0,6.6,2.1,VIRGINICA
4.9,2.5,4.5,1.7,VIRGINICA
7.3,2.9,6.3,1.8,VIRGINICA
6.7,2.5,5.8,1.8,VIRGINICA
7.2,3.6,6.1,2.5,VIRGINICA
6.5,3.2,5.1,2.0,VIRGINICA
6.4,2.7,5.3,1.9,VIRGINICA
6.8,3.0,5.5,2.1,VIRGINICA
5.7,2.5,5.0,2.0,VIRGINICA
5.8,2.8,5.1,2.4,VIRGINICA
6.4,3.2,5.3,2.3,VIRGINICA
6.5,3.0,5.5,1.8,VIRGINICA
7.7,3.8,6.7,2.2,VIRGINICA
7.7,2.6,6.9,2.3,VIRGINICA
6.0,2.2,5.0,1.5,VIRGINICA
6.9,3.2,5.7,2.3,VIRGINICA
5.6,2.8,4.9,2.0,VIRGINICA
7.7,2.8,6.7,2.0,VIRGINICA
6.3,2.7,4.9,1.8,VIRGINICA
6.7,3.3,5.7,2.1,VIRGINICA
7.2,3.2,6.0,1.8,VIRGINICA
6.2,2.8,4.8,1.8,VIRGINICA
6.1,3.0,4.9,1.8,VIRGINICA
6.4,2.8,5.6,2.1,VIRGINICA
7.2,3.0,5.8,1.6,VIRGINICA
7.4,2.8,6.1,1.9,VIRGINICA
7.9,3.8,6.4,2.0,VIRGINICA
6.4,2.8,5.6,2.2,VIRGINICA
6.3,2.8,5.1,1.5,VIRGINICA
6.1,2.6,5.6,1.4,VIRGINICA
7.7,3.0,6.1,2.3,VIRGINICA
6.3,3.4,5.6,2.4,VIRGINICA
6.4,3.1,5.5,1.8,VIRGINICA
6.0,3.0,4.8,1.8,VIRGINICA
6.9,3.1,5.4,2.1,VIRGINICA
6.7,3.1,5.6,2.4,VIRGINICA
6.9,3.1,5.1,2.3,VIRGINICA
5.8,2.7,5.1,1.9,VIRGINICA
6.8,3.2,5.9,2.3,VIRGINICA
6.7,3.3,5.7,2.5,VIRGINICA
6.7,3.0,5.2,2.3,VIRGINICA
6.3,2.5,5.0,1.9,VIRGINICA
6.5,3.0,5.2,2.0,VIRGINICA
6.2,3.4,5.4,2.3,VIRGINICA
5.9,3.0,5.1,1.8,VIRGINICA
1 sepal_length sepal_width petal_length petal_width class
2 5.1 3.5 1.4 0.2 SETOSA
3 4.9 3.0 1.4 0.2 SETOSA
4 4.7 3.2 1.3 0.2 SETOSA
5 4.6 3.1 1.5 0.2 SETOSA
6 5.0 3.6 1.4 0.2 SETOSA
7 5.4 3.9 1.7 0.4 SETOSA
8 4.6 3.4 1.4 0.3 SETOSA
9 5.0 3.4 1.5 0.2 SETOSA
10 4.4 2.9 1.4 0.2 SETOSA
11 4.9 3.1 1.5 0.1 SETOSA
12 5.4 3.7 1.5 0.2 SETOSA
13 4.8 3.4 1.6 0.2 SETOSA
14 4.8 3.0 1.4 0.1 SETOSA
15 4.3 3.0 1.1 0.1 SETOSA
16 5.8 4.0 1.2 0.2 SETOSA
17 5.7 4.4 1.5 0.4 SETOSA
18 5.4 3.9 1.3 0.4 SETOSA
19 5.1 3.5 1.4 0.3 SETOSA
20 5.7 3.8 1.7 0.3 SETOSA
21 5.1 3.8 1.5 0.3 SETOSA
22 5.4 3.4 1.7 0.2 SETOSA
23 5.1 3.7 1.5 0.4 SETOSA
24 4.6 3.6 1.0 0.2 SETOSA
25 5.1 3.3 1.7 0.5 SETOSA
26 4.8 3.4 1.9 0.2 SETOSA
27 5.0 3.0 1.6 0.2 SETOSA
28 5.0 3.4 1.6 0.4 SETOSA
29 5.2 3.5 1.5 0.2 SETOSA
30 5.2 3.4 1.4 0.2 SETOSA
31 4.7 3.2 1.6 0.2 SETOSA
32 4.8 3.1 1.6 0.2 SETOSA
33 5.4 3.4 1.5 0.4 SETOSA
34 5.2 4.1 1.5 0.1 SETOSA
35 5.5 4.2 1.4 0.2 SETOSA
36 4.9 3.1 1.5 0.1 SETOSA
37 5.0 3.2 1.2 0.2 SETOSA
38 5.5 3.5 1.3 0.2 SETOSA
39 4.9 3.1 1.5 0.1 SETOSA
40 4.4 3.0 1.3 0.2 SETOSA
41 5.1 3.4 1.5 0.2 SETOSA
42 5.0 3.5 1.3 0.3 SETOSA
43 4.5 2.3 1.3 0.3 SETOSA
44 4.4 3.2 1.3 0.2 SETOSA
45 5.0 3.5 1.6 0.6 SETOSA
46 5.1 3.8 1.9 0.4 SETOSA
47 4.8 3.0 1.4 0.3 SETOSA
48 5.1 3.8 1.6 0.2 SETOSA
49 4.6 3.2 1.4 0.2 SETOSA
50 5.3 3.7 1.5 0.2 SETOSA
51 5.0 3.3 1.4 0.2 SETOSA
52 7.0 3.2 4.7 1.4 VERSICOLOR
53 6.4 3.2 4.5 1.5 VERSICOLOR
54 6.9 3.1 4.9 1.5 VERSICOLOR
55 5.5 2.3 4.0 1.3 VERSICOLOR
56 6.5 2.8 4.6 1.5 VERSICOLOR
57 5.7 2.8 4.5 1.3 VERSICOLOR
58 6.3 3.3 4.7 1.6 VERSICOLOR
59 4.9 2.4 3.3 1.0 VERSICOLOR
60 6.6 2.9 4.6 1.3 VERSICOLOR
61 5.2 2.7 3.9 1.4 VERSICOLOR
62 5.0 2.0 3.5 1.0 VERSICOLOR
63 5.9 3.0 4.2 1.5 VERSICOLOR
64 6.0 2.2 4.0 1.0 VERSICOLOR
65 6.1 2.9 4.7 1.4 VERSICOLOR
66 5.6 2.9 3.6 1.3 VERSICOLOR
67 6.7 3.1 4.4 1.4 VERSICOLOR
68 5.6 3.0 4.5 1.5 VERSICOLOR
69 5.8 2.7 4.1 1.0 VERSICOLOR
70 6.2 2.2 4.5 1.5 VERSICOLOR
71 5.6 2.5 3.9 1.1 VERSICOLOR
72 5.9 3.2 4.8 1.8 VERSICOLOR
73 6.1 2.8 4.0 1.3 VERSICOLOR
74 6.3 2.5 4.9 1.5 VERSICOLOR
75 6.1 2.8 4.7 1.2 VERSICOLOR
76 6.4 2.9 4.3 1.3 VERSICOLOR
77 6.6 3.0 4.4 1.4 VERSICOLOR
78 6.8 2.8 4.8 1.4 VERSICOLOR
79 6.7 3.0 5.0 1.7 VERSICOLOR
80 6.0 2.9 4.5 1.5 VERSICOLOR
81 5.7 2.6 3.5 1.0 VERSICOLOR
82 5.5 2.4 3.8 1.1 VERSICOLOR
83 5.5 2.4 3.7 1.0 VERSICOLOR
84 5.8 2.7 3.9 1.2 VERSICOLOR
85 6.0 2.7 5.1 1.6 VERSICOLOR
86 5.4 3.0 4.5 1.5 VERSICOLOR
87 6.0 3.4 4.5 1.6 VERSICOLOR
88 6.7 3.1 4.7 1.5 VERSICOLOR
89 6.3 2.3 4.4 1.3 VERSICOLOR
90 5.6 3.0 4.1 1.3 VERSICOLOR
91 5.5 2.5 4.0 1.3 VERSICOLOR
92 5.5 2.6 4.4 1.2 VERSICOLOR
93 6.1 3.0 4.6 1.4 VERSICOLOR
94 5.8 2.6 4.0 1.2 VERSICOLOR
95 5.0 2.3 3.3 1.0 VERSICOLOR
96 5.6 2.7 4.2 1.3 VERSICOLOR
97 5.7 3.0 4.2 1.2 VERSICOLOR
98 5.7 2.9 4.2 1.3 VERSICOLOR
99 6.2 2.9 4.3 1.3 VERSICOLOR
100 5.1 2.5 3.0 1.1 VERSICOLOR
101 5.7 2.8 4.1 1.3 VERSICOLOR
102 6.3 3.3 6.0 2.5 VIRGINICA
103 5.8 2.7 5.1 1.9 VIRGINICA
104 7.1 3.0 5.9 2.1 VIRGINICA
105 6.3 2.9 5.6 1.8 VIRGINICA
106 6.5 3.0 5.8 2.2 VIRGINICA
107 7.6 3.0 6.6 2.1 VIRGINICA
108 4.9 2.5 4.5 1.7 VIRGINICA
109 7.3 2.9 6.3 1.8 VIRGINICA
110 6.7 2.5 5.8 1.8 VIRGINICA
111 7.2 3.6 6.1 2.5 VIRGINICA
112 6.5 3.2 5.1 2.0 VIRGINICA
113 6.4 2.7 5.3 1.9 VIRGINICA
114 6.8 3.0 5.5 2.1 VIRGINICA
115 5.7 2.5 5.0 2.0 VIRGINICA
116 5.8 2.8 5.1 2.4 VIRGINICA
117 6.4 3.2 5.3 2.3 VIRGINICA
118 6.5 3.0 5.5 1.8 VIRGINICA
119 7.7 3.8 6.7 2.2 VIRGINICA
120 7.7 2.6 6.9 2.3 VIRGINICA
121 6.0 2.2 5.0 1.5 VIRGINICA
122 6.9 3.2 5.7 2.3 VIRGINICA
123 5.6 2.8 4.9 2.0 VIRGINICA
124 7.7 2.8 6.7 2.0 VIRGINICA
125 6.3 2.7 4.9 1.8 VIRGINICA
126 6.7 3.3 5.7 2.1 VIRGINICA
127 7.2 3.2 6.0 1.8 VIRGINICA
128 6.2 2.8 4.8 1.8 VIRGINICA
129 6.1 3.0 4.9 1.8 VIRGINICA
130 6.4 2.8 5.6 2.1 VIRGINICA
131 7.2 3.0 5.8 1.6 VIRGINICA
132 7.4 2.8 6.1 1.9 VIRGINICA
133 7.9 3.8 6.4 2.0 VIRGINICA
134 6.4 2.8 5.6 2.2 VIRGINICA
135 6.3 2.8 5.1 1.5 VIRGINICA
136 6.1 2.6 5.6 1.4 VIRGINICA
137 7.7 3.0 6.1 2.3 VIRGINICA
138 6.3 3.4 5.6 2.4 VIRGINICA
139 6.4 3.1 5.5 1.8 VIRGINICA
140 6.0 3.0 4.8 1.8 VIRGINICA
141 6.9 3.1 5.4 2.1 VIRGINICA
142 6.7 3.1 5.6 2.4 VIRGINICA
143 6.9 3.1 5.1 2.3 VIRGINICA
144 5.8 2.7 5.1 1.9 VIRGINICA
145 6.8 3.2 5.9 2.3 VIRGINICA
146 6.7 3.3 5.7 2.5 VIRGINICA
147 6.7 3.0 5.2 2.3 VIRGINICA
148 6.3 2.5 5.0 1.9 VIRGINICA
149 6.5 3.0 5.2 2.0 VIRGINICA
150 6.2 3.4 5.4 2.3 VIRGINICA
151 5.9 3.0 5.1 1.8 VIRGINICA

119
libdataanalysis/8-Kmeans代码实现/data/microchips-tests.csv
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@ -1,119 +0,0 @@
param_1,param_2,validity
0.051267,0.69956,1
-0.092742,0.68494,1
-0.21371,0.69225,1
-0.375,0.50219,1
-0.51325,0.46564,1
-0.52477,0.2098,1
-0.39804,0.034357,1
-0.30588,-0.19225,1
0.016705,-0.40424,1
0.13191,-0.51389,1
0.38537,-0.56506,1
0.52938,-0.5212,1
0.63882,-0.24342,1
0.73675,-0.18494,1
0.54666,0.48757,1
0.322,0.5826,1
0.16647,0.53874,1
-0.046659,0.81652,1
-0.17339,0.69956,1
-0.47869,0.63377,1
-0.60541,0.59722,1
-0.62846,0.33406,1
-0.59389,0.005117,1
-0.42108,-0.27266,1
-0.11578,-0.39693,1
0.20104,-0.60161,1
0.46601,-0.53582,1
0.67339,-0.53582,1
-0.13882,0.54605,1
-0.29435,0.77997,1
-0.26555,0.96272,1
-0.16187,0.8019,1
-0.17339,0.64839,1
-0.28283,0.47295,1
-0.36348,0.31213,1
-0.30012,0.027047,1
-0.23675,-0.21418,1
-0.06394,-0.18494,1
0.062788,-0.16301,1
0.22984,-0.41155,1
0.2932,-0.2288,1
0.48329,-0.18494,1
0.64459,-0.14108,1
0.46025,0.012427,1
0.6273,0.15863,1
0.57546,0.26827,1
0.72523,0.44371,1
0.22408,0.52412,1
0.44297,0.67032,1
0.322,0.69225,1
0.13767,0.57529,1
-0.0063364,0.39985,1
-0.092742,0.55336,1
-0.20795,0.35599,1
-0.20795,0.17325,1
-0.43836,0.21711,1
-0.21947,-0.016813,1
-0.13882,-0.27266,1
0.18376,0.93348,0
0.22408,0.77997,0
0.29896,0.61915,0
0.50634,0.75804,0
0.61578,0.7288,0
0.60426,0.59722,0
0.76555,0.50219,0
0.92684,0.3633,0
0.82316,0.27558,0
0.96141,0.085526,0
0.93836,0.012427,0
0.86348,-0.082602,0
0.89804,-0.20687,0
0.85196,-0.36769,0
0.82892,-0.5212,0
0.79435,-0.55775,0
0.59274,-0.7405,0
0.51786,-0.5943,0
0.46601,-0.41886,0
0.35081,-0.57968,0
0.28744,-0.76974,0
0.085829,-0.75512,0
0.14919,-0.57968,0
-0.13306,-0.4481,0
-0.40956,-0.41155,0
-0.39228,-0.25804,0
-0.74366,-0.25804,0
-0.69758,0.041667,0
-0.75518,0.2902,0
-0.69758,0.68494,0
-0.4038,0.70687,0
-0.38076,0.91886,0
-0.50749,0.90424,0
-0.54781,0.70687,0
0.10311,0.77997,0
0.057028,0.91886,0
-0.10426,0.99196,0
-0.081221,1.1089,0
0.28744,1.087,0
0.39689,0.82383,0
0.63882,0.88962,0
0.82316,0.66301,0
0.67339,0.64108,0
1.0709,0.10015,0
-0.046659,-0.57968,0
-0.23675,-0.63816,0
-0.15035,-0.36769,0
-0.49021,-0.3019,0
-0.46717,-0.13377,0
-0.28859,-0.060673,0
-0.61118,-0.067982,0
-0.66302,-0.21418,0
-0.59965,-0.41886,0
-0.72638,-0.082602,0
-0.83007,0.31213,0
-0.72062,0.53874,0
-0.59389,0.49488,0
-0.48445,0.99927,0
-0.0063364,0.99927,0
0.63265,-0.030612,0
1 param_1 param_2 validity
2 0.051267 0.69956 1
3 -0.092742 0.68494 1
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14.5087472134072,15.46051652161006,0
15.69042695638351,16.48168851978138,0
12.95598191982515,12.43703005897334,0
13.59312604041728,14.84189902611636,0
15.12874638631439,17.14981222613881,0
14.26705036670259,15.67551973639503,0
15.6614505451442,14.81146451457414,0
14.33962672797097,15.49202297710026,0
14.2761765458781,14.70590693250814,0
14.86049072335336,15.59000779027686,0
14.10414479623351,15.1805045637764,0
15.98828286381979,15.62105187028486,0
13.47473582792461,15.59307141917535,0
13.77637601475249,14.99194426684731,0
12.82770875129005,15.67136906874635,0
13.67165486007913,15.11954159126301,0
15.38704283906103,15.56936935237784,0
15.54320933642332,15.51543150058866,0
13.85306094119846,15.60672436869602,0
13.62525245784644,14.45209462876985,0
15.0157784412311,14.91664093008973,0
13.83645753449745,15.24940725360926,0
14.22694438547307,14.3479843622948,0
13.23742625416296,14.61058751286003,0
13.38482919115422,14.7331933025011,0
13.87130103241151,14.97399468636979,0
12.39445846815594,14.64448216946588,0
14.32186557845068,14.52890629439163,0
15.82965092460402,15.71619455432355,0
15.80177302202355,16.01808914480403,0
14.69751200330076,14.11198748714029,0
14.70598656653535,16.46040295414171,0
13.59156859810395,14.91975097196414,0
12.29984538869378,14.77119467910275,0
13.3990474777037,16.11912910518291,0
15.13112869806696,15.90031130320181,0
15.38581197702793,15.71453967469415,0
15.45487421920634,15.4404224240544,0
13.74951530855867,15.26803135994583,0
15.69914333094722,16.05595814533895,0
14.80580490719942,14.33258926354469,0
15.17222942648117,16.70624397729834,0
11.24915511828765,15.13295896107001,0
13.88773906521638,14.48548132472444,0
15.3258701791002,16.58524064023295,0
12.97517063349011,15.1605677140184,0
14.07427780835002,17.21973519125371,0
14.1820256369139,17.83351945487566,0
12.23970014041095,14.72866833837743,0
14.82555960703615,15.94500684833057,0
13.09763368416417,16.23036500469445,0
13.85758877756093,15.03526838191721,0
15.52502523459987,16.78653607805479,0
15.31499528329094,14.56835427536349,0
14.03034873517879,15.6633618769716,0
14.42312994571211,14.94109334872472,0
13.63615118835241,14.96411634434718,0
14.53477942776931,13.35611764012331,0
14.61566223678644,14.15241034694619,0
13.08085544352481,14.0284594118694,0
14.93928677902786,14.54933745884242,0
16.0271266262212,15.70965830468461,0
14.31925037139242,15.11762658185582,0
14.86153307492049,14.28458412390706,0
14.01432032507764,16.77971266133154,0
13.40765469906171,14.60041190939531,0
13.0795973186072,14.19389917316378,0
12.68820688788819,13.81109597020173,0
14.19232756586644,15.36498178724437,0
14.86589365075524,14.47138789706538,0
13.39350297747264,14.34389892642248,0
13.58659142682796,14.39148496395445,0
13.10219289551651,14.3760326021477,0
14.54176555566262,16.37233995317341,0
14.25602703003231,15.0423494965284,0
16.18754760471493,16.36145253974863,0
13.63292362573135,13.62886893815872,0
14.65349334618363,14.97649220824924,0
12.61911799757794,16.77214314245786,0
13.03427729514449,14.25689090988086,0
10.85940051666349,14.47914434225415,0
12.93486070587027,14.60746677979927,0
13.9922676551586,14.96212808248882,0
12.57248704338531,15.1972734968139,0
15.68266703007037,16.22123922102406,0
13.2125815156299,14.3518273677709,0
13.98975002194823,14.52445650352669,0
13.4662664096024,13.65765529406475,0
13.13166385488746,15.79882584075226,0
