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added ridge regression #12250

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1cb79bc
added ridge regression
ankana2113 Oct 23, 2024
b72320b
added ridge regression
ankana2113 Oct 23, 2024
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[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 23, 2024
a84d209
added ridge regression
ankana2113 Oct 23, 2024
6fc134d
added ridge regression
ankana2113 Oct 23, 2024
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[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 23, 2024
7484cda
ridge regression
ankana2113 Oct 23, 2024
b1353dd
ridge regression
ankana2113 Oct 23, 2024
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[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Oct 23, 2024
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ankana2113 Oct 23, 2024
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ankana2113 Oct 23, 2024
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[pre-commit.ci] auto fixes from pre-commit.com hooks
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commit 21fe32fcbeebfe979511f7fb3fd0591ec05dd4ea
4 changes: 2 additions & 2 deletions machine_learning/ridge_regression/model.py
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@@ -1,18 +1,18 @@
import numpy as np

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machine_learning/ridge_regression/model.py:1:1: INP001 File `machine_learning/ridge_regression/model.py` is part of an implicit namespace package. Add an `__init__.py`.
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An error occurred while parsing the file: machine_learning/ridge_regression/model.py

Traceback (most recent call last):
  File "/opt/render/project/src/algorithms_keeper/parser/python_parser.py", line 146, in parse
    reports = lint_file(
              ^^^^^^^^^^
libcst._exceptions.ParserSyntaxError: Syntax Error @ 1:1.
tokenizer error: no matching outer block for dedent

import numpy as np
^

import pandas as pd


class RidgeRegression:
def __init__(self, alpha:float=0.001, regularization_param:float=0.1, num_iterations:int=1000) -> None:

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machine_learning/ridge_regression/model.py:6:89: E501 Line too long (107 > 88)
self.alpha:float = alpha
self.regularization_param:float = regularization_param
self.num_iterations:int = num_iterations
self.theta:np.ndarray = None

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def feature_scaling(self, X:np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:

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=======

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machine_learning/ridge_regression/model.py:15:1: SyntaxError: Expected a statement
def feature_scaling(self, X):
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Please provide return type hint for the function: feature_scaling. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression/model.py, please provide doctest for the function feature_scaling

Please provide descriptive name for the parameter: X

Please provide type hint for the parameter: X

>>>>>>> d4fc2bf852ec4a023380f4ef367edefa88fd6881
mean = np.mean(X, axis=0)
Expand Down Expand Up @@ -45,7 +45,7 @@

<<<<<<< HEAD
def compute_cost(self, X:np.ndarray, y:np.ndarray) -> float:
X_scaled, _, _ = self.feature_scaling(X)
X_scaled, _, _ = self.feature_scaling(X)
=======
def compute_cost(self, X, y):
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Please provide return type hint for the function: compute_cost. If the function does not return a value, please provide the type hint as: def function() -> None:

As there is no test file in this pull request nor any test function or class in the file machine_learning/ridge_regression/model.py, please provide doctest for the function compute_cost

Please provide descriptive name for the parameter: X

Please provide type hint for the parameter: X

Please provide descriptive name for the parameter: y

Please provide type hint for the parameter: y

X_scaled, _, _ = self.feature_scaling(X)
Expand All @@ -71,7 +71,7 @@

<<<<<<< HEAD
# added bias term to the feature matrix
X = np.c_[np.ones(X.shape[0]), X]
X = np.c_[np.ones(X.shape[0]), X]
=======
# Add bias term (intercept) to the feature matrix
X = np.c_[np.ones(X.shape[0]), X]
Expand Down
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