add files 2

机器学习代码实现/svm代码实现/svm_class.py

+import numpy as np
+import numpy as np
+from numpy import linalg
+import cvxopt
+import cvxopt.solvers
+
+def linear_kernel(x1, x2):
+    return np.dot(x1, x2)
+
+def polynomial_kernel(x1, x2, p=3):
+    return (1 + np.dot(x1, x2)) ** p
+
+# rbf
+def gaussian_kernel(x, y, sigma=5.0):
+    return np.exp(-linalg.norm(x-y) ** 2 / (2 * sigma ** 2))
+
+class SVM(object):
+    def __init__(self, kernel=linear_kernel, C=None):
+        self.kernel = kernel
+
+        # C=0: 硬间隔； 如果C不为0：软间隔
+        self.C = C
+        if self.C is not None: self.C = float(self.C)
+
+    def train(self, X, y):
+        n_smaples, n_features = X.shape
+
+        K = np.zeros((n_smaples, n_smaples))
+        for i in range(n_smaples):
+            for j in range(n_smaples):
+                K[i, j] = self.kernel(X[i], X[j])
+
+        P = cvxopt.matrix(np.outer(y, y) * K)
+        q = cvxopt.matrix(np.ones(n_smaples) * -1)
+        A = cvxopt.matrix(y, (1, n_smaples))
+        b = cvxopt.matrix(0.0)
+
+        if self.C is None:
+            G = cvxopt.matrix(np.diag(np.ones(n_smaples) * -1))
+            h = cvxopt.matrix(np.zeros(n_smaples))
+        else:
+            tmp1 = np.diag(np.ones(n_smaples) * -1)
+            tmp2 = np.identity(n_smaples)
+            G = cvxopt.matrix(np.vstack((tmp1, tmp2)))
+            tmp1 = np.zeros(n_smaples)
+            tmp2 = np.ones(n_smaples) * self.C
+            h = cvxopt.matrix(np.hstack((tmp1, tmp2)))
+
+        solution = cvxopt.solvers.qp(P, q, G, h, A, b)
+
+        #[x1, x2, x3, ...]
+        a = np.ravel(solution['x'])
+
+        # 支持向量
+        sv = a > 1e-5
+        ind = np.arange(len(a))[sv]
+        self.a = a[sv]
+        self.sv = X[sv]
+        self.sv_y = y[sv]
+        print('%d support vectors out of %d points' % (len(self.a), n_smaples))
+
+        # b
+        self.b = 0
+        for n in range(len(self.a)):
+            self.b += self.sv_y[n]
+            self.b -= np.sum(self.a * self.sv_y * K[ind[n], sv])
+        self.b /= len(self.a)
+
+        # w
+        if self.kernel == linear_kernel:
+            self.w = np.zeros(n_features)
+            for n in range(len(self.a)):
+                self.w += self.a[n] * self.sv_y[n] * self.sv[n]
+        else:
+            self.w = None
+
+
+    def project(self, X):
+        if self.w is not None:
+            # 线性核
+            return np.dot(X, self.w) + self.b
+        else:
+            # 非线性核
+            y_predict = np.zeros(len(X))
+            for i in range(len(X)):
+                s = 0
+                for a, sv_y, sv in zip(self.a, self.sv_y, self.sv):
+                    s += a * sv_y * self.kernel(X[i], sv)
+                y_predict[i] = s
+
+            return y_predict + self.b
+
+
+    def predict(self, X):
+        return np.sign(self.project(X))
+
+if __name__ == '__main__':
+    import pylab as pl
+
+    # 线性可分
+    def gen_lin_separable_data():
+        mean1 = np.array([0, 2])
+        mean2 = np.array([2, 0])
+        cov = np.array([[0.8, 0.6], [0.6, 0.8]])
+        X1 = np.random.multivariate_normal(mean1, cov, 100)
+        X2 = np.random.multivariate_normal(mean2, cov, 100)
+        y1 = np.ones(len(X1))
+        y2 = np.ones(len(X2)) * -1
+        return X1, y1, X2, y2
+
+    # 线性不可分-非线性
+    def gen_non_lin_separable_data():
+        mean1 = [-1, 2]
+        mean2 = [1, -1]
+        mean3 = [4, -4]
+        mean4 = [-4, 4]
+        cov = [[1.0, 0.8], [0.8, 1.0]]
+        X1= np.random.multivariate_normal(mean1, cov, 50)
+        X1 = np.vstack((X1, np.random.multivariate_normal(mean3, cov, 50)))
+        X2 = np.random.multivariate_normal(mean2, cov, 50)
+        X2 = np.vstack((X2, np.random.multivariate_normal(mean4, cov, 50)))
+        y1 = np.ones(len(X1))
+        y2 = np.ones(len(X2)) * -1
+        return X1, y1, X2, y2
+
+    # 线性不可分：有干扰项
+    def gen_lin_separable_overlap_data():
+        mean1 = np.array([0, 2])
+        mean2 = np.array([2, 0])