14.35439254719252,15.02329268379058,0
13.55329410888779,13.73218768633878,0
12.98628429130503,14.80983707085099,0
14.37264883162727,14.95148191190331,0
13.58869050224715,15.19778174710474,0
12.26002251889708,15.61364103922988,0
13.66602493759934,16.44517365387813,0
14.34554567080519,15.44883765222099,0
14.60667497581217,15.77655361118647,0
14.15369523977195,16.57440586446113,0
14.04899502017924,14.39078838248393,0
14.06857464220482,14.62364257375797,0
15.88890082127304,16.33705609429303,0
13.97601419894874,15.84206442894244,0
10.88221341356124,13.46166188373757,0
13.90920312008345,14.97657577218348,0
12.36776146202978,15.14204982137499,0
15.16765639256333,15.51933856946829,0
15.3376951724287,14.23319145087297,0
13.55057689653119,15.73044061233337,0
13.57918656724497,15.47264441338775,0
14.24479089854792,15.0850911865811,0
15.33086296717245,15.71142599198902,0
15.91714892779239,15.15651432878437,0
13.85421253890297,15.32125758133508,0
14.08736591098981,14.30728373787297,0
12.63610997338858,15.65066101888946,0
14.36282756033598,13.87195409310256,0
14.50066606012271,14.61759024545319,0
13.96984547008964,16.17341605305203,0
15.13133128099397,15.28924849061305,0
15.15300231315136,14.01362830007739,0
13.31011939341444,14.39060274697614,0
14.25712172586539,14.29705004451436,0
13.71613134707139,13.52733470384027,0
15.70094057818437,15.99611428697285,0
13.38943515399727,14.36513422537798,0
14.14088666467278,13.97440554314796,0
14.84487049785213,14.01695105963744,0
12.70489590338878,14.27293037161499,0
14.95353525235777,14.73218902472499,0
14.28114117782965,14.61262377516035,0
13.06799073973982,14.83286345035982,0
13.60279699846308,12.20295198971654,0
12.68816488185228,15.81141680713469,0
13.88291727981215,14.11808370066965,0
14.016482216113,14.33509982485053,0
15.36576550135049,15.82610475260424,0
13.57764756126836,14.88045533202498,0
13.3918924208501,14.34497756139911,0
13.69362090262048,15.92189939882443,0
12.87853442397187,13.20174479842375,0
13.69916365173765,15.41800069841461,0
14.01609081001448,15.82165925226776,0
14.5899650464961,16.38090675134464,0
15.00784342040606,15.50954333819685,0
14.05950746445452,13.75788684204651,0
14.46114683681014,13.34425721343066,0
14.64474777063343,15.03905866347516,0
13.85478898285457,15.86614260965412,0
14.2814175097121,14.02340696081207,0
14.93304554162803,14.32639552072927,0
13.7693080678919,16.51310530416839,0
13.44404345182867,15.07922662749323,0
14.0317928593353,14.40986664465888,0
13.81946840229293,15.58676798397279,0
16.50656640573653,15.22029747467542,0
12.20423230665472,14.32106064914233,0
14.8819298948981,16.36162230554352,0
15.16030999546341,15.14972042192441,0
11.78759609450762,14.55034168613148,0
12.88388298331717,14.57250347912669,0
13.62023705917705,16.42369250161395,0
14.53049363223479,15.44664319460541,0
12.64616608049998,15.10838775257841,0
15.54763373107359,16.43238820991158,0
14.4007699774828,15.21258204276164,0
15.21058389990948,14.93547994178749,0
15.06173440367518,15.11740665636805,0
14.86214589875373,14.70177771082854,0
15.40451989437227,15.34490711864667,0
13.79430574831448,14.68727111247282,0
14.63390271757003,16.30082803685785,0
12.45687580804446,15.54617986485219,0
13.99759772841731,16.73594542008409,0
12.93253733568772,12.62389976814524,0
13.70345190616539,14.71480993356161,0
13.12395594125503,15.44848980937747,0
13.81691009423219,14.09233539217894,0
13.02489337092878,14.25050251544228,0
14.53425534561566,15.76596516545384,0
13.25186260458783,16.3225231885698,0
13.23657554891477,15.33696609589177,0
12.1297131595538,12.66688846478064,0
14.3808873556303,16.03087164666765,0
15.98239721601976,15.52399453253037,0
13.75107909980303,13.64320737566979,0
13.35730012174231,13.42431786138274,0
13.08559089708043,14.86775905977197,0
13.6117330216296,14.86806413838196,0
15.1776173709485,14.15354188009321,0
14.15456588767872,15.28746897631645,0
13.22531906267953,13.9598546965538,0
13.94151500958564,14.76023193066396,0
15.39066478902675,15.71412823472551,0
13.17642606705518,13.67395694240669,0
13.38689005901117,14.66536821990745,0
15.15888821036137,14.78211270885843,0
14.55599224830758,14.04946255637684,0
14.62692885570043,14.29592015439668,0
13.28624407169681,15.6581260669439,0
13.8154823515179,14.1716943145893,0
14.3109896419094,16.25419059506493,0
13.53597112272297,15.77020127180871,0
14.80103055297733,13.81813140471321,0
13.77274485542839,14.64955360893938,0
13.76510156692244,15.02311286948475,0
14.05349835921094,13.93946896423697,0
15.30905390162218,16.04190604522437,0
13.15523771144825,16.9212211680188,0
12.69940390796505,13.99916733869651,0
14.3679922537568,16.75782353966251,0
13.2632541853177,14.09898705600851,0
11.91253508924009,14.61325734486844,0
13.37000592461161,15.18268143261131,0
15.99450697482097,15.4532938283601,0
14.15764860588238,13.77083846575649,0
14.96982662482653,15.59222552688896,0
14.75068711060737,15.46889187883478,0
13.33027919659259,14.34699591207669,0
13.05002153442813,14.68726188711367,0
13.77642646984253,14.23618563920568,0
15.17426585206286,15.5095749119089,0
14.21251759323552,15.08270517066944,0
13.82089482923982,15.61146315929325,0
14.12355955034152,14.95509753853501,0
14.54752171050364,14.85861945287413,0
14.09944359402792,16.03131199865159,0
14.57730180008498,14.25667659137451,0
14.52331832390665,14.2300499886642,0
14.30044704017983,15.26643299159799,0
14.55839285912062,15.48691913661183,0
14.22494186934392,15.86117827216267,0
12.04029344338111,13.34483350304919,0
13.07931049306772,9.347878119065356,1
21.7271340215587,4.126232224310076,1
12.4766288158932,14.4593696654036,1
19.5825727723877,10.4116189967773,1
23.33986752737173,16.29887355272053,1
18.2611884383863,17.9783089957873,1
4.752612823293772,24.35040724802435,1
1 Latency (ms) Throughput (mb/s) Anomaly
2 13.04681516870484 14.7411524132184 0
3 13.4085201853932 13.76326960024047 0
4 14.19591481245491 15.85318112982812 0
5 14.91470076531303 16.17425986715807 0
6 13.5766996051752 14.04284943755652 0
7 13.92240250750028 13.40646893666083 0
8 12.82213163903098 14.22318782380161 0
9 15.6763661470048 15.89169137219994 0
10 16.16287532482238 16.20299807446642 0
11 12.66645094909174 14.8990837351338 1
12 13.98454962300191 12.95800821585463 0
13 14.06146043109355 14.54908874282629 0
14 13.38988671215899 15.56202141787754 0
15 13.39350474623341 15.62698794188875 0
16 13.97900926099814 13.28061494266342 0
17 14.16791258723419 14.46583828507579 0
18 13.96176145283657 14.75182421254904 0
19 14.45899735355037 15.07018562997125 0
20 14.58476371878708 15.82743423785702 0
21 12.07427073619131 13.06711089796514 0
22 13.54912940444922 15.53827676982062 0
23 13.98625041879221 14.78776303583677 0
24 14.96991942049244 16.51830493015889 0
25 14.2557659665841 15.29427277420701 0
26 15.33425000108006 16.12469988952639 0
27 15.63504869777692 16.49094476663806 0
28 13.62081291712303 15.45947525058772 0
29 14.81548484709227 15.33956526603583 0
30 14.59318972857327 14.61238105671215 0
31 14.48906754712418 15.64087368177291 0
32 15.52704801171451 14.63568031226173 0
33 13.97506707358789 14.76531532927648 0
34 12.95364954381841 14.82328512087584 0
35 12.88787444214799 15.07607810133002 0
36 16.02178960565569 16.25746991816081 0
37 14.9262927071427 16.29725072434191 0
38 12.46559400363085 14.18321211753596 0
39 14.08466278107714 14.44192203204038 0
40 14.53717522545769 14.24224248113181 0
41 14.22250851601845 15.42386187610343 0
42 14.51908495978717 13.99871698993444 0
43 13.11971433616167 14.66081845898369 0
44 14.5108889424642 15.30465148682366 0
45 14.18262426407451 15.3938896849634 0
46 14.71651844926282 15.73369667477785 0
47 13.83454699853918 16.17138034441191 0
48 16.00076179182642 14.69232970320203 0
49 14.12702715242892 15.91462774747984 0
50 13.84578546855034 14.34139348861173 0
51 15.41426110064101 16.24243182463628 1
52 13.25273726696165 15.00861363933526 0
53 13.66840226015763 14.35886035673854 0
54 13.77534773921765 14.73808512203812 0
55 14.12582342640922 14.92980922624493 0
56 14.54724604324321 15.6333944514067 0
57 14.15258077112493 14.53622696521789 0
58 14.12648161131633 15.34467591276852 0
59 14.26324658304056 14.98556918087115 0
60 14.77324331862399 15.25299473774317 0
61 14.20969933686442 16.14572569071713 0
62 13.260655152992 15.48016214411599 0
63 14.25273350867239 15.03134360663839 0
64 12.92124446791387 13.19321540142361 0
65 13.852431292546 13.33213110580615 0
66 13.96856800302965 13.19821236714215 0
67 13.25206981975186 15.36846390294601 0
68 13.70449633962696 13.21431301976872 0
69 14.5087472134072 15.46051652161006 0
70 15.69042695638351 16.48168851978138 0
71 12.95598191982515 12.43703005897334 0
72 13.59312604041728 14.84189902611636 0
73 15.12874638631439 17.14981222613881 0
74 14.26705036670259 15.67551973639503 0
75 15.6614505451442 14.81146451457414 0
76 14.33962672797097 15.49202297710026 0
77 14.2761765458781 14.70590693250814 0
78 14.86049072335336 15.59000779027686 0
79 14.10414479623351 15.1805045637764 0
80 15.98828286381979 15.62105187028486 0
81 13.47473582792461 15.59307141917535 0
82 13.77637601475249 14.99194426684731 0
83 12.82770875129005 15.67136906874635 0
84 13.67165486007913 15.11954159126301 0
85 15.38704283906103 15.56936935237784 0
86 15.54320933642332 15.51543150058866 0
87 13.85306094119846 15.60672436869602 0
88 13.62525245784644 14.45209462876985 0
89 15.0157784412311 14.91664093008973 0
90 13.83645753449745 15.24940725360926 0
91 14.22694438547307 14.3479843622948 0
92 13.23742625416296 14.61058751286003 0
93 13.38482919115422 14.7331933025011 0
94 13.87130103241151 14.97399468636979 0
95 12.39445846815594 14.64448216946588 0
96 14.32186557845068 14.52890629439163 0
97 15.82965092460402 15.71619455432355 0
98 15.80177302202355 16.01808914480403 0
99 14.69751200330076 14.11198748714029 0
100 14.70598656653535 16.46040295414171 0
101 13.59156859810395 14.91975097196414 0
102 12.29984538869378 14.77119467910275 0
103 13.3990474777037 16.11912910518291 0
104 15.13112869806696 15.90031130320181 0
105 15.38581197702793 15.71453967469415 0
106 15.45487421920634 15.4404224240544 0
107 13.74951530855867 15.26803135994583 0
108 15.69914333094722 16.05595814533895 0
109 14.80580490719942 14.33258926354469 0
110 15.17222942648117 16.70624397729834 0
111 11.24915511828765 15.13295896107001 0
112 13.88773906521638 14.48548132472444 0
113 15.3258701791002 16.58524064023295 0
114 12.97517063349011 15.1605677140184 0
115 14.07427780835002 17.21973519125371 0
116 14.1820256369139 17.83351945487566 0
117 12.23970014041095 14.72866833837743 0
118 14.82555960703615 15.94500684833057 0
119 13.09763368416417 16.23036500469445 0
120 13.85758877756093 15.03526838191721 0
121 15.52502523459987 16.78653607805479 0
122 15.31499528329094 14.56835427536349 0
123 14.03034873517879 15.6633618769716 0
124 14.42312994571211 14.94109334872472 0
125 13.63615118835241 14.96411634434718 0
126 14.53477942776931 13.35611764012331 0
127 14.61566223678644 14.15241034694619 0
128 13.08085544352481 14.0284594118694 0
129 14.93928677902786 14.54933745884242 0
130 16.0271266262212 15.70965830468461 0
131 14.31925037139242 15.11762658185582 0
132 14.86153307492049 14.28458412390706 0
133 14.01432032507764 16.77971266133154 0
134 13.40765469906171 14.60041190939531 0
135 13.0795973186072 14.19389917316378 0
136 12.68820688788819 13.81109597020173 0
137 14.19232756586644 15.36498178724437 0
138 14.86589365075524 14.47138789706538 0
139 13.39350297747264 14.34389892642248 0
