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svm.py
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svm.py
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import numpy as np
class SVM:
def __init__(self, lr=0.01, lamda=0.01, epochs=100):
"""
params:
lr : Learning rate
lamda : For regularization
epochs: Epochs(number of times loop will during training)
"""
self.lr = lr
self.lamda = lamda
self.epochs = epochs
#w - Weights
self.w = None
#b - Bias
self.b = None
def fit(self, X, y):
n_samples, n_features = X.shape
# Initializing weights and bias
self.w = np.zeros(n_features)
self.b = 0
# Converting labels to {-1, 1}
y_ = np.where(y <= 0, -1, 1)
# Gradient descent optimization
for _ in range(self.epochs):
for idx, x_i in enumerate(X):
condition = y_[idx] * (np.dot(x_i, self.w) + self.b) >= 1
if condition:
self.w -= self.lr * (2 * self.lamda * self.w)
else:
self.w -= self.lr * (2 * self.lamda * self.w - np.dot(x_i, y_[idx]))
self.b -= self.lr * y_[idx]
if _ % 10 == 0:
print(f"After epoch:{_} value of weights and bias is:")
print(self.w)
print(self.b)
def predict(self, X):
linear_output = np.dot(X, self.w) + self.b
# Convert -1 back to 0 for compatibility with the original labels
return np.where(np.sign(linear_output) == -1, 0, 1)