SVM

Author: Y1ran

Diff for: Supervised_learn/Nerual_Network.py

@@ -0,0 +1,23 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug  7 18:25:15 2018
+
+@author: Administrator
+"""
+
+from sklearn.neural_network import MLPClassifier
+from sklearn.datasets import make_moons
+from sklearn.model_selection import train_test_split
+import mglearn
+
+def Muti_Layer_perceptron():
+    '''test'''
+    display(mglearn.plots.plot_logistic_regression_graph())
+    X, y= make_moons(n_samples=100, noise=0.25, random_state=3)
+    X_train, X_test, y_train, y_test = train_test_split(
+                X, y ,random_state=0)
+    
+    mlp = MLPClassifier(solver='lbfgs', random_state=0).fit(X_train,y_train)
+    
+    mglearn.plots.plot_2d_separator(mlp, X_train, fill=True, alpha=0.3)
+    mglearn.discrete_scatter(X_train[:,0], X_train[:,1], y_train)

Diff for: Supervised_learn/SVM.py

@@ -0,0 +1,54 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Aug  7 17:57:41 2018
+
+@author: Administrator
+"""
+
+from sklearn.svm import LinearSVC, SVC
+import mglearn
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_blobs 
+from sklearn.datasets import load_breast_cancer
+from sklearn.model_selection import train_test_split
+
+def SVC_test():
+    from sklearn.datasets import make_blobs 
+    X, y = make_blobs(centers=4, random_state=8)
+    y = y % 2
+    
+    mglearn.discrete_scatter(X[:,0],X[:,1], y)
+    plt.xlabel("F0"); plt.ylabel("F1")
+    
+    lin_svm = LinearSVC().fit(X, y)
+    svm = SVC(kernel='rbf', C=10,gamma=0.1).fit(X,y)
+    mglearn.plots.plot_2d_separator(lin_svm, X)
+    mglearn.discrete_scatter(X[:,0],X[:,1], y)
+    plt.xlabel("F0"); plt.ylabel("F1")
+    
+    print(svm.dual_coef_)
+    
+    fig,axes = plt.subplots(3, 3, figsize=(15,10))
+    
+    for ax, c in zip(axes, [-1, 0, 3]):
+        for a, gamma in zip(ax, range(-1,2)):
+            mglearn.plots.plot_svm(log_C=c, log_gamma=gamma, ax=a)
+    
+    axes[0,0].legend(["class 0", "class 1", "sv class 0", "sv class 1"],
+        ncol=4, loc=(.9,1.2))
+
+def SVC_fit():
+    
+    cancer = load_breast_cancer()
+
+    X_train, X_test, y_train, y_test = train_test_split(
+                cancer.data, cancer.target, stratify=cancer.target
+                , random_state=42)
+    
+    svc = SVC()
+    svc.fit(X_train,y_train)
+    
+    print(svc.score(X_train,y_train))
+    print(svc.score(X_test
+                    ,y_test))
+