140 13.58659142682796 14.39148496395445 0
141 13.10219289551651 14.3760326021477 0
142 14.54176555566262 16.37233995317341 0
143 14.25602703003231 15.0423494965284 0
144 16.18754760471493 16.36145253974863 0
145 13.63292362573135 13.62886893815872 0
146 14.65349334618363 14.97649220824924 0
147 12.61911799757794 16.77214314245786 0
148 13.03427729514449 14.25689090988086 0
149 10.85940051666349 14.47914434225415 0
150 12.93486070587027 14.60746677979927 0
151 13.9922676551586 14.96212808248882 0
152 12.57248704338531 15.1972734968139 0
153 15.68266703007037 16.22123922102406 0
154 13.2125815156299 14.3518273677709 0
155 13.98975002194823 14.52445650352669 0
156 13.4662664096024 13.65765529406475 0
157 13.13166385488746 15.79882584075226 0
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308 4.752612823293772 24.35040724802435 1

156
libdataanalysis/8-Kmeans代码实现/data/world-happiness-report-2017.csv
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@ -1,156 +0,0 @@
"Country","Happiness.Rank","Happiness.Score","Whisker.high","Whisker.low","Economy..GDP.per.Capita.","Family","Health..Life.Expectancy.","Freedom","Generosity","Trust..Government.Corruption.","Dystopia.Residual"
"Norway",1,7.53700017929077,7.59444482058287,7.47955553799868,1.61646318435669,1.53352355957031,0.796666502952576,0.635422587394714,0.36201223731041,0.315963834524155,2.27702665328979
"Denmark",2,7.52199983596802,7.58172806486487,7.46227160707116,1.48238301277161,1.55112159252167,0.792565524578094,0.626006722450256,0.355280488729477,0.40077006816864,2.31370735168457
"Iceland",3,7.50400018692017,7.62203047305346,7.38596990078688,1.480633020401,1.6105740070343,0.833552122116089,0.627162635326385,0.475540220737457,0.153526559472084,2.32271528244019
"Switzerland",4,7.49399995803833,7.56177242040634,7.42622749567032,1.56497955322266,1.51691174507141,0.858131289482117,0.620070576667786,0.290549278259277,0.367007285356522,2.2767162322998
"Finland",5,7.4689998626709,7.52754207581282,7.41045764952898,1.44357192516327,1.5402467250824,0.80915766954422,0.617950856685638,0.24548277258873,0.38261154294014,2.4301815032959
"Netherlands",6,7.3769998550415,7.42742584124207,7.32657386884093,1.50394463539124,1.42893922328949,0.810696125030518,0.585384488105774,0.470489829778671,0.282661825418472,2.29480409622192
"Canada",7,7.31599998474121,7.38440283536911,7.24759713411331,1.47920441627502,1.48134899139404,0.83455765247345,0.611100912094116,0.435539722442627,0.287371516227722,2.18726444244385
"New Zealand",8,7.31400012969971,7.3795104418695,7.24848981752992,1.40570604801178,1.54819512367249,0.816759705543518,0.614062130451202,0.500005125999451,0.382816702127457,2.0464563369751
"Sweden",9,7.28399991989136,7.34409487739205,7.22390496239066,1.49438726902008,1.47816216945648,0.830875158309937,0.612924098968506,0.385399252176285,0.384398728609085,2.09753799438477
"Australia",10,7.28399991989136,7.35665122494102,7.2113486148417,1.484414935112,1.51004195213318,0.84388679265976,0.601607382297516,0.477699249982834,0.301183730363846,2.06521081924438
"Israel",11,7.21299982070923,7.27985325649381,7.14614638492465,1.37538242340088,1.37628996372223,0.83840399980545,0.405988603830338,0.330082654953003,0.0852421000599861,2.80175733566284
"Costa Rica",12,7.0789999961853,7.16811166629195,6.98988832607865,1.10970628261566,1.41640365123749,0.759509265422821,0.580131649971008,0.214613229036331,0.100106589496136,2.89863920211792
"Austria",13,7.00600004196167,7.07066981211305,6.94133027181029,1.48709726333618,1.4599449634552,0.815328419208527,0.567766189575195,0.316472321748734,0.221060365438461,2.1385064125061
"United States",14,6.99300003051758,7.07465674757957,6.91134331345558,1.54625928401947,1.41992056369781,0.77428662776947,0.505740523338318,0.392578780651093,0.135638788342476,2.2181134223938
"Ireland",15,6.97700023651123,7.04335166752338,6.91064880549908,1.53570663928986,1.55823111534119,0.80978262424469,0.573110342025757,0.42785832285881,0.29838815331459,1.77386903762817
"Germany",16,6.95100021362305,7.00538156926632,6.89661885797977,1.48792338371277,1.47252035140991,0.798950731754303,0.562511384487152,0.336269170045853,0.276731938123703,2.01576995849609
"Belgium",17,6.89099979400635,6.95582075044513,6.82617883756757,1.46378076076508,1.46231269836426,0.818091869354248,0.539770722389221,0.231503337621689,0.251343131065369,2.12421035766602
"Luxembourg",18,6.86299991607666,6.92368609987199,6.80231373228133,1.74194359779358,1.45758366584778,0.845089495182037,0.59662789106369,0.283180981874466,0.31883442401886,1.61951208114624
"United Kingdom",19,6.71400022506714,6.78379176110029,6.64420868903399,1.44163393974304,1.49646008014679,0.805335938930511,0.508190035820007,0.492774158716202,0.265428066253662,1.70414352416992
"Chile",20,6.65199995040894,6.73925056010485,6.56474934071302,1.25278460979462,1.28402495384216,0.819479703903198,0.376895278692245,0.326662421226501,0.0822879821062088,2.50958585739136
"United Arab Emirates",21,6.64799976348877,6.72204730376601,6.57395222321153,1.62634336948395,1.26641023159027,0.726798236370087,0.60834527015686,0.3609419465065,0.324489563703537,1.734703540802
"Brazil",22,6.63500022888184,6.72546950161457,6.5445309561491,1.10735321044922,1.43130600452423,0.616552352905273,0.437453746795654,0.16234989464283,0.111092761158943,2.76926708221436
"Czech Republic",23,6.60900020599365,6.68386246263981,6.5341379493475,1.35268235206604,1.43388521671295,0.754444003105164,0.490946173667908,0.0881067588925362,0.0368729270994663,2.45186185836792
"Argentina",24,6.59899997711182,6.69008508607745,6.50791486814618,1.18529546260834,1.44045114517212,0.695137083530426,0.494519203901291,0.109457060694695,0.059739887714386,2.61400532722473
"Mexico",25,6.57800006866455,6.67114890769124,6.48485122963786,1.15318381786346,1.210862159729,0.709978997707367,0.412730008363724,0.120990432798862,0.132774114608765,2.83715486526489
"Singapore",26,6.57200002670288,6.63672306910157,6.50727698430419,1.69227766990662,1.35381436347961,0.949492394924164,0.549840569496155,0.345965981483459,0.46430778503418,1.21636199951172
"Malta",27,6.52699995040894,6.59839677289128,6.45560312792659,1.34327983856201,1.48841166496277,0.821944236755371,0.588767051696777,0.574730575084686,0.153066068887711,1.55686283111572
"Uruguay",28,6.4539999961853,6.54590621769428,6.36209377467632,1.21755969524384,1.41222786903381,0.719216823577881,0.57939225435257,0.175096929073334,0.178061872720718,2.17240953445435
"Guatemala",29,6.4539999961853,6.56687397271395,6.34112601965666,0.872001945972443,1.25558519363403,0.540239989757538,0.531310617923737,0.283488392829895,0.0772232785820961,2.89389109611511
"Panama",30,6.4520001411438,6.55713071614504,6.34686956614256,1.23374843597412,1.37319254875183,0.706156134605408,0.550026834011078,0.21055693924427,0.070983923971653,2.30719995498657
"France",31,6.44199991226196,6.51576780244708,6.36823202207685,1.43092346191406,1.38777685165405,0.844465851783752,0.470222115516663,0.129762306809425,0.172502428293228,2.00595474243164
"Thailand",32,6.42399978637695,6.50911685571074,6.33888271704316,1.12786877155304,1.42579245567322,0.647239029407501,0.580200731754303,0.572123110294342,0.0316127352416515,2.03950834274292
"Taiwan Province of China",33,6.42199993133545,6.49459602192044,6.34940384075046,1.43362653255463,1.38456535339355,0.793984234333038,0.361466586589813,0.258360475301743,0.0638292357325554,2.1266074180603
"Spain",34,6.40299987792969,6.4710548453033,6.33494491055608,1.38439786434174,1.53209090232849,0.888960599899292,0.408781230449677,0.190133571624756,0.0709140971302986,1.92775774002075
"Qatar",35,6.375,6.56847681432962,6.18152318567038,1.87076568603516,1.27429687976837,0.710098087787628,0.604130983352661,0.330473870038986,0.439299255609512,1.1454644203186
"Colombia",36,6.35699987411499,6.45202005416155,6.26197969406843,1.07062232494354,1.4021829366684,0.595027923583984,0.477487415075302,0.149014472961426,0.0466687418520451,2.61606812477112
"Saudi Arabia",37,6.3439998626709,6.44416661202908,6.24383311331272,1.53062355518341,1.28667759895325,0.590148329734802,0.449750572443008,0.147616013884544,0.27343225479126,2.0654296875
"Trinidad and Tobago",38,6.16800022125244,6.38153389066458,5.95446655184031,1.36135590076447,1.3802285194397,0.519983291625977,0.518630743026733,0.325296461582184,0.00896481610834599,2.05324745178223
"Kuwait",39,6.10500001907349,6.1919569888711,6.01804304927588,1.63295245170593,1.25969874858856,0.632105708122253,0.496337592601776,0.228289797902107,0.215159550309181,1.64042520523071
"Slovakia",40,6.09800004959106,6.1773484121263,6.01865168705583,1.32539355754852,1.50505924224854,0.712732911109924,0.295817464590073,0.136544480919838,0.0242108516395092,2.09777665138245
"Bahrain",41,6.08699989318848,6.17898906782269,5.99501071855426,1.48841226100922,1.32311046123505,0.653133034706116,0.536746919155121,0.172668486833572,0.257042169570923,1.65614938735962
"Malaysia",42,6.08400011062622,6.17997963652015,5.98802058473229,1.29121541976929,1.28464603424072,0.618784427642822,0.402264982461929,0.416608929634094,0.0656007081270218,2.00444889068604
"Nicaragua",43,6.07100009918213,6.18658360034227,5.95541659802198,0.737299203872681,1.28721570968628,0.653095960617065,0.447551846504211,0.301674216985703,0.130687981843948,2.51393055915833
"Ecuador",44,6.00799989700317,6.10584767535329,5.91015211865306,1.00082039833069,1.28616881370544,0.685636222362518,0.4551981985569,0.150112465023994,0.140134647488594,2.29035258293152
"El Salvador",45,6.00299978256226,6.108635122329,5.89736444279552,0.909784495830536,1.18212509155273,0.596018552780151,0.432452529668808,0.0782579854130745,0.0899809598922729,2.7145938873291
"Poland",46,5.97300004959106,6.05390834122896,5.89209175795317,1.29178786277771,1.44571197032928,0.699475347995758,0.520342111587524,0.158465966582298,0.0593078061938286,1.79772281646729
"Uzbekistan",47,5.97100019454956,6.06553757295012,5.876462816149,0.786441087722778,1.54896914958954,0.498272627592087,0.658248662948608,0.415983647108078,0.246528223156929,1.81691360473633
"Italy",48,5.96400022506714,6.04273690596223,5.88526354417205,1.39506661891937,1.44492328166962,0.853144347667694,0.256450712680817,0.17278964817524,0.0280280914157629,1.81331205368042
"Russia",49,5.96299982070923,6.03027490749955,5.89572473391891,1.28177809715271,1.46928238868713,0.547349333763123,0.373783111572266,0.0522638224065304,0.0329628810286522,2.20560741424561
"Belize",50,5.95599985122681,6.19724231779575,5.71475738465786,0.907975316047668,1.08141779899597,0.450191766023636,0.547509372234344,0.240015640854836,0.0965810716152191,2.63195562362671
"Japan",51,5.92000007629395,5.99071944460273,5.84928070798516,1.41691517829895,1.43633782863617,0.913475871086121,0.505625545978546,0.12057276815176,0.163760736584663,1.36322355270386
"Lithuania",52,5.90199995040894,5.98266964137554,5.82133025944233,1.31458234786987,1.47351610660553,0.62894994020462,0.234231784939766,0.010164656676352,0.0118656428530812,2.22844052314758
"Algeria",53,5.87200021743774,5.97828643366694,5.76571400120854,1.09186446666718,1.1462174654007,0.617584645748138,0.233335807919502,0.0694366469979286,0.146096110343933,2.56760382652283
"Latvia",54,5.84999990463257,5.92026353821158,5.77973627105355,1.26074862480164,1.40471494197845,0.638566970825195,0.325707912445068,0.153074786067009,0.0738427266478539,1.99365520477295