+        cov = np.array([[1.5, 1.0], [1.0, 1.5]])
+        X1 = np.random.multivariate_normal(mean1, cov, 100)
+        X2 = np.random.multivariate_normal(mean2, cov, 100)
+        y1 = np.ones(len(X1))
+        y2 = np.ones(len(X2)) * -1
+        return X1, y1, X2, y2
+
+    def split_train(X1, y1, X2, y2):
+        X1_train = X1[:90]
+        X2_train = X2[:90]
+        y1_train = y1[:90]
+        y2_train = y2[:90]
+        X_train = np.vstack((X1_train, X2_train))
+        y_train = np.hstack((y1_train, y2_train))
+        return X_train, y_train
+
+    def split_test(X1, y1, X2, y2):
+        X1_test = X1[:90]
+        X2_test = X2[:90]
+        y1_test = y1[:90]
+        y2_test = y2[:90]
+        X_test = np.vstack((X1_test, X2_test))
+        y_test = np.hstack((y1_test, y2_test))
+        return X_test, y_test
+
+    def plot_margin(X1_train, X2_train, clf):
+        def f(x, w, b, c=0):
+            return (-w[0] * x -b + c) / w[1]
+
+        pl.plot(X1_train[:, 0], X1_train[:, 1] , 'ro')
+        pl.plot(X2_train[:, 0], X2_train[:, 1] , 'bo')
+        pl.scatter(clf.sv[:, 0], clf.sv[:, 1], s=100, c='g')
+
+        # w.x + b = 0
+        a0 = -4;a1 = f(a0, clf.w, clf.b)
+        b0 = 4 ;b1 = f(b0, clf.w, clf.b)
+        pl.plot([a0, b0], [a1, b1], 'k')
+
+        # w.x + b = 1
+        a0 = -4; a1 = f(a0, clf.w, 1)
+        b0 = 4; b1 = f(b0, clf.w, 1)
+        pl.plot([a0, b0], [a1, b1], 'k--')
+
+        # w.x + b = -1
+        a0 = -4; a1 = f(a0, clf.w, -1)
+        b0 = 4 ;b1 = f(b0, clf.w, -1)
+        pl.plot([a0, b0], [a1, b1], 'k--')
+
+        pl.axis('tight')
+        pl.show()
+
+    def plot_contor(X1_train, X2_train, clf):
+        pl.plot(X1_train[:, 0], X1_train[:, 1], 'ro')
+        pl.plot(X2_train[:, 0], X2_train[:, 1], 'bo')
+        pl.scatter(clf.sv[:, 0], clf.sv[:, 1], s=100, c='g')
+
+        X1, X2 = np.meshgrid(np.linspace(-6, 6, 50), np.linspace(-6, 6, 50))
+        X = np.array([[x1, x2] for x1, x2 in zip(np.ravel(X1), np.ravel(X2))])
+        z = clf.project(X).reshape(X1.shape)
+        pl.contour(X1, X2, z, [0.0], colors='k', linewidths=1, origin='lower')
+        pl.contour(X1, X2, z + 1, [0.0], colors='grey', linewidths=1, origin='lower')
+        pl.contour(X1, X2, z - 1, [0.0], colors='grey', linewidths=1, origin='lower')
+
+        pl.axis('tight')
+        pl.show()
+
+    def test_linear():
+        X1, y1, X2, y2 = gen_lin_separable_data()
+        X_train, y_train = split_train(X1, y1, X2, y2)
+        X_test, y_test = split_test(X1, y1, X2, y2)
+
+        clf =SVM()
+        clf.train(X_train, y_train)
+
+        y_predict = clf.predict(X_test)
+        correct = np.sum(y_predict == y_test)
+        print('%d out of %d predictions correct.' % (correct, len(y_predict)))
+        plot_margin(X_train[y_train==1], X_train[y_train==-1], clf)
+
+
+    def test_non_linear():
+        X1, y1, X2, y2 = gen_non_lin_separable_data()
+        X_train, y_train = split_train(X1, y1, X2, y2)
+        X_test, y_test = split_test(X1, y1, X2, y2)
+
+        clf = SVM(polynomial_kernel)
+        clf.train(X_train, y_train)
+
+        y_predict = clf.predict(X_test)
+        correct = np.sum(y_predict == y_test)
+        print('%d out of %d predictions correct.' % (correct, len(y_predict)))
+        plot_contor(X_train[y_train == 1], X_train[y_train == -1], clf)
+
+    def test_soft():
+        X1, y1, X2, y2 = gen_lin_separable_overlap_data()
+        X_train, y_train = split_train(X1, y1, X2, y2)
+        X_test, y_test = split_test(X1, y1, X2, y2)
+
+        clf = SVM(C=1000.1)
+        clf.train(X_train, y_train)
+
+        y_predict = clf.predict(X_test)
+        correct = np.sum(y_predict == y_test)
+        print('%d out of %d predictions correct.' % (correct, len(y_predict)))
+        plot_contor(X_train[y_train == 1], X_train[y_train == -1], clf)
+
+    # test_linear()
+    # test_soft()
+    test_non_linear()
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