Dimensionality reduction

[Diegomangasco]

Describe your change:

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the commit message contains Fixes: #{$ISSUE_NO}.

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

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[rohan472000]

@Diegomangasco , run ruff . to rectify the failure of ruff

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[algorithms-keeper]

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

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[cclauss]

Pytest discovery is not finding/running these

See the GitHub Actions output.

machine_learning/data_transformations.py .. [ 54%]
machine_learning/decision_tree.py . [ 54%]
machine_learning/k_means_clust.py . [ 54%]
machine_learning/k_nearest_neighbours.py .. [ 55%]
machine_learning/linear_discriminant_analysis.py ....... [ 55%]
machine_learning/multilayer_perceptron_classifier.py . [ 55%]
machine_learning/scoring_functions.py ..... [ 56%]
machine_learning/self_organizing_map.py .. [ 56%]
machine_learning/similarity_search.py ... [ 56%]
machine_learning/support_vector_machines.py ... [ 56%]
machine_learning/word_frequency_functions.py .... [ 57%]
machine_learning/xgboost_classifier.py .. [ 57%]
machine_learning/xgboost_regressor.py ... [ 57%]
machine_learning/forecasting/run.py ..... [ 57%]
machine_learning/local_weighted_learning/local_weighted_learning.py .... [ 58%]

[rohan472000]

why it is not running these???

[Diegomangasco]

@rohan472000@cclauss@chriso345
I have found an interesting information on this thread: https://stats.stackexchange.com/questions/30348/is-it-acceptable-to-reverse-a-sign-of-a-principal-component-score.
I report here the answer:

The signs of the eigenvectors are essentially arbitrary; if a colleague were to run the same analyses on the same data but on a different computer it would not be surprising to see one or both eigenvectors (your PC1a, & PC2a) to have different signs. Computing the PCA using the same data on the same computer but via different software packages can also have the same effect.
As such you can quite happily change the sign of the eigenvectors without altering the PCA.

This can be helpful.

[rohan472000]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

Yes because the projection could be done in whatever direction (plus or minus sign), the important things are the values.

[Diegomangasco]

I think that the round errors may due to machine rounding.
Because all the operation that I wrote are mathematical-deterministic.

[rohan472000]

yes but values that we are getting are also different.

[Diegomangasco]

yes but values that we are getting are also different.

Can you provide me the input you gave?
Because it seems strange that math operation, like dot product between matrices, give different results.

[rohan472000]

deftest_pca():
features=np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions=2expected_output=np.array([[6.92820323, 8.66025404, 10.39230485], [3., 3., 3.]])
output=principal_component_analysis(features, dimensions)
assertnp.allclose(expected_output, output), f"Expected {expected_output}, but got {output}"test_pca()

error

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]], but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@rohan472000@cclauss
I share some trials on my local machine:

It seems to be deterministic, maybe there are some problems with doctests (?)

[Diegomangasco]

@rohan472000@cclauss
I switched from doctest to an homemade test for pca, now all build tests pass.

[rohan472000]

I'm not sure about using different function to test the various cases comes under a good practice or not for this repo, other than that everything looks fine to me.

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[rohan472000]

why it is not running these???

[Shaquum]

**_> projected_data = linear_discriminant_analysis(features, labels, classes, 3)

except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." ### " {output}"

if name == "main":
import doctest

doctest.testmod()_**

[Shaquum]

projected_data = linear_discriminant_analysis(features, labels, classes, 3)
except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." {output}"

if name ==dismissed "main":

[Diegomangasco]

Describe your change:

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the commit message contains Fixes: #{$ISSUE_NO}.

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[rohan472000]

@Diegomangasco , run ruff . to rectify the failure of ruff

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[algorithms-keeper]

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

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[cclauss]

Pytest discovery is not finding/running these

See the GitHub Actions output.

machine_learning/data_transformations.py .. [ 54%]
machine_learning/decision_tree.py . [ 54%]
machine_learning/k_means_clust.py . [ 54%]
machine_learning/k_nearest_neighbours.py .. [ 55%]
machine_learning/linear_discriminant_analysis.py ....... [ 55%]
machine_learning/multilayer_perceptron_classifier.py . [ 55%]
machine_learning/scoring_functions.py ..... [ 56%]
machine_learning/self_organizing_map.py .. [ 56%]
machine_learning/similarity_search.py ... [ 56%]
machine_learning/support_vector_machines.py ... [ 56%]
machine_learning/word_frequency_functions.py .... [ 57%]
machine_learning/xgboost_classifier.py .. [ 57%]
machine_learning/xgboost_regressor.py ... [ 57%]
machine_learning/forecasting/run.py ..... [ 57%]
machine_learning/local_weighted_learning/local_weighted_learning.py .... [ 58%]

[rohan472000]

why it is not running these???