"South Korea",55,5.83799982070923,5.92255902826786,5.7534406131506,1.40167844295502,1.12827444076538,0.900214076042175,0.257921665906906,0.206674367189407,0.0632826685905457,1.88037800788879
"Moldova",56,5.83799982070923,5.90837083846331,5.76762880295515,0.728870630264282,1.25182557106018,0.589465200901031,0.240729048848152,0.208779126405716,0.0100912861526012,2.80780839920044
"Romania",57,5.82499980926514,5.91969415679574,5.73030546173453,1.21768391132355,1.15009129047394,0.685158312320709,0.457003742456436,0.133519917726517,0.00438790069893003,2.17683148384094
"Bolivia",58,5.82299995422363,5.9039769025147,5.74202300593257,0.833756566047668,1.22761905193329,0.473630249500275,0.558732926845551,0.22556072473526,0.0604777261614799,2.44327902793884
"Turkmenistan",59,5.82200002670288,5.88518087550998,5.75881917789578,1.13077676296234,1.49314916133881,0.437726080417633,0.41827192902565,0.24992498755455,0.259270340204239,1.83290982246399
"Kazakhstan",60,5.81899976730347,5.90364177465439,5.73435775995255,1.28455626964569,1.38436901569366,0.606041550636292,0.437454283237457,0.201964423060417,0.119282886385918,1.78489255905151
"North Cyprus",61,5.80999994277954,5.89736646488309,5.72263342067599,1.3469113111496,1.18630337715149,0.834647238254547,0.471203625202179,0.266845703125,0.155353352427483,1.54915761947632
"Slovenia",62,5.75799989700317,5.84222516000271,5.67377463400364,1.3412059545517,1.45251882076263,0.790828227996826,0.572575807571411,0.242649093270302,0.0451289787888527,1.31331729888916
"Peru",63,5.71500015258789,5.81194677859545,5.61805352658033,1.03522527217865,1.21877038478851,0.630166113376617,0.450002878904343,0.126819714903831,0.0470490865409374,2.20726943016052
"Mauritius",64,5.62900018692017,5.72986219167709,5.52813818216324,1.18939554691315,1.20956099033356,0.638007462024689,0.491247326135635,0.360933750867844,0.0421815551817417,1.6975839138031
"Cyprus",65,5.62099981307983,5.71469269931316,5.5273069268465,1.35593807697296,1.13136327266693,0.84471470117569,0.355111539363861,0.271254301071167,0.0412379764020443,1.62124919891357
"Estonia",66,5.61100006103516,5.68813987419009,5.53386024788022,1.32087934017181,1.47667109966278,0.695168316364288,0.479131430387497,0.0988908112049103,0.183248922228813,1.35750865936279
"Belarus",67,5.56899976730347,5.64611424401402,5.49188529059291,1.15655755996704,1.44494521617889,0.637714266777039,0.295400261878967,0.15513750910759,0.156313821673393,1.72323298454285
"Libya",68,5.52500009536743,5.67695380687714,5.37304638385773,1.10180306434631,1.35756433010101,0.520169019699097,0.465733230113983,0.152073666453362,0.0926102101802826,1.83501124382019
"Turkey",69,5.5,5.59486496329308,5.40513503670692,1.19827437400818,1.33775317668915,0.637605607509613,0.300740599632263,0.0466930419206619,0.0996715798974037,1.87927794456482
"Paraguay",70,5.49300003051758,5.57738126963377,5.40861879140139,0.932537317276001,1.50728487968445,0.579250693321228,0.473507791757584,0.224150657653809,0.091065913438797,1.6853334903717
"Hong Kong S.A.R., China",71,5.47200012207031,5.54959417313337,5.39440607100725,1.55167484283447,1.26279091835022,0.943062424659729,0.490968644618988,0.374465793371201,0.293933749198914,0.554633140563965
"Philippines",72,5.42999982833862,5.54533505424857,5.31466460242867,0.85769921541214,1.25391757488251,0.468009054660797,0.585214674472809,0.193513423204422,0.0993318930268288,1.97260475158691
"Serbia",73,5.39499998092651,5.49156965613365,5.29843030571938,1.06931757926941,1.25818979740143,0.65078467130661,0.208715528249741,0.220125883817673,0.0409037806093693,1.94708442687988
"Jordan",74,5.33599996566772,5.44841002240777,5.22358990892768,0.991012394428253,1.23908889293671,0.604590058326721,0.418421149253845,0.172170460224152,0.11980327218771,1.79117655754089
"Hungary",75,5.32399988174438,5.40303970918059,5.24496005430818,1.2860119342804,1.34313309192657,0.687763452529907,0.175863519310951,0.0784016624093056,0.0366369374096394,1.71645927429199
"Jamaica",76,5.31099987030029,5.58139872848988,5.04060101211071,0.925579309463501,1.36821806430817,0.641022384166718,0.474307239055634,0.233818337321281,0.0552677810192108,1.61232566833496
"Croatia",77,5.29300022125244,5.39177720457315,5.19422323793173,1.22255623340607,0.96798300743103,0.701288521289825,0.255772292613983,0.248002976179123,0.0431031100451946,1.85449242591858
"Kosovo",78,5.27899980545044,5.36484799548984,5.19315161541104,0.951484382152557,1.13785350322723,0.541452050209045,0.260287940502167,0.319931447505951,0.0574716180562973,2.01054072380066
"China",79,5.27299976348877,5.31927808977663,5.2267214372009,1.08116579055786,1.16083741188049,0.741415500640869,0.472787708044052,0.0288068410009146,0.0227942746132612,1.76493859291077
"Pakistan",80,5.26900005340576,5.35998364135623,5.17801646545529,0.72688353061676,0.672690689563751,0.402047783136368,0.23521526157856,0.315446019172668,0.124348066747189,2.79248929023743
"Indonesia",81,5.26200008392334,5.35288859814405,5.17111156970263,0.995538592338562,1.27444469928741,0.492345720529556,0.443323463201523,0.611704587936401,0.0153171354904771,1.42947697639465
"Venezuela",82,5.25,5.3700319455564,5.1299680544436,1.12843120098114,1.43133759498596,0.617144227027893,0.153997123241425,0.0650196298956871,0.0644911229610443,1.78946375846863
"Montenegro",83,5.23699998855591,5.34104444056749,5.13295553654432,1.12112903594971,1.23837649822235,0.667464673519135,0.194989055395126,0.197911024093628,0.0881741940975189,1.72919154167175
"Morocco",84,5.2350001335144,5.31834096476436,5.15165930226445,0.878114581108093,0.774864435195923,0.59771066904068,0.408158332109451,0.0322099551558495,0.0877631828188896,2.45618939399719
"Azerbaijan",85,5.23400020599365,5.29928653523326,5.16871387675405,1.15360176563263,1.15240025520325,0.540775775909424,0.398155838251114,0.0452693402767181,0.180987507104874,1.76248168945312
"Dominican Republic",86,5.23000001907349,5.34906088516116,5.11093915298581,1.07937383651733,1.40241670608521,0.574873745441437,0.55258983373642,0.186967849731445,0.113945253193378,1.31946516036987
"Greece",87,5.22700023651123,5.3252461694181,5.12875430360436,1.28948748111725,1.23941457271576,0.810198903083801,0.0957312509417534,0,0.04328977689147,1.74922156333923
"Lebanon",88,5.22499990463257,5.31888228848577,5.13111752077937,1.07498753070831,1.12962424755096,0.735081076622009,0.288515985012054,0.264450758695602,0.037513829767704,1.69507384300232
"Portugal",89,5.19500017166138,5.28504173308611,5.10495861023665,1.3151752948761,1.36704301834106,0.795843541622162,0.498465299606323,0.0951027125120163,0.0158694516867399,1.10768270492554
"Bosnia and Herzegovina",90,5.18200016021729,5.27633568674326,5.08766463369131,0.982409417629242,1.0693359375,0.705186307430267,0.204403176903725,0.328867495059967,0,1.89217257499695
"Honduras",91,5.18100023269653,5.30158279687166,5.0604176685214,0.730573117733002,1.14394497871399,0.582569479942322,0.348079860210419,0.236188873648643,0.0733454525470734,2.06581115722656
"Macedonia",92,5.17500019073486,5.27217263966799,5.07782774180174,1.06457793712616,1.20789301395416,0.644948184490204,0.325905978679657,0.25376096367836,0.0602777935564518,1.6174693107605
"Somalia",93,5.15100002288818,5.24248370990157,5.0595163358748,0.0226431842893362,0.721151351928711,0.113989137113094,0.602126955986023,0.291631311178207,0.282410323619843,3.11748456954956
"Vietnam",94,5.07399988174438,5.14728076457977,5.000718998909,0.788547575473785,1.27749133110046,0.652168989181519,0.571055591106415,0.234968051314354,0.0876332372426987,1.46231865882874
"Nigeria",95,5.07399988174438,5.20950013548136,4.93849962800741,0.783756256103516,1.21577048301697,0.0569157302379608,0.394952565431595,0.230947196483612,0.0261215660721064,2.36539053916931
"Tajikistan",96,5.04099988937378,5.11142559587956,4.970574182868,0.524713635444641,1.27146327495575,0.529235124588013,0.471566706895828,0.248997643589973,0.146377146244049,1.84904932975769
"Bhutan",97,5.01100015640259,5.07933456212282,4.94266575068235,0.885416388511658,1.34012651443481,0.495879292488098,0.501537680625916,0.474054545164108,0.173380389809608,1.14018440246582
"Kyrgyzstan",98,5.00400018692017,5.08991990312934,4.91808047071099,0.596220076084137,1.39423859119415,0.553457796573639,0.454943388700485,0.42858037352562,0.0394391790032387,1.53672313690186
"Nepal",99,4.96199989318848,5.06735607936978,4.85664370700717,0.479820191860199,1.17928326129913,0.504130780696869,0.440305948257446,0.394096165895462,0.0729755461215973,1.8912410736084
"Mongolia",100,4.95499992370605,5.0216795091331,4.88832033827901,1.02723586559296,1.4930112361908,0.557783484458923,0.394143968820572,0.338464230298996,0.0329022891819477,1.11129236221313
"South Africa",101,4.8289999961853,4.92943518772721,4.72856480464339,1.05469870567322,1.38478863239288,0.187080070376396,0.479246735572815,0.139362379908562,0.0725094974040985,1.51090860366821
"Tunisia",102,4.80499982833862,4.88436700701714,4.72563264966011,1.00726580619812,0.868351459503174,0.613212049007416,0.289680689573288,0.0496933571994305,0.0867231488227844,1.89025115966797
"Palestinian Territories",103,4.77500009536743,4.88184834256768,4.66815184816718,0.716249227523804,1.15564715862274,0.565666973590851,0.25471106171608,0.114173173904419,0.0892826020717621,1.8788902759552
"Egypt",104,4.7350001335144,4.82513378962874,4.64486647740006,0.989701807498932,0.997471392154694,0.520187258720398,0.282110154628754,0.128631442785263,0.114381365478039,1.70216107368469
"Bulgaria",105,4.71400022506714,4.80369470641017,4.62430574372411,1.1614590883255,1.43437945842743,0.708217680454254,0.289231717586517,0.113177694380283,0.0110515309497714,0.996139287948608
"Sierra Leone",106,4.70900011062622,4.85064333498478,4.56735688626766,0.36842092871666,0.984136044979095,0.00556475389748812,0.318697690963745,0.293040901422501,0.0710951760411263,2.66845989227295
"Cameroon",107,4.69500017166138,4.79654085725546,4.5934594860673,0.564305365085602,0.946018218994141,0.132892116904259,0.430388748645782,0.236298456788063,0.0513066314160824,2.3336455821991
"Iran",108,4.69199991226196,4.79822470769286,4.58577511683106,1.15687310695648,0.711551249027252,0.639333188533783,0.249322608113289,0.387242913246155,0.048761073499918,1.49873495101929
"Albania",109,4.64400005340576,4.75246400639415,4.53553610041738,0.996192753314972,0.803685247898102,0.731159746646881,0.381498634815216,0.201312944293022,0.0398642159998417,1.49044156074524
"Bangladesh",110,4.60799980163574,4.68982165828347,4.52617794498801,0.586682975292206,0.735131740570068,0.533241033554077,0.478356659412384,0.172255352139473,0.123717859387398,1.97873616218567
"Namibia",111,4.57399988174438,4.77035474091768,4.37764502257109,0.964434325695038,1.0984708070755,0.33861181139946,0.520303547382355,0.0771337449550629,0.0931469723582268,1.4818902015686
"Kenya",112,4.55299997329712,4.65569159060717,4.45030835598707,0.560479462146759,1.06795072555542,0.309988349676132,0.452763766050339,0.444860309362411,0.0646413192152977,1.6519021987915
"Mozambique",113,4.55000019073486,4.77410232633352,4.3258980551362,0.234305649995804,0.870701014995575,0.106654435396194,0.480791091918945,0.322228103876114,0.179436385631561,2.35565090179443
"Myanmar",114,4.54500007629395,4.61473994642496,4.47526020616293,0.367110550403595,1.12323594093323,0.397522568702698,0.514492034912109,0.838075160980225,0.188816204667091,1.11529040336609
"Senegal",115,4.53499984741211,4.6016037812829,4.46839591354132,0.479309022426605,1.17969191074371,0.409362852573395,0.377922266721725,0.183468893170357,0.115460447967052,1.78964614868164
