Notes

class LinReg():
"""
Custom made Linear Regression class
"""
def __init__(self, alpha=0.01, n_iter= 1000):
self.alpha = alpha
self.n_iter = n_iter
self.params = {}

def param_init(self, X_train):
"""
Initialize parameters for linear regression model
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Input(s)
X: Training data
"""
_, n_features = self.X.shape # shape of training data

# initializing coefficents to 0
self.params["W"] = np.zeros(n_features)
self.params["b"] = 0
return self


def gradient_descent(self, X_train, y_train):
"""
Gradient descent to minimize cost function
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Input(s)
X: Training data
y: Labels
params: Dictionary contatining random coefficients
alpha: Model learning rate
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Output(s)
params: Dictionary containing optimized coefficients
"""
W = self.params["W"]
b = self.params["b"]
m = X_train.shape[0]

for _ in range(self.n_iter):
# prediction with random weights
y_pred = np.dot(X_train, W) + b
# taking the partial derivative of coefficients
dW = (2/m) * np.dot(X_train.T, (y_pred - y_train))
db = (2/m) * np.sum(y_pred - y_train)
# updates to coefficients
W -= self.alpha * dW
b -= self.alpha * db

self.params["W"] = W
self.params["b"] = b
return self

def train(self, X_train, y_train):
"""
Train Linear Regression model with Gradient decent
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Input(s)
X: Training data
y: Labels
alpha: Model learning rate
n_iter: Number of iterations
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Output(s)
params: Dictionary containing optimized coefficients
"""
self.params = param_init(X_train)
gradient_descent(X_train, y_train, self.params , self.alpha, self.n_iter)
return self

def predict(self, X_test):
"""
Train Linear Regression model with Gradient decent
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Input(s)
X: Unseen data
params: Dictionary contianing optimized weights from training
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Output(s)
y_preds: Predictions of model
"""
y_preds = np.dot(X_test, self.params["W"]) + self.params["b"]
return y_preds

linreg = LinReg()
linreg.train(X_train, y_train)
linreg.predict(X_test)