The task is to implement multiple functions and 2 classes:
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def h(x, theta) Write the linear hypothesis function. (see above)
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def mean_squared_error(y_pred, y_label) Write the Mean Squared Error function between the predicted values and the labels.
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def bias_column(x) Write a function which adds one to each instance.
X_new = bias_column(x) print(X[:5]) print(" ---- ") print(X_new[:5])
Classes class LeastSquaresRegression: (see description above) def init(self, ) def fit() def predict
class GradientDescentOptimizer: (see description above) def init() def step() def optimize() def getCurrentValue()
In this project, I have implemented a Least Squares Regression model and a Gradient Descent Optimizer using Python. The Least Squares Regression model is used for linear regression, while the Gradient Descent Optimizer is a numerical optimization algorithm that can be used to find the minimum of a given function.
The following libraries were installed: numpy statistics matplotlib
Least Squares Regression: The LeastSquaresRegression class is used to perform linear regression using the least squares method. It fits a linear model to the given data and makes predictions using the model.
Gradient Descent Optimizer: The GradientDescentOptimizer class is used to optimize a given function using gradient descent. It performs a series of optimization steps to find the minimum of the function.
./ python my_linear_regression.py
deniran_o
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