92 lines
3.1 KiB
Python
Executable File
92 lines
3.1 KiB
Python
Executable File
"""
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Article: Reduced Universum Least Squares Support Vector Machine
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Link : New
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Author : Saeed Khosravi
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"""
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import numpy as np
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import math
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class RULSTSVM:
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def __init__(self, X, y, C, eps):
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self.X = X
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self.y = y
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self.C = C
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self.eps = eps
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def fit(self):
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self.plane1(self.X, self.y, self.C[0], self.C[1], self.C[2], self.eps)
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self.plane2(self.X, self.y, self.C[3], self.C[4], self.C[5], self.eps)
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def predict(self, x_test):
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distance_1 = np.abs(np.dot(x_test, self.w1) + self.b1)
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distance_2 = np.abs(np.dot(x_test, self.w2) + self.b2)
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y_pred = np.zeros_like(distance_1).reshape((-1, 1))
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for i in range(y_pred.shape[0]):
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if (distance_1[i] < distance_2[i]):
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y_pred[i][0] = 1;
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else:
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y_pred[i][0] = -1;
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self.preds = y_pred
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def plane1(self, X, y, C1, C2, C3, eps):
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S, T_, O_, e1, eg = self.definitions1(X, y)
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STS = np.dot(S.T, S)
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T_TT_ = np.dot(T_.T, T_)
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O_TO_ = np.dot(O_.T, O_)
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I = np.eye(STS.shape[0], STS.shape[1])
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v1 = -np.dot(np.linalg.inv(STS + C1*T_TT_ + C2*I + C3*O_TO_), np.dot(C1*T_.T, e1) + (1-eps)*C3*np.dot(O_.T, eg))
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self.w1 = v1[:-1, :]
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self.b1 = v1[ -1, :]
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def plane2(self, X, y, C4, C5, C6, eps):
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S, T, O, e1, ed = self.definitions2(X, y)
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TTT = np.dot(T.T, T)
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STS = np.dot(S.T, S)
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OTO = np.dot(O.T, O)
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I = np.dot(TTT.shape[0], TTT.shape[0])
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v2 = np.dot(np.linalg.inv(TTT + C4*STS + C5*I + C6*OTO), C4*np.dot(S.T, e1) - C6*np.dot(O.T, (1-eps)*ed))
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self.w2 = v2[:-1, :]
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self.b2 = v2[ -1, :]
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def definitions1(self, X, y):
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X1 = X[np.ix_(y[:,0] == 1),:][0,:,:]
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X2 = X[np.ix_(y[:,0] == -1),:][0,:,:]
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r, n = X1.shape
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s, n = X2.shape
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np.random.shuffle(X2)
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X2_ = X2[:r, :]
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U = X2[r: , :]
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d, n = U.shape
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g = math.ceil(r/2)
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U_ = U[np.random.choice(np.arange(1, d), g), :]
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e1 = np.ones((X1.shape[0], 1))
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eg = np.ones((U_.shape[0], 1))
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S = np.concatenate((X1 , e1), axis = 1)
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T_ = np.concatenate((X2_, e1), axis = 1)
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O_ = np.concatenate((U_ , eg), axis = 1)
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return S, T_, O_, e1, eg
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def definitions2(self, X, y):
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X1 = X[np.ix_(y[:,0] == 1),:][0,:,:]
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X2 = X[np.ix_(y[:,0] == -1),:][0,:,:]
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r, n = X1.shape
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s, n = X2.shape
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np.random.shuffle(X2)
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X2_ = X2[:r, :]
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U = X2[r: , :]
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d, n = U.shape
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g = math.ceil(r/2)
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e1 = np.ones((X1.shape[0], 1))
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e2 = np.ones((X2.shape[0], 1))
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ed = np.ones((U.shape[0] , 1))
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S = np.concatenate((X1 , e1), axis = 1)
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T = np.concatenate((X2 , e2), axis = 1)
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O = np.concatenate((U , ed), axis = 1)
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return S, T, O, e1, ed
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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 |