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DLSTSVM.py

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+"""
+Article : Deep Least Squares Support Vector Machine 
+Link    : New
+Author  : Saeed Khosravi
+"""
+import numpy as np
+import LSTSVM
+class DLSTSVM:
+    def __init__(self, X, y, C, eps = 1e-4):
+        self.X   = X
+        self.y   = y
+        self.C   = C
+        self.eps = eps
+        
+    def fit(self):
+        #LSTSVM 1
+        C1 = self.C[0]
+        C2 = self.C[1]
+        y  = self.y
+        lstsvm1 = LSTSVM.LSTSVM(self.X, y, C1, C2)
+        lstsvm1.fit()
+        self.w11, self.b11, self.w12, self.b12 = lstsvm1.get_params()
+        self.f1 = self.f_(self.X, self.w11, self.b11, self.w12, self.b12)
+        
+        #LSTSVM 2
+        C1 = self.C[2]
+        C2 = self.C[3]
+        y  = self.y
+        lstsvm2 = LSTSVM.LSTSVM(self.X, y, C1, C2)
+        lstsvm2.fit()
+        self.w21, self.b21, self.w22, self.b22 = lstsvm2.get_params()
+        self.f2 = self.f_(self.X, self.w21, self.b21, self.w22, self.b22)
+        
+        #LSTSVM Main
+        C1 = self.C[4]
+        C2 = self.C[5]
+        X = self.f(self.f1, self.f2)
+        y = self.y
+        lstsvm_M = LSTSVM.LSTSVM(X, y, C1, C2)
+        lstsvm_M.fit()
+        self.w1, self.b1, self.w2, self.b2 = lstsvm_M.get_params()
+        
+    def predict(self, x_test, y_test):
+        f1 = self.f_(x_test, self.w11, self.b11, self.w12, self.b12)
+        f2 = self.f_(x_test, self.w21, self.b21, self.w22, self.b22)
+        f = self.f(f1, f2)
+        distance_1 = np.abs(np.dot(f, self.w1) + self.b1)
+        distance_2 = np.abs(np.dot(f, self.w2) + self.b2)
+        y_pred = np.zeros_like(distance_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 f_(self, x, w1, b1, w2, b2):
+        f  = np.concatenate((np.dot(x, w1)+b1, np.dot(x, w2)+b2), axis = 1)
+        return f
+        
+    def f(self, f1, f2):
+        f = np.concatenate((f1,f2), axis = 1)
+        return f
+    
+    
+    def get_hidden_params(self):
+        return self.w11, self.b11, self.w12, self.b12, self.w21, self.b21, self.w22, self.b22
+    
+    def get_output_params(self):
+        return self.w1, self.b1, self.w2, self.b2
+    
+    def get_preds(self):
+        return self.preds
+
+