"Zambia",116,4.51399993896484,4.64410550147295,4.38389437645674,0.636406779289246,1.00318729877472,0.257835894823074,0.461603492498398,0.249580144882202,0.0782135501503944,1.82670545578003
"Iraq",117,4.49700021743774,4.62259140968323,4.37140902519226,1.10271048545837,0.978613197803497,0.501180469989777,0.288555532693863,0.19963726401329,0.107215754687786,1.31890726089478
"Gabon",118,4.46500015258789,4.5573617656529,4.37263853952289,1.1982102394104,1.1556202173233,0.356578588485718,0.312328577041626,0.0437853783369064,0.0760467872023582,1.32291626930237
"Ethiopia",119,4.46000003814697,4.54272867664695,4.377271399647,0.339233845472336,0.86466920375824,0.353409707546234,0.408842742443085,0.312650740146637,0.165455713868141,2.01574373245239
"Sri Lanka",120,4.44000005722046,4.55344719231129,4.32655292212963,1.00985014438629,1.25997638702393,0.625130832195282,0.561213254928589,0.490863561630249,0.0736539661884308,0.419389247894287
"Armenia",121,4.37599992752075,4.46673461228609,4.28526524275541,0.900596737861633,1.00748372077942,0.637524425983429,0.198303267359734,0.0834880918264389,0.0266744215041399,1.5214991569519
"India",122,4.31500005722046,4.37152201749384,4.25847809694707,0.792221248149872,0.754372596740723,0.455427616834641,0.469987004995346,0.231538489460945,0.0922268852591515,1.5191171169281
"Mauritania",123,4.29199981689453,4.37716361626983,4.20683601751924,0.648457288742065,1.2720308303833,0.285349279642105,0.0960980430245399,0.201870024204254,0.136957004666328,1.65163731575012
"Congo (Brazzaville)",124,4.29099988937378,4.41005350500345,4.17194627374411,0.808964252471924,0.832044363021851,0.28995743393898,0.435025870800018,0.120852127671242,0.0796181336045265,1.72413563728333
"Georgia",125,4.28599977493286,4.37493396580219,4.19706558406353,0.950612664222717,0.57061493396759,0.649546980857849,0.309410035610199,0.0540088154375553,0.251666635274887,1.50013780593872
"Congo (Kinshasa)",126,4.28000020980835,4.35781083270907,4.20218958690763,0.0921023488044739,1.22902345657349,0.191407024860382,0.235961347818375,0.246455833315849,0.0602413564920425,2.22495865821838
"Mali",127,4.19000005722046,4.26967071101069,4.11032940343022,0.476180493831635,1.28147339820862,0.169365674257278,0.306613743305206,0.183354198932648,0.104970246553421,1.66819095611572
"Ivory Coast",128,4.17999982833862,4.27518256321549,4.08481709346175,0.603048920631409,0.904780030250549,0.0486421696841717,0.447706192731857,0.201237469911575,0.130061775445938,1.84496426582336
"Cambodia",129,4.16800022125244,4.27851781353354,4.05748262897134,0.601765096187592,1.00623834133148,0.429783403873444,0.633375823497772,0.385922968387604,0.0681059509515762,1.04294109344482
"Sudan",130,4.13899993896484,4.34574716508389,3.9322527128458,0.65951669216156,1.21400856971741,0.290920823812485,0.0149958552792668,0.182317450642586,0.089847519993782,1.68706583976746
"Ghana",131,4.11999988555908,4.22270720854402,4.01729256257415,0.667224824428558,0.873664736747742,0.295637726783752,0.423026293516159,0.256923943758011,0.0253363698720932,1.57786750793457
"Ukraine",132,4.09600019454956,4.18541010454297,4.00659028455615,0.89465194940567,1.39453756809235,0.575903952121735,0.122974775731564,0.270061463117599,0.0230294708162546,0.814382314682007
"Uganda",133,4.08099985122681,4.19579996705055,3.96619973540306,0.381430715322495,1.12982773780823,0.217632606625557,0.443185955286026,0.325766056776047,0.057069718837738,1.526362657547
"Burkina Faso",134,4.03200006484985,4.12405906438828,3.93994106531143,0.3502277135849,1.04328000545502,0.215844258666039,0.324367851018906,0.250864684581757,0.120328105986118,1.72721290588379
"Niger",135,4.02799987792969,4.11194681972265,3.94405293613672,0.161925330758095,0.993025004863739,0.26850500702858,0.36365869641304,0.228673845529556,0.138572946190834,1.87398338317871
"Malawi",136,3.97000002861023,4.07747881740332,3.86252123981714,0.233442038297653,0.512568831443787,0.315089583396912,0.466914653778076,0.287170469760895,0.0727116540074348,2.08178615570068
"Chad",137,3.93600010871887,4.0347115239501,3.83728869348764,0.438012987375259,0.953855872154236,0.0411347150802612,0.16234202682972,0.216113850474358,0.0535818822681904,2.07123804092407
"Zimbabwe",138,3.875,3.97869964271784,3.77130035728216,0.375846534967422,1.08309590816498,0.196763753890991,0.336384207010269,0.189143493771553,0.0953753814101219,1.59797024726868
"Lesotho",139,3.80800008773804,4.04434397548437,3.5716561999917,0.521021246910095,1.19009518623352,0,0.390661299228668,0.157497271895409,0.119094640016556,1.42983531951904
"Angola",140,3.79500007629395,3.95164193540812,3.63835821717978,0.858428180217743,1.10441195964813,0.0498686656355858,0,0.097926490008831,0.0697203353047371,1.61448240280151
"Afghanistan",141,3.79399991035461,3.87366141527891,3.71433840543032,0.401477217674255,0.581543326377869,0.180746778845787,0.106179520487785,0.311870932579041,0.0611578300595284,2.15080118179321
"Botswana",142,3.76600003242493,3.87412266626954,3.65787739858031,1.12209415435791,1.22155499458313,0.341755509376526,0.505196332931519,0.0993484482169151,0.0985831990838051,0.3779137134552
"Benin",143,3.65700006484985,3.74578355133533,3.56821657836437,0.431085407733917,0.435299843549728,0.209930211305618,0.425962775945663,0.207948461174965,0.0609290152788162,1.88563096523285
"Madagascar",144,3.64400005340576,3.71431910589337,3.57368100091815,0.305808693170547,0.913020372390747,0.375223308801651,0.189196765422821,0.208732530474663,0.0672319754958153,1.58461260795593
"Haiti",145,3.6029999256134,3.73471479773521,3.47128505349159,0.368610262870789,0.640449821949005,0.277321130037308,0.0303698573261499,0.489203780889511,0.0998721495270729,1.69716763496399
"Yemen",146,3.59299993515015,3.69275031983852,3.49324955046177,0.591683447360992,0.93538224697113,0.310080915689468,0.249463722109795,0.104125209152699,0.0567674227058887,1.34560060501099
"South Sudan",147,3.59100008010864,3.72553858578205,3.45646157443523,0.39724862575531,0.601323127746582,0.163486003875732,0.147062435746193,0.285670816898346,0.116793513298035,1.87956738471985
"Liberia",148,3.53299999237061,3.65375626087189,3.41224372386932,0.119041793048382,0.872117936611176,0.229918196797371,0.332881182432175,0.26654988527298,0.0389482490718365,1.67328596115112
"Guinea",149,3.50699996948242,3.58442812889814,3.4295718100667,0.244549930095673,0.791244685649872,0.194129139184952,0.348587512969971,0.264815092086792,0.110937617719173,1.55231189727783
"Togo",150,3.49499988555908,3.59403811171651,3.39596165940166,0.305444717407227,0.431882530450821,0.247105568647385,0.38042613863945,0.196896150708199,0.0956650152802467,1.83722925186157
"Rwanda",151,3.47099995613098,3.54303023353219,3.39896967872977,0.368745893239975,0.945707023143768,0.326424807310104,0.581843852996826,0.252756029367447,0.455220013856888,0.540061235427856
"Syria",152,3.46199989318848,3.66366855680943,3.26033122956753,0.777153134346008,0.396102607250214,0.50053334236145,0.0815394446253777,0.493663728237152,0.151347130537033,1.06157350540161
"Tanzania",153,3.34899997711182,3.46142975538969,3.23657019883394,0.511135876178741,1.04198980331421,0.364509284496307,0.390017777681351,0.354256361722946,0.0660351067781448,0.621130466461182
"Burundi",154,2.90499997138977,3.07469033300877,2.73530960977077,0.091622568666935,0.629793584346771,0.151610791683197,0.0599007532000542,0.204435184597969,0.0841479450464249,1.68302416801453
"Central African Republic",155,2.69300007820129,2.86488426923752,2.52111588716507,0,0,0.0187726859003305,0.270842045545578,0.280876487493515,0.0565650761127472,2.06600475311279
1 Country Happiness.Rank Happiness.Score Whisker.high Whisker.low Economy..GDP.per.Capita. Family Health..Life.Expectancy. Freedom Generosity Trust..Government.Corruption. Dystopia.Residual
2 Norway 1 7.53700017929077 7.59444482058287 7.47955553799868 1.61646318435669 1.53352355957031 0.796666502952576 0.635422587394714 0.36201223731041 0.315963834524155 2.27702665328979
3 Denmark 2 7.52199983596802 7.58172806486487 7.46227160707116 1.48238301277161 1.55112159252167 0.792565524578094 0.626006722450256 0.355280488729477 0.40077006816864 2.31370735168457
4 Iceland 3 7.50400018692017 7.62203047305346 7.38596990078688 1.480633020401 1.6105740070343 0.833552122116089 0.627162635326385 0.475540220737457 0.153526559472084 2.32271528244019
5 Switzerland 4 7.49399995803833 7.56177242040634 7.42622749567032 1.56497955322266 1.51691174507141 0.858131289482117 0.620070576667786 0.290549278259277 0.367007285356522 2.2767162322998
6 Finland 5 7.4689998626709 7.52754207581282 7.41045764952898 1.44357192516327 1.5402467250824 0.80915766954422 0.617950856685638 0.24548277258873 0.38261154294014 2.4301815032959
7 Netherlands 6 7.3769998550415 7.42742584124207 7.32657386884093 1.50394463539124 1.42893922328949 0.810696125030518 0.585384488105774 0.470489829778671 0.282661825418472 2.29480409622192
8 Canada 7 7.31599998474121 7.38440283536911 7.24759713411331 1.47920441627502 1.48134899139404 0.83455765247345 0.611100912094116 0.435539722442627 0.287371516227722 2.18726444244385
9 New Zealand 8 7.31400012969971 7.3795104418695 7.24848981752992 1.40570604801178 1.54819512367249 0.816759705543518 0.614062130451202 0.500005125999451 0.382816702127457 2.0464563369751
10 Sweden 9 7.28399991989136 7.34409487739205 7.22390496239066 1.49438726902008 1.47816216945648 0.830875158309937 0.612924098968506 0.385399252176285 0.384398728609085 2.09753799438477
11 Australia 10 7.28399991989136 7.35665122494102 7.2113486148417 1.484414935112 1.51004195213318 0.84388679265976 0.601607382297516 0.477699249982834 0.301183730363846 2.06521081924438
12 Israel 11 7.21299982070923 7.27985325649381 7.14614638492465 1.37538242340088 1.37628996372223 0.83840399980545 0.405988603830338 0.330082654953003 0.0852421000599861 2.80175733566284
13 Costa Rica 12 7.0789999961853 7.16811166629195 6.98988832607865 1.10970628261566 1.41640365123749 0.759509265422821 0.580131649971008 0.214613229036331 0.100106589496136 2.89863920211792
14 Austria 13 7.00600004196167 7.07066981211305 6.94133027181029 1.48709726333618 1.4599449634552 0.815328419208527 0.567766189575195 0.316472321748734 0.221060365438461 2.1385064125061
15 United States 14 6.99300003051758 7.07465674757957 6.91134331345558 1.54625928401947 1.41992056369781 0.77428662776947 0.505740523338318 0.392578780651093 0.135638788342476 2.2181134223938
16 Ireland 15 6.97700023651123 7.04335166752338 6.91064880549908 1.53570663928986 1.55823111534119 0.80978262424469 0.573110342025757 0.42785832285881 0.29838815331459 1.77386903762817
17 Germany 16 6.95100021362305 7.00538156926632 6.89661885797977 1.48792338371277 1.47252035140991 0.798950731754303 0.562511384487152 0.336269170045853 0.276731938123703 2.01576995849609
18 Belgium 17 6.89099979400635 6.95582075044513 6.82617883756757 1.46378076076508 1.46231269836426 0.818091869354248 0.539770722389221 0.231503337621689 0.251343131065369 2.12421035766602
19 Luxembourg 18 6.86299991607666 6.92368609987199 6.80231373228133 1.74194359779358 1.45758366584778 0.845089495182037 0.59662789106369 0.283180981874466 0.31883442401886 1.61951208114624
20 United Kingdom 19 6.71400022506714 6.78379176110029 6.64420868903399 1.44163393974304 1.49646008014679 0.805335938930511 0.508190035820007 0.492774158716202 0.265428066253662 1.70414352416992
