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