104 lines
3.4 KiB
Python
104 lines
3.4 KiB
Python
"""
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Article : Least squares twin support vector machines for pattern classification
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Link : https://sci-hub.tw/https://www.sciencedirect.com/science/article/abs/pii/S0957417408006854
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Author : Saeed Khosravi
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"""
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import numpy as np
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class LSTSVM:
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"""
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Least Squares Support Vector Machines
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A = Instances with label +1
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B = Instances with label -1
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C1 = hyperparameter for hyperplane 1
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C2 = hyperparameter for hyperplane 2
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"""
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def __init__(self, X, y, C1, C2, eps = 1e-4):
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self.A = X[np.ix_(y[:,0] == 1),:][0,:,:]
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self.B = X[np.ix_(y[:,0] == -1),:][0,:,:]
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self.C1 = C1
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self.C2 = C2
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self.eps = eps
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def fit(self):
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A = self.A
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B = self.B
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C1 = self.C1
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C2 = self.C2
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eps = self.eps
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m1, n = A.shape
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m2, n = B.shape
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e1 = np.ones((m1, 1))
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e2 = np.ones((m2, 1))
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X = np.concatenate((A, B), axis=0)
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G = np.concatenate((A, e1), axis=1)
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H = np.concatenate((B, e2), axis=1)
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if(m1 < m2):
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Y = self.calc_Y_or_Z(H)
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#w1, b1
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GYGT = np.dot(np.dot(G, Y), G.T)
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I = np.eye(GYGT.shape[0], GYGT.shape[1])
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w1_b1 = - np.dot(Y - np.dot(np.dot(np.dot(Y, G.T), np.linalg.inv(C1*I + GYGT)), np.dot(G, Y)),
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np.dot(H.T, np.ones((H.T.shape[1], 1))))
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w1 = w1_b1[:-1, :]
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b1 = w1_b1[ -1, :]
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#w2, b2
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w2_b2 = C2 * np.dot(Y - np.dot(np.dot(np.dot(Y, G.T), np.linalg.inv((I/C2)+GYGT)), np.dot(G, Y)),
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np.dot(G.T, np.ones((G.T.shape[1], 1))))
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w2 = w2_b2[:-1, :]
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b2 = w2_b2[ -1, :]
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else:
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Z = self.calc_Y_or_Z(G)
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#w1, b1
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HZHT = np.dot(np.dot(H, Z), H.T)
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I = np.eye(HZHT.shape[0], HZHT.shape[1])
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w1_b1 = -C1*np.dot(Z - np.dot(np.dot(np.dot(Z, H.T), np.linalg.inv((I/C1) + HZHT)), np.dot(H, Z)),
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np.dot(H.T, np.ones((H.T.shape[1], 1))))
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w1 = w1_b1[:-1, :]
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b1 = w1_b1[ -1, :]
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#w2, b2
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w2_b2 = np.dot(Z - np.dot(np.dot(np.dot(Z, H.T), np.linalg.inv(C2*I + HZHT)), np.dot(H, Z)),
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np.dot(G.T, np.ones((G.T.shape[1], 1))))
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w2 = w2_b2[:-1, :]
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b2 = w2_b2[ -1, :]
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self.w1 = w1
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self.w2 = w2
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self.b1 = b1
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self.b2 = b2
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def predict(self, x_test, y_test):
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distance1 = np.abs(np.dot(x_test, self.w1) + self.b1)
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distance2 = np.abs(np.dot(x_test, self.w2) + self.b2)
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y_pred = np.zeros_like(y_test)
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for d in range(y_pred.shape[0]):
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if (distance1[d] < distance2[d]):
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y_pred[d][0] = 1;
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else:
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y_pred[d][0] = -1;
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self.preds = y_pred
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def calc_Y_or_Z(self, M):
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MMT = np.dot(M, M.T)
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I = np.eye(MMT.shape[0], MMT.shape[1])
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tmp = np.dot(np.dot(M.T, np.linalg.inv(self.eps*I + MMT)), M)
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I = np.eye(tmp.shape[0], tmp.shape[1])
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return (1/self.eps)*(I-tmp)
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def get_params(self):
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return self.w1, self.b1, self.w2, self.b2
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def get_preds(self):
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return self.preds
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def score(self, y_test):
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accuracy = np.sum(self.preds == y_test)/y_test.shape[0]
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return accuracy |