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add normalization step #12

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12 changes: 10 additions & 2 deletions 02 Linear Regression/LinearRegression.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,12 +8,19 @@ def __init__(self, lr = 0.001, n_iters=1000):
self.n_iters = n_iters
self.weights = None
self.bias = None

self.mean = None
self.std = None

def _standardize(self, X):
return (X - self.mean) / self.std
def fit(self, X, y):
n_samples, n_features = X.shape
self.weights = np.zeros(n_features)
self.bias = 0

self.mean = np.mean(X, axis=0)
self.std = np.std(X, axis=0)
X = self._standardize(X)

for _ in range(self.n_iters):
y_pred = np.dot(X, self.weights) + self.bias

Expand All @@ -24,5 +31,6 @@ def fit(self, X, y):
self.bias = self.bias - self.lr * db

def predict(self, X):
X = self._standardize(X)
y_pred = np.dot(X, self.weights) + self.bias
return y_pred
12 changes: 10 additions & 2 deletions 03 Logistic Regression/LogisticRegression.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,12 +10,19 @@ def __init__(self, lr=0.001, n_iters=1000):
self.n_iters = n_iters
self.weights = None
self.bias = None

self.mean = None
self.std = None

def _standardize(self, X):
return (X - self.mean) / self.std
def fit(self, X, y):
n_samples, n_features = X.shape
self.weights = np.zeros(n_features)
self.bias = 0

self.mean = np.mean(X, axis=0)
self.std = np.std(X, axis=0)
X = self._standardize(X)

for _ in range(self.n_iters):
linear_pred = np.dot(X, self.weights) + self.bias
predictions = sigmoid(linear_pred)
Expand All @@ -28,6 +35,7 @@ def fit(self, X, y):


def predict(self, X):
X = self._standardize(X)
linear_pred = np.dot(X, self.weights) + self.bias
y_pred = sigmoid(linear_pred)
class_pred = [0 if y<=0.5 else 1 for y in y_pred]
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