21 Chile 20 6.65199995040894 6.73925056010485 6.56474934071302 1.25278460979462 1.28402495384216 0.819479703903198 0.376895278692245 0.326662421226501 0.0822879821062088 2.50958585739136
22 United Arab Emirates 21 6.64799976348877 6.72204730376601 6.57395222321153 1.62634336948395 1.26641023159027 0.726798236370087 0.60834527015686 0.3609419465065 0.324489563703537 1.734703540802
23 Brazil 22 6.63500022888184 6.72546950161457 6.5445309561491 1.10735321044922 1.43130600452423 0.616552352905273 0.437453746795654 0.16234989464283 0.111092761158943 2.76926708221436
24 Czech Republic 23 6.60900020599365 6.68386246263981 6.5341379493475 1.35268235206604 1.43388521671295 0.754444003105164 0.490946173667908 0.0881067588925362 0.0368729270994663 2.45186185836792
25 Argentina 24 6.59899997711182 6.69008508607745 6.50791486814618 1.18529546260834 1.44045114517212 0.695137083530426 0.494519203901291 0.109457060694695 0.059739887714386 2.61400532722473
26 Mexico 25 6.57800006866455 6.67114890769124 6.48485122963786 1.15318381786346 1.210862159729 0.709978997707367 0.412730008363724 0.120990432798862 0.132774114608765 2.83715486526489
27 Singapore 26 6.57200002670288 6.63672306910157 6.50727698430419 1.69227766990662 1.35381436347961 0.949492394924164 0.549840569496155 0.345965981483459 0.46430778503418 1.21636199951172
28 Malta 27 6.52699995040894 6.59839677289128 6.45560312792659 1.34327983856201 1.48841166496277 0.821944236755371 0.588767051696777 0.574730575084686 0.153066068887711 1.55686283111572
29 Uruguay 28 6.4539999961853 6.54590621769428 6.36209377467632 1.21755969524384 1.41222786903381 0.719216823577881 0.57939225435257 0.175096929073334 0.178061872720718 2.17240953445435
30 Guatemala 29 6.4539999961853 6.56687397271395 6.34112601965666 0.872001945972443 1.25558519363403 0.540239989757538 0.531310617923737 0.283488392829895 0.0772232785820961 2.89389109611511
31 Panama 30 6.4520001411438 6.55713071614504 6.34686956614256 1.23374843597412 1.37319254875183 0.706156134605408 0.550026834011078 0.21055693924427 0.070983923971653 2.30719995498657
32 France 31 6.44199991226196 6.51576780244708 6.36823202207685 1.43092346191406 1.38777685165405 0.844465851783752 0.470222115516663 0.129762306809425 0.172502428293228 2.00595474243164
33 Thailand 32 6.42399978637695 6.50911685571074 6.33888271704316 1.12786877155304 1.42579245567322 0.647239029407501 0.580200731754303 0.572123110294342 0.0316127352416515 2.03950834274292
34 Taiwan Province of China 33 6.42199993133545 6.49459602192044 6.34940384075046 1.43362653255463 1.38456535339355 0.793984234333038 0.361466586589813 0.258360475301743 0.0638292357325554 2.1266074180603
35 Spain 34 6.40299987792969 6.4710548453033 6.33494491055608 1.38439786434174 1.53209090232849 0.888960599899292 0.408781230449677 0.190133571624756 0.0709140971302986 1.92775774002075
36 Qatar 35 6.375 6.56847681432962 6.18152318567038 1.87076568603516 1.27429687976837 0.710098087787628 0.604130983352661 0.330473870038986 0.439299255609512 1.1454644203186
37 Colombia 36 6.35699987411499 6.45202005416155 6.26197969406843 1.07062232494354 1.4021829366684 0.595027923583984 0.477487415075302 0.149014472961426 0.0466687418520451 2.61606812477112
38 Saudi Arabia 37 6.3439998626709 6.44416661202908 6.24383311331272 1.53062355518341 1.28667759895325 0.590148329734802 0.449750572443008 0.147616013884544 0.27343225479126 2.0654296875
39 Trinidad and Tobago 38 6.16800022125244 6.38153389066458 5.95446655184031 1.36135590076447 1.3802285194397 0.519983291625977 0.518630743026733 0.325296461582184 0.00896481610834599 2.05324745178223
40 Kuwait 39 6.10500001907349 6.1919569888711 6.01804304927588 1.63295245170593 1.25969874858856 0.632105708122253 0.496337592601776 0.228289797902107 0.215159550309181 1.64042520523071
41 Slovakia 40 6.09800004959106 6.1773484121263 6.01865168705583 1.32539355754852 1.50505924224854 0.712732911109924 0.295817464590073 0.136544480919838 0.0242108516395092 2.09777665138245
42 Bahrain 41 6.08699989318848 6.17898906782269 5.99501071855426 1.48841226100922 1.32311046123505 0.653133034706116 0.536746919155121 0.172668486833572 0.257042169570923 1.65614938735962
43 Malaysia 42 6.08400011062622 6.17997963652015 5.98802058473229 1.29121541976929 1.28464603424072 0.618784427642822 0.402264982461929 0.416608929634094 0.0656007081270218 2.00444889068604
44 Nicaragua 43 6.07100009918213 6.18658360034227 5.95541659802198 0.737299203872681 1.28721570968628 0.653095960617065 0.447551846504211 0.301674216985703 0.130687981843948 2.51393055915833
45 Ecuador 44 6.00799989700317 6.10584767535329 5.91015211865306 1.00082039833069 1.28616881370544 0.685636222362518 0.4551981985569 0.150112465023994 0.140134647488594 2.29035258293152
46 El Salvador 45 6.00299978256226 6.108635122329 5.89736444279552 0.909784495830536 1.18212509155273 0.596018552780151 0.432452529668808 0.0782579854130745 0.0899809598922729 2.7145938873291
47 Poland 46 5.97300004959106 6.05390834122896 5.89209175795317 1.29178786277771 1.44571197032928 0.699475347995758 0.520342111587524 0.158465966582298 0.0593078061938286 1.79772281646729
48 Uzbekistan 47 5.97100019454956 6.06553757295012 5.876462816149 0.786441087722778 1.54896914958954 0.498272627592087 0.658248662948608 0.415983647108078 0.246528223156929 1.81691360473633
49 Italy 48 5.96400022506714 6.04273690596223 5.88526354417205 1.39506661891937 1.44492328166962 0.853144347667694 0.256450712680817 0.17278964817524 0.0280280914157629 1.81331205368042
50 Russia 49 5.96299982070923 6.03027490749955 5.89572473391891 1.28177809715271 1.46928238868713 0.547349333763123 0.373783111572266 0.0522638224065304 0.0329628810286522 2.20560741424561
51 Belize 50 5.95599985122681 6.19724231779575 5.71475738465786 0.907975316047668 1.08141779899597 0.450191766023636 0.547509372234344 0.240015640854836 0.0965810716152191 2.63195562362671
52 Japan 51 5.92000007629395 5.99071944460273 5.84928070798516 1.41691517829895 1.43633782863617 0.913475871086121 0.505625545978546 0.12057276815176 0.163760736584663 1.36322355270386
53 Lithuania 52 5.90199995040894 5.98266964137554 5.82133025944233 1.31458234786987 1.47351610660553 0.62894994020462 0.234231784939766 0.010164656676352 0.0118656428530812 2.22844052314758
54 Algeria 53 5.87200021743774 5.97828643366694 5.76571400120854 1.09186446666718 1.1462174654007 0.617584645748138 0.233335807919502 0.0694366469979286 0.146096110343933 2.56760382652283
55 Latvia 54 5.84999990463257 5.92026353821158 5.77973627105355 1.26074862480164 1.40471494197845 0.638566970825195 0.325707912445068 0.153074786067009 0.0738427266478539 1.99365520477295
56 South Korea 55 5.83799982070923 5.92255902826786 5.7534406131506 1.40167844295502 1.12827444076538 0.900214076042175 0.257921665906906 0.206674367189407 0.0632826685905457 1.88037800788879
57 Moldova 56 5.83799982070923 5.90837083846331 5.76762880295515 0.728870630264282 1.25182557106018 0.589465200901031 0.240729048848152 0.208779126405716 0.0100912861526012 2.80780839920044
58 Romania 57 5.82499980926514 5.91969415679574 5.73030546173453 1.21768391132355 1.15009129047394 0.685158312320709 0.457003742456436 0.133519917726517 0.00438790069893003 2.17683148384094
59 Bolivia 58 5.82299995422363 5.9039769025147 5.74202300593257 0.833756566047668 1.22761905193329 0.473630249500275 0.558732926845551 0.22556072473526 0.0604777261614799 2.44327902793884
60 Turkmenistan 59 5.82200002670288 5.88518087550998 5.75881917789578 1.13077676296234 1.49314916133881 0.437726080417633 0.41827192902565 0.24992498755455 0.259270340204239 1.83290982246399
61 Kazakhstan 60 5.81899976730347 5.90364177465439 5.73435775995255 1.28455626964569 1.38436901569366 0.606041550636292 0.437454283237457 0.201964423060417 0.119282886385918 1.78489255905151
62 North Cyprus 61 5.80999994277954 5.89736646488309 5.72263342067599 1.3469113111496 1.18630337715149 0.834647238254547 0.471203625202179 0.266845703125 0.155353352427483 1.54915761947632
63 Slovenia 62 5.75799989700317 5.84222516000271 5.67377463400364 1.3412059545517 1.45251882076263 0.790828227996826 0.572575807571411 0.242649093270302 0.0451289787888527 1.31331729888916
64 Peru 63 5.71500015258789 5.81194677859545 5.61805352658033 1.03522527217865 1.21877038478851 0.630166113376617 0.450002878904343 0.126819714903831 0.0470490865409374 2.20726943016052
65 Mauritius 64 5.62900018692017 5.72986219167709 5.52813818216324 1.18939554691315 1.20956099033356 0.638007462024689 0.491247326135635 0.360933750867844 0.0421815551817417 1.6975839138031
66 Cyprus 65 5.62099981307983 5.71469269931316 5.5273069268465 1.35593807697296 1.13136327266693 0.84471470117569 0.355111539363861 0.271254301071167 0.0412379764020443 1.62124919891357
67 Estonia 66 5.61100006103516 5.68813987419009 5.53386024788022 1.32087934017181 1.47667109966278 0.695168316364288 0.479131430387497 0.0988908112049103 0.183248922228813 1.35750865936279
68 Belarus 67 5.56899976730347 5.64611424401402 5.49188529059291 1.15655755996704 1.44494521617889 0.637714266777039 0.295400261878967 0.15513750910759 0.156313821673393 1.72323298454285
69 Libya 68 5.52500009536743 5.67695380687714 5.37304638385773 1.10180306434631 1.35756433010101 0.520169019699097 0.465733230113983 0.152073666453362 0.0926102101802826 1.83501124382019
70 Turkey 69 5.5 5.59486496329308 5.40513503670692 1.19827437400818 1.33775317668915 0.637605607509613 0.300740599632263 0.0466930419206619 0.0996715798974037 1.87927794456482
71 Paraguay 70 5.49300003051758 5.57738126963377 5.40861879140139 0.932537317276001 1.50728487968445 0.579250693321228 0.473507791757584 0.224150657653809 0.091065913438797 1.6853334903717
72 Hong Kong S.A.R., China 71 5.47200012207031 5.54959417313337 5.39440607100725 1.55167484283447 1.26279091835022 0.943062424659729 0.490968644618988 0.374465793371201 0.293933749198914 0.554633140563965
73 Philippines 72 5.42999982833862 5.54533505424857 5.31466460242867 0.85769921541214 1.25391757488251 0.468009054660797 0.585214674472809 0.193513423204422 0.0993318930268288 1.97260475158691
74 Serbia 73 5.39499998092651 5.49156965613365 5.29843030571938 1.06931757926941 1.25818979740143 0.65078467130661 0.208715528249741 0.220125883817673 0.0409037806093693 1.94708442687988
75 Jordan 74 5.33599996566772 5.44841002240777 5.22358990892768 0.991012394428253 1.23908889293671 0.604590058326721 0.418421149253845 0.172170460224152 0.11980327218771 1.79117655754089
76 Hungary 75 5.32399988174438 5.40303970918059 5.24496005430818 1.2860119342804 1.34313309192657 0.687763452529907 0.175863519310951 0.0784016624093056 0.0366369374096394 1.71645927429199
77 Jamaica 76 5.31099987030029 5.58139872848988 5.04060101211071 0.925579309463501 1.36821806430817 0.641022384166718 0.474307239055634 0.233818337321281 0.0552677810192108 1.61232566833496
78 Croatia 77 5.29300022125244 5.39177720457315 5.19422323793173 1.22255623340607 0.96798300743103 0.701288521289825 0.255772292613983 0.248002976179123 0.0431031100451946 1.85449242591858
79 Kosovo 78 5.27899980545044 5.36484799548984 5.19315161541104 0.951484382152557 1.13785350322723 0.541452050209045 0.260287940502167 0.319931447505951 0.0574716180562973 2.01054072380066
80 China 79 5.27299976348877 5.31927808977663 5.2267214372009 1.08116579055786 1.16083741188049 0.741415500640869 0.472787708044052 0.0288068410009146 0.0227942746132612 1.76493859291077
81 Pakistan 80 5.26900005340576 5.35998364135623 5.17801646545529 0.72688353061676 0.672690689563751 0.402047783136368 0.23521526157856 0.315446019172668 0.124348066747189 2.79248929023743
