多项式非线性

linear/train.py

@@ -58,8 +58,8 @@ def test():
 	test_mse = metrics.mean_squared_error(y_test, y_pred_test)
 	print "测试集MSE:".decode('utf-8'),test_mse
 
-	train_curve = curce_data(X_train,y_train,y_pred_train)
-	test_curve = curce_data(X_test,y_test,y_pred_test)
+	# train_curve = curce_data(X_train,y_train,y_pred_train)
+	# test_curve = curce_data(X_test,y_test,y_pred_test)
 	print "推广mse差".decode('utf-8'), test_mse-train_mse
 
 	'''

linear/train_paracurve_data

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linear/train_xsquare.py

@@ -1,6 +1,8 @@
 #!/usr/bin/python
 # -*- coding: UTF-8 -*-
 import sys
+import matplotlib.pyplot as plt
+import numpy as np
 
 reload(sys)
 sys.setdefaultencoding('utf-8')
@@ -8,9 +10,11 @@ import numpy as np
 from sklearn.linear_model import LinearRegression
 from sklearn import metrics
 
-
+'''
+lesson4
+把特征性扩展平方
+'''
 def extend_feature(x):
-    # return [x[0]]
     return [x[0], x[0] * x[0]]
 
 
@@ -19,13 +23,48 @@ def read_data(path):
         lines = f.readlines()
     lines = [eval(line.strip()) for line in lines]
     X, y = zip(*lines)
+    X = np.array(X)
+    y = np.array(y)
+    return X, y
+
+
+def read_data2(path):
+    with open(path) as f:
+        lines = f.readlines()
+    lines = [eval(line.strip()) for line in lines]
+    X, y = zip(*lines)
     X = [extend_feature(x) for x in X]
     X = np.array(X)
     y = np.array(y)
     return X, y
 
 
-if __name__ == '__main__':
+def drawScatterAndLine(p, q, w, b):
+    plt.scatter(p, q)
+    plt.xlabel('p')
+    plt.ylabel('q')
+    plt.title('line regesion')
+
+    x = np.arange(-11, 11)
+    y = w * x + b
+
+    plt.plot(x, y, color='red')
+    plt.show()
+
+
+def drawScatterAndLine2(p, q, w, b):
+    plt.scatter(p, q)
+    plt.xlabel('p')
+    plt.ylabel('q')
+    plt.title('line regesion')
+
+    x = np.arange(-11, 11)
+    y = w[0] * x + w[1]*x*x + b
+
+    plt.plot(x, y, color='red')
+    plt.show()
+
+def test1():
     X_train, y_train = read_data("train_paracurve_data")
     X_test, y_test = read_data("test_paracurve_data")
     model = LinearRegression()
@@ -41,4 +80,44 @@ if __name__ == '__main__':
     test_mse = metrics.mean_squared_error(y_test, y_pred_test)
     print "MSE:", test_mse
     print "推广mse差", test_mse - train_mse
+    return X_train, y_train, model.coef_, model.intercept_
+
+
+def test2():
+    print("---------特征性修改平方------------")
+    X_train, y_train = read_data2("train_paracurve_data")
+    X_test, y_test = read_data2("test_paracurve_data")
+    model = LinearRegression()
+    model.fit(X_train, y_train)
+    print model.coef_
+    print model.intercept_
+
+    y_pred_train = model.predict(X_train)
+    train_mse = metrics.mean_squared_error(y_train, y_pred_train)
+    print "特征+平方非线性"
+    print "MSE:", train_mse
+    y_pred_test = model.predict(X_test)
+    test_mse = metrics.mean_squared_error(y_test, y_pred_test)
+    print "MSE:", test_mse
+    print "推广mse差", test_mse - train_mse
+    return X_train, y_train, model.coef_, model.intercept_
+
+
+if __name__ == '__main__':
+    p,q,w,b = test1()
+
+    p = [i[0] for i in p.tolist()]
+    q = [i[0] for i in q.tolist()]
+    w = w[0]
+    b = b[0]
+    drawScatterAndLine(p, q, w, b)
+
+    p,q,w,b = test2()
+    p = [i[0] for i in p.tolist()]
+    q = [i[0] for i in q.tolist()]
+    w = w[0]
+    b = b[0]
+    drawScatterAndLine2(p, q, w, b)
+
+