56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from linear_regression import LinearRegression
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data = pd.read_csv('../data/world-happiness-report-2017.csv')
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# 得到训练和测试数据
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train_data = data.sample(frac = 0.8)
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test_data = data.drop(train_data.index)
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input_param_name = 'Economy..GDP.per.Capita.'
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output_param_name = 'Happiness.Score'
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x_train = train_data[[input_param_name]].values
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y_train = train_data[[output_param_name]].values
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x_test = test_data[input_param_name].values
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y_test = test_data[output_param_name].values
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plt.scatter(x_train,y_train,label='Train data')
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plt.scatter(x_test,y_test,label='test data')
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plt.xlabel(input_param_name)
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plt.ylabel(output_param_name)
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plt.title('Happy')
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plt.legend()
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plt.show()
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num_iterations = 500
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learning_rate = 0.01
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linear_regression = LinearRegression(x_train,y_train)
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(theta,cost_history) = linear_regression.train(learning_rate,num_iterations)
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print ('开始时的损失：',cost_history[0])
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print ('训练后的损失：',cost_history[-1])
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plt.plot(range(num_iterations),cost_history)
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plt.xlabel('Iter')
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plt.ylabel('cost')
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plt.title('GD')
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plt.show()
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predictions_num = 100
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x_predictions = np.linspace(x_train.min(),x_train.max(),predictions_num).reshape(predictions_num,1)
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y_predictions = linear_regression.predict(x_predictions)
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plt.scatter(x_train,y_train,label='Train data')
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plt.scatter(x_test,y_test,label='test data')
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plt.plot(x_predictions,y_predictions,'r',label = 'Prediction')
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plt.xlabel(input_param_name)
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plt.ylabel(output_param_name)
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plt.title('Happy')
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plt.legend()
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plt.show()
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