82 Indonesia 81 5.26200008392334 5.35288859814405 5.17111156970263 0.995538592338562 1.27444469928741 0.492345720529556 0.443323463201523 0.611704587936401 0.0153171354904771 1.42947697639465
83 Venezuela 82 5.25 5.3700319455564 5.1299680544436 1.12843120098114 1.43133759498596 0.617144227027893 0.153997123241425 0.0650196298956871 0.0644911229610443 1.78946375846863
84 Montenegro 83 5.23699998855591 5.34104444056749 5.13295553654432 1.12112903594971 1.23837649822235 0.667464673519135 0.194989055395126 0.197911024093628 0.0881741940975189 1.72919154167175
85 Morocco 84 5.2350001335144 5.31834096476436 5.15165930226445 0.878114581108093 0.774864435195923 0.59771066904068 0.408158332109451 0.0322099551558495 0.0877631828188896 2.45618939399719
86 Azerbaijan 85 5.23400020599365 5.29928653523326 5.16871387675405 1.15360176563263 1.15240025520325 0.540775775909424 0.398155838251114 0.0452693402767181 0.180987507104874 1.76248168945312
87 Dominican Republic 86 5.23000001907349 5.34906088516116 5.11093915298581 1.07937383651733 1.40241670608521 0.574873745441437 0.55258983373642 0.186967849731445 0.113945253193378 1.31946516036987
88 Greece 87 5.22700023651123 5.3252461694181 5.12875430360436 1.28948748111725 1.23941457271576 0.810198903083801 0.0957312509417534 0 0.04328977689147 1.74922156333923
89 Lebanon 88 5.22499990463257 5.31888228848577 5.13111752077937 1.07498753070831 1.12962424755096 0.735081076622009 0.288515985012054 0.264450758695602 0.037513829767704 1.69507384300232
90 Portugal 89 5.19500017166138 5.28504173308611 5.10495861023665 1.3151752948761 1.36704301834106 0.795843541622162 0.498465299606323 0.0951027125120163 0.0158694516867399 1.10768270492554
91 Bosnia and Herzegovina 90 5.18200016021729 5.27633568674326 5.08766463369131 0.982409417629242 1.0693359375 0.705186307430267 0.204403176903725 0.328867495059967 0 1.89217257499695
92 Honduras 91 5.18100023269653 5.30158279687166 5.0604176685214 0.730573117733002 1.14394497871399 0.582569479942322 0.348079860210419 0.236188873648643 0.0733454525470734 2.06581115722656
93 Macedonia 92 5.17500019073486 5.27217263966799 5.07782774180174 1.06457793712616 1.20789301395416 0.644948184490204 0.325905978679657 0.25376096367836 0.0602777935564518 1.6174693107605
94 Somalia 93 5.15100002288818 5.24248370990157 5.0595163358748 0.0226431842893362 0.721151351928711 0.113989137113094 0.602126955986023 0.291631311178207 0.282410323619843 3.11748456954956
95 Vietnam 94 5.07399988174438 5.14728076457977 5.000718998909 0.788547575473785 1.27749133110046 0.652168989181519 0.571055591106415 0.234968051314354 0.0876332372426987 1.46231865882874
96 Nigeria 95 5.07399988174438 5.20950013548136 4.93849962800741 0.783756256103516 1.21577048301697 0.0569157302379608 0.394952565431595 0.230947196483612 0.0261215660721064 2.36539053916931
97 Tajikistan 96 5.04099988937378 5.11142559587956 4.970574182868 0.524713635444641 1.27146327495575 0.529235124588013 0.471566706895828 0.248997643589973 0.146377146244049 1.84904932975769
98 Bhutan 97 5.01100015640259 5.07933456212282 4.94266575068235 0.885416388511658 1.34012651443481 0.495879292488098 0.501537680625916 0.474054545164108 0.173380389809608 1.14018440246582
99 Kyrgyzstan 98 5.00400018692017 5.08991990312934 4.91808047071099 0.596220076084137 1.39423859119415 0.553457796573639 0.454943388700485 0.42858037352562 0.0394391790032387 1.53672313690186
100 Nepal 99 4.96199989318848 5.06735607936978 4.85664370700717 0.479820191860199 1.17928326129913 0.504130780696869 0.440305948257446 0.394096165895462 0.0729755461215973 1.8912410736084
101 Mongolia 100 4.95499992370605 5.0216795091331 4.88832033827901 1.02723586559296 1.4930112361908 0.557783484458923 0.394143968820572 0.338464230298996 0.0329022891819477 1.11129236221313
102 South Africa 101 4.8289999961853 4.92943518772721 4.72856480464339 1.05469870567322 1.38478863239288 0.187080070376396 0.479246735572815 0.139362379908562 0.0725094974040985 1.51090860366821
103 Tunisia 102 4.80499982833862 4.88436700701714 4.72563264966011 1.00726580619812 0.868351459503174 0.613212049007416 0.289680689573288 0.0496933571994305 0.0867231488227844 1.89025115966797
104 Palestinian Territories 103 4.77500009536743 4.88184834256768 4.66815184816718 0.716249227523804 1.15564715862274 0.565666973590851 0.25471106171608 0.114173173904419 0.0892826020717621 1.8788902759552
105 Egypt 104 4.7350001335144 4.82513378962874 4.64486647740006 0.989701807498932 0.997471392154694 0.520187258720398 0.282110154628754 0.128631442785263 0.114381365478039 1.70216107368469
106 Bulgaria 105 4.71400022506714 4.80369470641017 4.62430574372411 1.1614590883255 1.43437945842743 0.708217680454254 0.289231717586517 0.113177694380283 0.0110515309497714 0.996139287948608
107 Sierra Leone 106 4.70900011062622 4.85064333498478 4.56735688626766 0.36842092871666 0.984136044979095 0.00556475389748812 0.318697690963745 0.293040901422501 0.0710951760411263 2.66845989227295
108 Cameroon 107 4.69500017166138 4.79654085725546 4.5934594860673 0.564305365085602 0.946018218994141 0.132892116904259 0.430388748645782 0.236298456788063 0.0513066314160824 2.3336455821991
109 Iran 108 4.69199991226196 4.79822470769286 4.58577511683106 1.15687310695648 0.711551249027252 0.639333188533783 0.249322608113289 0.387242913246155 0.048761073499918 1.49873495101929
110 Albania 109 4.64400005340576 4.75246400639415 4.53553610041738 0.996192753314972 0.803685247898102 0.731159746646881 0.381498634815216 0.201312944293022 0.0398642159998417 1.49044156074524
111 Bangladesh 110 4.60799980163574 4.68982165828347 4.52617794498801 0.586682975292206 0.735131740570068 0.533241033554077 0.478356659412384 0.172255352139473 0.123717859387398 1.97873616218567
112 Namibia 111 4.57399988174438 4.77035474091768 4.37764502257109 0.964434325695038 1.0984708070755 0.33861181139946 0.520303547382355 0.0771337449550629 0.0931469723582268 1.4818902015686
113 Kenya 112 4.55299997329712 4.65569159060717 4.45030835598707 0.560479462146759 1.06795072555542 0.309988349676132 0.452763766050339 0.444860309362411 0.0646413192152977 1.6519021987915
114 Mozambique 113 4.55000019073486 4.77410232633352 4.3258980551362 0.234305649995804 0.870701014995575 0.106654435396194 0.480791091918945 0.322228103876114 0.179436385631561 2.35565090179443
115 Myanmar 114 4.54500007629395 4.61473994642496 4.47526020616293 0.367110550403595 1.12323594093323 0.397522568702698 0.514492034912109 0.838075160980225 0.188816204667091 1.11529040336609
116 Senegal 115 4.53499984741211 4.6016037812829 4.46839591354132 0.479309022426605 1.17969191074371 0.409362852573395 0.377922266721725 0.183468893170357 0.115460447967052 1.78964614868164
117 Zambia 116 4.51399993896484 4.64410550147295 4.38389437645674 0.636406779289246 1.00318729877472 0.257835894823074 0.461603492498398 0.249580144882202 0.0782135501503944 1.82670545578003
118 Iraq 117 4.49700021743774 4.62259140968323 4.37140902519226 1.10271048545837 0.978613197803497 0.501180469989777 0.288555532693863 0.19963726401329 0.107215754687786 1.31890726089478
119 Gabon 118 4.46500015258789 4.5573617656529 4.37263853952289 1.1982102394104 1.1556202173233 0.356578588485718 0.312328577041626 0.0437853783369064 0.0760467872023582 1.32291626930237
120 Ethiopia 119 4.46000003814697 4.54272867664695 4.377271399647 0.339233845472336 0.86466920375824 0.353409707546234 0.408842742443085 0.312650740146637 0.165455713868141 2.01574373245239
121 Sri Lanka 120 4.44000005722046 4.55344719231129 4.32655292212963 1.00985014438629 1.25997638702393 0.625130832195282 0.561213254928589 0.490863561630249 0.0736539661884308 0.419389247894287
122 Armenia 121 4.37599992752075 4.46673461228609 4.28526524275541 0.900596737861633 1.00748372077942 0.637524425983429 0.198303267359734 0.0834880918264389 0.0266744215041399 1.5214991569519
123 India 122 4.31500005722046 4.37152201749384 4.25847809694707 0.792221248149872 0.754372596740723 0.455427616834641 0.469987004995346 0.231538489460945 0.0922268852591515 1.5191171169281
124 Mauritania 123 4.29199981689453 4.37716361626983 4.20683601751924 0.648457288742065 1.2720308303833 0.285349279642105 0.0960980430245399 0.201870024204254 0.136957004666328 1.65163731575012
125 Congo (Brazzaville) 124 4.29099988937378 4.41005350500345 4.17194627374411 0.808964252471924 0.832044363021851 0.28995743393898 0.435025870800018 0.120852127671242 0.0796181336045265 1.72413563728333
126 Georgia 125 4.28599977493286 4.37493396580219 4.19706558406353 0.950612664222717 0.57061493396759 0.649546980857849 0.309410035610199 0.0540088154375553 0.251666635274887 1.50013780593872
127 Congo (Kinshasa) 126 4.28000020980835 4.35781083270907 4.20218958690763 0.0921023488044739 1.22902345657349 0.191407024860382 0.235961347818375 0.246455833315849 0.0602413564920425 2.22495865821838
128 Mali 127 4.19000005722046 4.26967071101069 4.11032940343022 0.476180493831635 1.28147339820862 0.169365674257278 0.306613743305206 0.183354198932648 0.104970246553421 1.66819095611572
129 Ivory Coast 128 4.17999982833862 4.27518256321549 4.08481709346175 0.603048920631409 0.904780030250549 0.0486421696841717 0.447706192731857 0.201237469911575 0.130061775445938 1.84496426582336
130 Cambodia 129 4.16800022125244 4.27851781353354 4.05748262897134 0.601765096187592 1.00623834133148 0.429783403873444 0.633375823497772 0.385922968387604 0.0681059509515762 1.04294109344482
131 Sudan 130 4.13899993896484 4.34574716508389 3.9322527128458 0.65951669216156 1.21400856971741 0.290920823812485 0.0149958552792668 0.182317450642586 0.089847519993782 1.68706583976746
132 Ghana 131 4.11999988555908 4.22270720854402 4.01729256257415 0.667224824428558 0.873664736747742 0.295637726783752 0.423026293516159 0.256923943758011 0.0253363698720932 1.57786750793457
133 Ukraine 132 4.09600019454956 4.18541010454297 4.00659028455615 0.89465194940567 1.39453756809235 0.575903952121735 0.122974775731564 0.270061463117599 0.0230294708162546 0.814382314682007
134 Uganda 133 4.08099985122681 4.19579996705055 3.96619973540306 0.381430715322495 1.12982773780823 0.217632606625557 0.443185955286026 0.325766056776047 0.057069718837738 1.526362657547
135 Burkina Faso 134 4.03200006484985 4.12405906438828 3.93994106531143 0.3502277135849 1.04328000545502 0.215844258666039 0.324367851018906 0.250864684581757 0.120328105986118 1.72721290588379
136 Niger 135 4.02799987792969 4.11194681972265 3.94405293613672 0.161925330758095 0.993025004863739 0.26850500702858 0.36365869641304 0.228673845529556 0.138572946190834 1.87398338317871
137 Malawi 136 3.97000002861023 4.07747881740332 3.86252123981714 0.233442038297653 0.512568831443787 0.315089583396912 0.466914653778076 0.287170469760895 0.0727116540074348 2.08178615570068
138 Chad 137 3.93600010871887 4.0347115239501 3.83728869348764 0.438012987375259 0.953855872154236 0.0411347150802612 0.16234202682972 0.216113850474358 0.0535818822681904 2.07123804092407
139 Zimbabwe 138 3.875 3.97869964271784 3.77130035728216 0.375846534967422 1.08309590816498 0.196763753890991 0.336384207010269 0.189143493771553 0.0953753814101219 1.59797024726868
140 Lesotho 139 3.80800008773804 4.04434397548437 3.5716561999917 0.521021246910095 1.19009518623352 0 0.390661299228668 0.157497271895409 0.119094640016556 1.42983531951904
