更新svm画图

src/python/6.SVM/sklearn-svm-demo.py

@@ -7,6 +7,7 @@ Updated on 2017-06-28
 SVM：最大边距分离超平面
 @author: 片刻
 《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
+sklearn-SVM译文链接: http://cwiki.apachecn.org/pages/viewpage.action?pageId=10031359
 """
 import numpy as np
 import matplotlib.pyplot as plt
@@ -52,7 +53,7 @@ clf.fit(X, Y)
 # 获取分割超平面
 w = clf.coef_[0]
 # 斜率
-a = -w[0] / w[1]
+a = -w[0]/w[1]
 # 从-5到5，顺序间隔采样50个样本，默认是num=50
 # xx = np.linspace(-5, 5)  # , num=50)
 xx = np.linspace(-2, 10)  # , num=50)
@@ -74,7 +75,7 @@ plt.plot(xx, yy_down, 'k--')
 plt.plot(xx, yy_up, 'k--')
 
 plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=80, facecolors='none')
-plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
+plt.scatter([X[:, 0]], [X[:, 1]], c=Y, cmap=plt.cm.Paired)
 
 plt.axis('tight')
 plt.show()

src/python/7.AdaBoost/sklearn-adaboost-demo.py

@@ -0,0 +1,91 @@
+#!/usr/bin/python
+# coding:utf8
+
+"""
+Created on 2017-07-10
+Updated on 2017-07-10
+@author: 片刻
+《机器学习实战》更新地址：https://github.com/apachecn/MachineLearning
+sklearn-AdaBoost译文链接: http://cwiki.apachecn.org/pages/viewpage.action?pageId=10813457
+"""
+print(__doc__)
+
+# Author: Noel Dawe <noel.dawe@gmail.com>
+#
+# License: BSD 3 clause
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.datasets import make_gaussian_quantiles
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.tree import DecisionTreeClassifier
+
+# Construct dataset
+X1, y1 = make_gaussian_quantiles(cov=2.,
+                                 n_samples=200, n_features=2,
+                                 n_classes=2, random_state=1)
+X2, y2 = make_gaussian_quantiles(mean=(3, 3), cov=1.5,
+                                 n_samples=300, n_features=2,
+                                 n_classes=2, random_state=1)
+X = np.concatenate((X1, X2))
+y = np.concatenate((y1, - y2 + 1))
+
+# Create and fit an AdaBoosted decision tree
+bdt = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1),
+                         algorithm="SAMME",
+                         n_estimators=200)
+
+bdt.fit(X, y)
+
+plot_colors = "br"
+plot_step = 0.02
+class_names = "AB"
+
+plt.figure(figsize=(10, 5))
+
+# Plot the decision boundaries
+plt.subplot(121)
+x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
+y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
+xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
+                     np.arange(y_min, y_max, plot_step))
+
+Z = bdt.predict(np.c_[xx.ravel(), yy.ravel()])
+Z = Z.reshape(xx.shape)
+cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
+plt.axis("tight")
+
+# Plot the training points
+for i, n, c in zip(range(2), class_names, plot_colors):
+    idx = np.where(y == i)
+    plt.scatter(X[idx, 0], X[idx, 1],
+                c=c, cmap=plt.cm.Paired,
+                label="Class %s" % n)
+plt.xlim(x_min, x_max)
+plt.ylim(y_min, y_max)
+plt.legend(loc='upper right')
+plt.xlabel('x')
+plt.ylabel('y')
+plt.title('Decision Boundary')
+
+# Plot the two-class decision scores
+twoclass_output = bdt.decision_function(X)
+plot_range = (twoclass_output.min(), twoclass_output.max())
+plt.subplot(122)
+for i, n, c in zip(range(2), class_names, plot_colors):
+    plt.hist(twoclass_output[y == i],
+             bins=10,
+             range=plot_range,
+             facecolor=c,
+             label='Class %s' % n,
+             alpha=.5)
+x1, x2, y1, y2 = plt.axis()
+plt.axis((x1, x2, y1, y2 * 1.2))
+plt.legend(loc='upper right')
+plt.ylabel('Samples')
+plt.xlabel('Score')
+plt.title('Decision Scores')
+
+plt.tight_layout()
+plt.subplots_adjust(wspace=0.35)
+plt.show()