init commit

RULSTSVM.py

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