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Add pep8-naming to pre-commit hooks and fixes incorrect naming conventions (#7062)

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* ci(pre-commit): Add pep8-naming to `pre-commit` hooks (#7038)

* refactor: Fix naming conventions (#7038)

* Update arithmetic_analysis/lu_decomposition.py

Co-authored-by: Christian Clauss <cclauss@me.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* refactor(lu_decomposition): Replace `NDArray` with `ArrayLike` (#7038)

* chore: Fix naming conventions in doctests (#7038)

* fix: Temporarily disable project euler problem 104 (#7069)

* chore: Fix naming conventions in doctests (#7038)

Co-authored-by: Christian Clauss <cclauss@me.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Caeden
2022-10-12 23:54:20 +01:00
committed by GitHub
parent e2cd982b11
commit 07e991d553
140 changed files with 1552 additions and 1536 deletions
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54
machine_learning/decision_tree.py
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@ -6,7 +6,7 @@ Output: The decision tree maps a real number input to a real number output.
import numpy as np
class Decision_Tree:
class DecisionTree:
def __init__(self, depth=5, min_leaf_size=5):
self.depth = depth
self.decision_boundary = 0
@ -22,17 +22,17 @@ class Decision_Tree:
@param prediction: a floating point value
return value: mean_squared_error calculates the error if prediction is used to
estimate the labels
>>> tester = Decision_Tree()
>>> tester = DecisionTree()
>>> test_labels = np.array([1,2,3,4,5,6,7,8,9,10])
>>> test_prediction = np.float(6)
>>> tester.mean_squared_error(test_labels, test_prediction) == (
... Test_Decision_Tree.helper_mean_squared_error_test(test_labels,
... TestDecisionTree.helper_mean_squared_error_test(test_labels,
... test_prediction))
True
>>> test_labels = np.array([1,2,3])
>>> test_prediction = np.float(2)
>>> tester.mean_squared_error(test_labels, test_prediction) == (
... Test_Decision_Tree.helper_mean_squared_error_test(test_labels,
... TestDecisionTree.helper_mean_squared_error_test(test_labels,
... test_prediction))
True
"""
@ -41,10 +41,10 @@ class Decision_Tree:
return np.mean((labels - prediction) ** 2)
def train(self, X, y):
def train(self, x, y):
"""
train:
@param X: a one dimensional numpy array
@param x: a one dimensional numpy array
@param y: a one dimensional numpy array.
The contents of y are the labels for the corresponding X values
@ -55,17 +55,17 @@ class Decision_Tree:
this section is to check that the inputs conform to our dimensionality
constraints
"""
if X.ndim != 1:
if x.ndim != 1:
print("Error: Input data set must be one dimensional")
return
if len(X) != len(y):
if len(x) != len(y):
print("Error: X and y have different lengths")
return
if y.ndim != 1:
print("Error: Data set labels must be one dimensional")
return
if len(X) < 2 * self.min_leaf_size:
if len(x) < 2 * self.min_leaf_size:
self.prediction = np.mean(y)
return
@ -74,7 +74,7 @@ class Decision_Tree:
return
best_split = 0
min_error = self.mean_squared_error(X, np.mean(y)) * 2
min_error = self.mean_squared_error(x, np.mean(y)) * 2
"""
loop over all possible splits for the decision tree. find the best split.
@ -82,34 +82,34 @@ class Decision_Tree:
then the data set is not split and the average for the entire array is used as
the predictor
"""
for i in range(len(X)):
if len(X[:i]) < self.min_leaf_size:
for i in range(len(x)):
if len(x[:i]) < self.min_leaf_size:
continue
elif len(X[i:]) < self.min_leaf_size:
elif len(x[i:]) < self.min_leaf_size:
continue
else:
error_left = self.mean_squared_error(X[:i], np.mean(y[:i]))
error_right = self.mean_squared_error(X[i:], np.mean(y[i:]))
error_left = self.mean_squared_error(x[:i], np.mean(y[:i]))
error_right = self.mean_squared_error(x[i:], np.mean(y[i:]))
error = error_left + error_right
if error < min_error:
best_split = i
min_error = error
if best_split != 0:
left_X = X[:best_split]
left_x = x[:best_split]
left_y = y[:best_split]
right_X = X[best_split:]
right_x = x[best_split:]
right_y = y[best_split:]
self.decision_boundary = X[best_split]
self.left = Decision_Tree(
self.decision_boundary = x[best_split]
self.left = DecisionTree(
depth=self.depth - 1, min_leaf_size=self.min_leaf_size
)
self.right = Decision_Tree(
self.right = DecisionTree(
depth=self.depth - 1, min_leaf_size=self.min_leaf_size
)
self.left.train(left_X, left_y)
self.right.train(right_X, right_y)
self.left.train(left_x, left_y)
self.right.train(right_x, right_y)
else:
self.prediction = np.mean(y)
@ -134,7 +134,7 @@ class Decision_Tree:
return None
class Test_Decision_Tree:
class TestDecisionTree:
"""Decision Tres test class"""
@staticmethod
@ -159,11 +159,11 @@ def main():
predict the label of 10 different test values. Then the mean squared error over
this test is displayed.
"""
X = np.arange(-1.0, 1.0, 0.005)
y = np.sin(X)
x = np.arange(-1.0, 1.0, 0.005)
y = np.sin(x)
tree = Decision_Tree(depth=10, min_leaf_size=10)
tree.train(X, y)
tree = DecisionTree(depth=10, min_leaf_size=10)
tree.train(x, y)
test_cases = (np.random.rand(10) * 2) - 1
predictions = np.array([tree.predict(x) for x in test_cases])