75 lines
2.2 KiB
Python
Executable File
75 lines
2.2 KiB
Python
Executable File
"""
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Article : Deep 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 LSTSVM
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class DLSTSVM:
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def __init__(self, X, y, C, eps = 1e-4):
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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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#LSTSVM 1
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C1 = self.C[0]
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C2 = self.C[1]
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y = self.y
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lstsvm1 = LSTSVM.LSTSVM(self.X, y, C1, C2)
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lstsvm1.fit()
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self.w11, self.b11, self.w12, self.b12 = lstsvm1.get_params()
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self.f1 = self.f_(self.X, self.w11, self.b11, self.w12, self.b12)
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#LSTSVM 2
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C1 = self.C[2]
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C2 = self.C[3]
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y = self.y
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lstsvm2 = LSTSVM.LSTSVM(self.X, y, C1, C2)
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lstsvm2.fit()
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self.w21, self.b21, self.w22, self.b22 = lstsvm2.get_params()
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self.f2 = self.f_(self.X, self.w21, self.b21, self.w22, self.b22)
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#LSTSVM Main
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C1 = self.C[4]
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C2 = self.C[5]
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X = self.f(self.f1, self.f2)
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y = self.y
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lstsvm_M = LSTSVM.LSTSVM(X, y, C1, C2)
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lstsvm_M.fit()
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self.w1, self.b1, self.w2, self.b2 = lstsvm_M.get_params()
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def predict(self, x_test, y_test):
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f1 = self.f_(x_test, self.w11, self.b11, self.w12, self.b12)
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f2 = self.f_(x_test, self.w21, self.b21, self.w22, self.b22)
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f = self.f(f1, f2)
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distance_1 = np.abs(np.dot(f, self.w1) + self.b1)
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distance_2 = np.abs(np.dot(f, self.w2) + self.b2)
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y_pred = np.zeros_like(distance_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 f_(self, x, w1, b1, w2, b2):
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f = np.concatenate((np.dot(x, w1)+b1, np.dot(x, w2)+b2), axis = 1)
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return f
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def f(self, f1, f2):
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f = np.concatenate((f1,f2), axis = 1)
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return f
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def get_hidden_params(self):
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return self.w11, self.b11, self.w12, self.b12, self.w21, self.b21, self.w22, self.b22
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def get_output_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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