Python机器学习创建线性回归器

蔚落 2022-03-21 14:18 204阅读 0赞

机器学习需要解决的问题一般可以分为分类、聚类、回归等几大类问题。  
线性回归是拟合输入数据和输出数据之间的关系的过程。  
输入数据：二维坐标的一些散点  
输出：拟合的一次线性方程

import sys
    import numpy as np
    
    x=[]
    y=[]
    filename='/home/XXX/tensorflow/data_singlevar.txt'
    with open(filename,'r') as f:
        for line in f.readlines():
            xt,yt=[float(i) for i in line.split(',')]
            x.append(xt)
            y.append(yt)
    
    num_training=int(0.8*len(x))
    num_test=len(x)-num_training
    
    x_train=np.array(x[:num_training]).reshape((num_training,1))
    y_train=np.array(y[:num_training])
    x_test=np.array(x[num_training:]).reshape((num_test,1))
    y_test=np.array(y[num_training:])
    
    from sklearn import linear_model
    linear_regressor=linear_model.LinearRegression()
    linear_regressor.fit(x_train,y_train)
    import matplotlib.pyplot as plt
    y_train_pred=linear_regressor.predict(x_train)
    plt.figure()
    plt.scatter(x_train,y_train,color='green')
    plt.plot(x_train,y_train_pred,color='black',linewidth=4)
    plt.title('Training Data')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70]

y_test_pred=linear_regressor.predict(x_test)
    plt.scatter(x_test,y_test,color='green')
    plt.plot(x_test,y_test_pred,color='black',linewidth=4)
    plt.title('Test Data')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70 1]

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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl8zODYzMjI0Ng_size_16_color_FFFFFF_t_70 1]: /images/20220321/79676ec37ada466389979a56dba1a451.png