First comment!

Author: UznetDev

model.py

@@ -0,0 +1,123 @@
+class LinearRegression():
+ """
+ A simple implementation of Linear Regression using Gradient Descent.
+ """
+
+ def __init__(self, step=0.001, n_iters=10000):
+ """
+ Initializes the LinearRegression class with the provided learning rate (`step`)
+ and the number of iterations (`n_iters`). Also initializes the slope and intercept
+ of the model, as well as internal variables to track the number of features (`__m_`)
+ and samples (`__n_`).
+
+ Parameters:
+ -----------
+ step : float
+ The learning rate for the gradient descent optimization. It controls how big
+ of a step is taken towards the minimum during each iteration.
+ n_iters : int
+ The number of iterations for the gradient descent optimization.
+ """
+ self.__k = 0
+ self.step = step
+ self.n_iters = n_iters
+ self._slope_ = 0
+ self._intercept_ = 0
+ self.__m_ = 0
+ self.__n_ = 0
+
+ def fit(self, X, y):
+ """
+ Trains the linear regression model using the input data `X` and target values `y`.
+ It adjusts the slope (`_slope_`) and intercept (`_intercept_`) by performing gradient
+ descent over `n_iters` iterations.
+
+ Parameters:
+ -----------
+ X : list of list of float or list of float
+ The input data, where each inner list represents a sample with multiple features,
+ or a simple list if there's only one feature.
+ y : list of float
+ The target values corresponding to each sample in `X`.
+
+ Raises:
+ -------
+ ValueError:
+ If the number of samples in `X` and `y` do not match.
+ """
+ self.__n_ = len(X)
+
+ if isinstance(X[0], list):
+ self.__m_ = len(X[0])
+ else:
+ self.__m_ = 1
+ X = [[x] for x in X]
+
+ if self.__n_ != len(y):
+ raise ValueError(f"X and y must have the same number of samples: {(self.__n_, len(y))}")
+
+ self._slope_ = [0] * self.__m_
+ self._intercept_ = 0
+
+ for _ in range(self.n_iters):
+ y_pred = self.predict(X)
+
+ for j in range(self.__m_):
+ self._slope_[j] -= self.step * (-(2/self.__n_) * sum((y[i] - y_pred[i]) * X[i][j] for i in range(self.__n_)))
+ self._intercept_ -= self.step * (-(2/self.__n_) * sum(y[i] - y_pred[i] for i in range(self.__n_)))
+
+ def predict(self, X):
+ """
+ Predicts the target values for the given input data `X` using the trained linear
+ regression model.
+
+ Parameters:
+ -----------
+ X : list of list of float or list of float
+ The input data, where each inner list represents a sample with multiple features,
+ or a simple list if there's only one feature.
+
+ Returns:
+ --------
+ y_pred : list of float
+ The predicted target values corresponding to each sample in `X`.
+
+ Raises:
+ -------
+ ValueError:
+ If the input data `X` has a different number of features or samples compared
+ to the data used for training.
+ """
+ if isinstance(X[0], list):
+ m = len(X[0])
+ else:
+ m = 1
+ X = [[x] for x in X]
+ n = len(X)
+ if m != self.__m_ or n != self.__n_:
+ raise ValueError(f"X must have the same number of features as the training data: {(m, n)}, Except: {(self.__m_, self.__n_)}")
+
+ y_pred = []
+ for i in range(len(X)):
+ y_pred.append(sum(self._slope_[j] * X[i][j] for j in range(m)) + self._intercept_)
+
+ return y_pred
+
+ def MSE(self, y, y_pred):
+ """
+ Calculates the Mean Squared Error (MSE) between the true target values `y` and the
+ predicted values `y_pred`.
+
+ Parameters:
+ -----------
+ y : list of float
+ The true target values.
+ y_pred : list of float
+ The predicted target values.
+
+ Returns:
+ --------
+ mse : float
+ The mean squared error between `y` and `y_pred`.
+ """
+ return sum((y[i] - y_pred[i]) ** 2 for i in range(len(y))) / len(y)