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LinearRegression/LinearRegression_scikit-learn.py

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+#-*- coding: utf-8 -*-
+import numpy as np
+from sklearn import linear_model
+from sklearn.preprocessing import StandardScaler    #引入归一化的包
+
+def linearRegression():
+    print u"加载数据...\n"
+    data = loadtxtAndcsv_data("data.txt",",",np.float64)  #读取数据
+    X = np.array(data[:,0:-1],dtype=np.float64)      # X对应0到倒数第2列                  
+    y = np.array(data[:,-1],dtype=np.float64)        # y对应最后一列  
+
+    # 归一化操作
+    scaler = StandardScaler()   
+    scaler.fit(X)
+    x_train = scaler.transform(X)
+    x_test = scaler.transform(np.array([1650,3]))
+
+    # 线性模型拟合
+    model = linear_model.LinearRegression()
+    model.fit(x_train, y)
+
+    #预测结果
+    result = model.predict(x_test)
+    print model.coef_       # Coefficient of the features 决策函数中的特征系数
+    print model.intercept_  # 又名bias偏置,若设置为False，则为0
+    print result            # 预测结果
+
+
+# 加载txt和csv文件
+def loadtxtAndcsv_data(fileName,split,dataType):
+    return np.loadtxt(fileName,delimiter=split,dtype=dataType)
+
+# 加载npy文件
+def loadnpy_data(fileName):
+    return np.load(fileName)
+
+
+
+
+if __name__ == "__main__":
+    linearRegression()