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Wrap lines that go beyond GitHub Editor (#1925)

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* Wrap lines that go beyond GiHub Editor

* flake8 --count --select=E501 --max-line-length=127

* updating DIRECTORY.md

* Update strassen_matrix_multiplication.py

* fixup! Format Python code with psf/black push

* Update decision_tree.py

Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>
This commit is contained in:
Christian Clauss
2020-05-01 23:36:35 +02:00
committed by GitHub
parent bcaa88b26c
commit 6acd7fb5ce
19 changed files with 161 additions and 82 deletions
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29
machine_learning/decision_tree.py
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@ -20,15 +20,21 @@ class Decision_Tree:
mean_squared_error:
@param labels: a one dimensional numpy array
@param prediction: a floating point value
return value: mean_squared_error calculates the error if prediction is used to estimate the labels
return value: mean_squared_error calculates the error if prediction is used to
estimate the labels
>>> tester = Decision_Tree()
>>> test_labels = np.array([1,2,3,4,5,6,7,8,9,10])
>>> test_prediction = np.float(6)
>>> assert tester.mean_squared_error(test_labels, test_prediction) == Test_Decision_Tree.helper_mean_squared_error_test(test_labels, test_prediction)
>>> tester.mean_squared_error(test_labels, test_prediction) == (
... Test_Decision_Tree.helper_mean_squared_error_test(test_labels,
... test_prediction))
True
>>> test_labels = np.array([1,2,3])
>>> test_prediction = np.float(2)
>>> assert tester.mean_squared_error(test_labels, test_prediction) == Test_Decision_Tree.helper_mean_squared_error_test(test_labels, test_prediction)
>>> tester.mean_squared_error(test_labels, test_prediction) == (
... Test_Decision_Tree.helper_mean_squared_error_test(test_labels,
... test_prediction))
True
"""
if labels.ndim != 1:
print("Error: Input labels must be one dimensional")
@ -46,7 +52,8 @@ class Decision_Tree:
"""
"""
this section is to check that the inputs conform to our dimensionality constraints
this section is to check that the inputs conform to our dimensionality
constraints
"""
if X.ndim != 1:
print("Error: Input data set must be one dimensional")
@ -72,7 +79,8 @@ class Decision_Tree:
"""
loop over all possible splits for the decision tree. find the best split.
if no split exists that is less than 2 * error for the entire array
then the data set is not split and the average for the entire array is used as the predictor
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:
@ -136,7 +144,7 @@ class Test_Decision_Tree:
helper_mean_squared_error_test:
@param labels: a one dimensional numpy array
@param prediction: a floating point value
return value: helper_mean_squared_error_test calculates the mean squared error
return value: helper_mean_squared_error_test calculates the mean squared error
"""
squared_error_sum = np.float(0)
for label in labels:
@ -147,9 +155,10 @@ class Test_Decision_Tree:
def main():
"""
In this demonstration we're generating a sample data set from the sin function in numpy.
We then train a decision tree on the data set and use the decision tree to predict the
label of 10 different test values. Then the mean squared error over this test is displayed.
In this demonstration we're generating a sample data set from the sin function in
numpy. We then train a decision tree on the data set and use the decision tree to
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)