141 Angola 140 3.79500007629395 3.95164193540812 3.63835821717978 0.858428180217743 1.10441195964813 0.0498686656355858 0 0.097926490008831 0.0697203353047371 1.61448240280151
142 Afghanistan 141 3.79399991035461 3.87366141527891 3.71433840543032 0.401477217674255 0.581543326377869 0.180746778845787 0.106179520487785 0.311870932579041 0.0611578300595284 2.15080118179321
143 Botswana 142 3.76600003242493 3.87412266626954 3.65787739858031 1.12209415435791 1.22155499458313 0.341755509376526 0.505196332931519 0.0993484482169151 0.0985831990838051 0.3779137134552
144 Benin 143 3.65700006484985 3.74578355133533 3.56821657836437 0.431085407733917 0.435299843549728 0.209930211305618 0.425962775945663 0.207948461174965 0.0609290152788162 1.88563096523285
145 Madagascar 144 3.64400005340576 3.71431910589337 3.57368100091815 0.305808693170547 0.913020372390747 0.375223308801651 0.189196765422821 0.208732530474663 0.0672319754958153 1.58461260795593
146 Haiti 145 3.6029999256134 3.73471479773521 3.47128505349159 0.368610262870789 0.640449821949005 0.277321130037308 0.0303698573261499 0.489203780889511 0.0998721495270729 1.69716763496399
147 Yemen 146 3.59299993515015 3.69275031983852 3.49324955046177 0.591683447360992 0.93538224697113 0.310080915689468 0.249463722109795 0.104125209152699 0.0567674227058887 1.34560060501099
148 South Sudan 147 3.59100008010864 3.72553858578205 3.45646157443523 0.39724862575531 0.601323127746582 0.163486003875732 0.147062435746193 0.285670816898346 0.116793513298035 1.87956738471985
149 Liberia 148 3.53299999237061 3.65375626087189 3.41224372386932 0.119041793048382 0.872117936611176 0.229918196797371 0.332881182432175 0.26654988527298 0.0389482490718365 1.67328596115112
150 Guinea 149 3.50699996948242 3.58442812889814 3.4295718100667 0.244549930095673 0.791244685649872 0.194129139184952 0.348587512969971 0.264815092086792 0.110937617719173 1.55231189727783
151 Togo 150 3.49499988555908 3.59403811171651 3.39596165940166 0.305444717407227 0.431882530450821 0.247105568647385 0.38042613863945 0.196896150708199 0.0956650152802467 1.83722925186157
152 Rwanda 151 3.47099995613098 3.54303023353219 3.39896967872977 0.368745893239975 0.945707023143768 0.326424807310104 0.581843852996826 0.252756029367447 0.455220013856888 0.540061235427856
153 Syria 152 3.46199989318848 3.66366855680943 3.26033122956753 0.777153134346008 0.396102607250214 0.50053334236145 0.0815394446253777 0.493663728237152 0.151347130537033 1.06157350540161
154 Tanzania 153 3.34899997711182 3.46142975538969 3.23657019883394 0.511135876178741 1.04198980331421 0.364509284496307 0.390017777681351 0.354256361722946 0.0660351067781448 0.621130466461182
155 Burundi 154 2.90499997138977 3.07469033300877 2.73530960977077 0.091622568666935 0.629793584346771 0.151610791683197 0.0599007532000542 0.204435184597969 0.0841479450464249 1.68302416801453
156 Central African Republic 155 2.69300007820129 2.86488426923752 2.52111588716507 0 0 0.0187726859003305 0.270842045545578 0.280876487493515 0.0565650761127472 2.06600475311279

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libdataanalysis/8-Kmeans代码实现/utils/__init__.py
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libdataanalysis/8-Kmeans代码实现/utils/features/__init__.py
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"""Dataset Features Related Utils"""
from .normalize import normalize
from .generate_polynomials import generate_polynomials
from .generate_sinusoids import generate_sinusoids
from .prepare_for_training import prepare_for_training

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libdataanalysis/8-Kmeans代码实现/utils/features/generate_polynomials.py
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"""Add polynomial features to the features set"""
import numpy as np
from .normalize import normalize
def generate_polynomials(dataset, polynomial_degree, normalize_data=False):
"""变换方法：
x1, x2, x1^2, x2^2, x1*x2, x1*x2^2, etc.
"""
features_split = np.array_split(dataset, 2, axis=1)
dataset_1 = features_split[0]
dataset_2 = features_split[1]
(num_examples_1, num_features_1) = dataset_1.shape
(num_examples_2, num_features_2) = dataset_2.shape
if num_examples_1 != num_examples_2:
raise ValueError('Can not generate polynomials for two sets with different number of rows')
if num_features_1 == 0 and num_features_2 == 0:
raise ValueError('Can not generate polynomials for two sets with no columns')
if num_features_1 == 0:
dataset_1 = dataset_2
elif num_features_2 == 0:
dataset_2 = dataset_1
num_features = num_features_1 if num_features_1 < num_examples_2 else num_features_2
dataset_1 = dataset_1[:, :num_features]
dataset_2 = dataset_2[:, :num_features]
polynomials = np.empty((num_examples_1, 0))
for i in range(1, polynomial_degree + 1):
for j in range(i + 1):
polynomial_feature = (dataset_1 ** (i - j)) * (dataset_2 ** j)
polynomials = np.concatenate((polynomials, polynomial_feature), axis=1)
if normalize_data:
polynomials = normalize(polynomials)[0]
return polynomials

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libdataanalysis/8-Kmeans代码实现/utils/features/generate_sinusoids.py
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import numpy as np
def generate_sinusoids(dataset, sinusoid_degree):
"""
sin(x).
"""
num_examples = dataset.shape[0]
sinusoids = np.empty((num_examples, 0))
for degree in range(1, sinusoid_degree + 1):
sinusoid_features = np.sin(degree * dataset)
sinusoids = np.concatenate((sinusoids, sinusoid_features), axis=1)
return sinusoids

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libdataanalysis/8-Kmeans代码实现/utils/features/normalize.py
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"""Normalize features"""
import numpy as np
def normalize(features):
features_normalized = np.copy(features).astype(float)
# 计算均值
features_mean = np.mean(features, 0)
# 计算标准差
features_deviation = np.std(features, 0)
# 标准化操作
if features.shape[0] > 1:
features_normalized -= features_mean
# 防止除以0
features_deviation[features_deviation == 0] = 1
features_normalized /= features_deviation
return features_normalized, features_mean, features_deviation

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libdataanalysis/8-Kmeans代码实现/utils/features/prepare_for_training.py
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"""Prepares the dataset for training"""
import numpy as np
from .normalize import normalize
from .generate_sinusoids import generate_sinusoids
from .generate_polynomials import generate_polynomials
def prepare_for_training(data, polynomial_degree=0, sinusoid_degree=0, normalize_data=True):
# 计算样本总数
num_examples = data.shape[0]
data_processed = np.copy(data)
# 预处理
features_mean = 0
features_deviation = 0
data_normalized = data_processed
if normalize_data:
(
data_normalized,
features_mean,
features_deviation
) = normalize(data_processed)
data_processed = data_normalized
# 特征变换sinusoidal
if sinusoid_degree > 0:
sinusoids = generate_sinusoids(data_normalized, sinusoid_degree)
data_processed = np.concatenate((data_processed, sinusoids), axis=1)
# 特征变换polynomial
if polynomial_degree > 0:
polynomials = generate_polynomials(data_normalized, polynomial_degree, normalize_data)
data_processed = np.concatenate((data_processed, polynomials), axis=1)
# 加一列1
data_processed = np.hstack((np.ones((num_examples, 1)), data_processed))
return data_processed, features_mean, features_deviation

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libdataanalysis/8-Kmeans代码实现/utils/hypothesis/__init__.py
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"""Dataset Hypothesis Related Utils"""
from .sigmoid import sigmoid
from .sigmoid_gradient import sigmoid_gradient

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libdataanalysis/8-Kmeans代码实现/utils/hypothesis/sigmoid.py
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"""Sigmoid function"""
import numpy as np
def sigmoid(matrix):
"""Applies sigmoid function to NumPy matrix"""
return 1 / (1 + np.exp(-matrix))

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libdataanalysis/8-Kmeans代码实现/utils/hypothesis/sigmoid_gradient.py
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"""Sigmoid gradient function"""
from .sigmoid import sigmoid
def sigmoid_gradient(matrix):
"""Computes the gradient of the sigmoid function evaluated at z."""
return sigmoid(matrix) * (1 - sigmoid(matrix))

0
libdataanalysis/2-线性回归代码实现/LinearRegression/MultivariateLinearRegression.py → libdataanalysis/LinearRegression/MultivariateLinearRegression.py
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libdataanalysis/2-线性回归代码实现/LinearRegression/Non-linearRegression.py → libdataanalysis/LinearRegression/Non-linearRegression.py
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@ -40,7 +40,7 @@ plt.title('Gradient Descent Progress')
plt.show() plt.show()
predictions_num = 1000 predictions_num = 1000
x_predictions = np.linspace(x.min(), x.max(), predictions_num).reshape(predictions_num, 1); x_predictions = np.linspace(x.min(), x.max(), predictions_num).reshape(predictions_num, 1)
y_predictions = linear_regression.predict(x_predictions) y_predictions = linear_regression.predict(x_predictions)
plt.scatter(x, y, label='Training Dataset') plt.scatter(x, y, label='Training Dataset')

0
libdataanalysis/2-线性回归代码实现/LinearRegression/UnivariateLinearRegression.py → libdataanalysis/LinearRegression/UnivariateLinearRegression.py
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libdataanalysis/2-线性回归代码实现/LinearRegression/linear_regression.py → libdataanalysis/LinearRegression/linear_regression.py
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libdataanalysis/2-线性回归代码实现/LinearRegression/temp-plot.html → libdataanalysis/LinearRegression/temp-plot.html
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libdataanalysis/8-Kmeans代码实现/kmeans/demo.py → libdataanalysis/kmeans/demo.py
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libdataanalysis/8-Kmeans代码实现/kmeans/k_means.py → libdataanalysis/kmeans/k_means.py
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libdataanalysis/2-线性回归代码实现/utils/__init__.py → libdataanalysis/utils/__init__.py
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libdataanalysis/2-线性回归代码实现/utils/features/__init__.py → libdataanalysis/utils/features/__init__.py
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libdataanalysis/2-线性回归代码实现/utils/features/generate_polynomials.py → libdataanalysis/utils/features/generate_polynomials.py
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libdataanalysis/2-线性回归代码实现/utils/features/generate_sinusoids.py → libdataanalysis/utils/features/generate_sinusoids.py
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libdataanalysis/2-线性回归代码实现/utils/features/normalize.py → libdataanalysis/utils/features/normalize.py
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libdataanalysis/2-线性回归代码实现/utils/features/prepare_for_training.py → libdataanalysis/utils/features/prepare_for_training.py
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libdataanalysis/2-线性回归代码实现/utils/hypothesis/__init__.py → libdataanalysis/utils/hypothesis/__init__.py
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libdataanalysis/2-线性回归代码实现/utils/hypothesis/sigmoid.py → libdataanalysis/utils/hypothesis/sigmoid.py
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libdataanalysis/2-线性回归代码实现/utils/hypothesis/sigmoid_gradient.py → libdataanalysis/utils/hypothesis/sigmoid_gradient.py
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