[Diegomangasco]

@rohan472000@cclauss@chriso345
I have found an interesting information on this thread: https://stats.stackexchange.com/questions/30348/is-it-acceptable-to-reverse-a-sign-of-a-principal-component-score.
I report here the answer:

The signs of the eigenvectors are essentially arbitrary; if a colleague were to run the same analyses on the same data but on a different computer it would not be surprising to see one or both eigenvectors (your PC1a, & PC2a) to have different signs. Computing the PCA using the same data on the same computer but via different software packages can also have the same effect.
As such you can quite happily change the sign of the eigenvectors without altering the PCA.

This can be helpful.

[rohan472000]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

Yes because the projection could be done in whatever direction (plus or minus sign), the important things are the values.

[Diegomangasco]

I think that the round errors may due to machine rounding.
Because all the operation that I wrote are mathematical-deterministic.

[rohan472000]

yes but values that we are getting are also different.

[Diegomangasco]

yes but values that we are getting are also different.

Can you provide me the input you gave?
Because it seems strange that math operation, like dot product between matrices, give different results.

[rohan472000]

deftest_pca():
features=np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions=2expected_output=np.array([[6.92820323, 8.66025404, 10.39230485], [3., 3., 3.]])
output=principal_component_analysis(features, dimensions)
assertnp.allclose(expected_output, output), f"Expected {expected_output}, but got {output}"test_pca()

error

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]], but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@rohan472000@cclauss
I share some trials on my local machine:

It seems to be deterministic, maybe there are some problems with doctests (?)

[Diegomangasco]

@rohan472000@cclauss
I switched from doctest to an homemade test for pca, now all build tests pass.

[rohan472000]

I'm not sure about using different function to test the various cases comes under a good practice or not for this repo, other than that everything looks fine to me.

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[rohan472000]

why it is not running these???

[Shaquum]

**_> projected_data = linear_discriminant_analysis(features, labels, classes, 3)

except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." ### " {output}"

if name == "main":
import doctest

doctest.testmod()_**

[Shaquum]

projected_data = linear_discriminant_analysis(features, labels, classes, 3)
except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." {output}"

if name ==dismissed "main":

[Diegomangasco]

Describe your change:

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the commit message contains Fixes: #{$ISSUE_NO}.

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[rohan472000]

@Diegomangasco , run ruff . to rectify the failure of ruff

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[algorithms-keeper]

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

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[cclauss]

Pytest discovery is not finding/running these

See the GitHub Actions output.

machine_learning/data_transformations.py .. [ 54%]
machine_learning/decision_tree.py . [ 54%]
machine_learning/k_means_clust.py . [ 54%]
machine_learning/k_nearest_neighbours.py .. [ 55%]
machine_learning/linear_discriminant_analysis.py ....... [ 55%]
machine_learning/multilayer_perceptron_classifier.py . [ 55%]
machine_learning/scoring_functions.py ..... [ 56%]
machine_learning/self_organizing_map.py .. [ 56%]
machine_learning/similarity_search.py ... [ 56%]
machine_learning/support_vector_machines.py ... [ 56%]
machine_learning/word_frequency_functions.py .... [ 57%]
machine_learning/xgboost_classifier.py .. [ 57%]
machine_learning/xgboost_regressor.py ... [ 57%]
machine_learning/forecasting/run.py ..... [ 57%]
machine_learning/local_weighted_learning/local_weighted_learning.py .... [ 58%]

[rohan472000]

why it is not running these???

[Diegomangasco]

@rohan472000@cclauss@chriso345
I have found an interesting information on this thread: https://stats.stackexchange.com/questions/30348/is-it-acceptable-to-reverse-a-sign-of-a-principal-component-score.
I report here the answer:

The signs of the eigenvectors are essentially arbitrary; if a colleague were to run the same analyses on the same data but on a different computer it would not be surprising to see one or both eigenvectors (your PC1a, & PC2a) to have different signs. Computing the PCA using the same data on the same computer but via different software packages can also have the same effect.
As such you can quite happily change the sign of the eigenvectors without altering the PCA.

This can be helpful.

[rohan472000]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

Yes because the projection could be done in whatever direction (plus or minus sign), the important things are the values.

[Diegomangasco]

I think that the round errors may due to machine rounding.
Because all the operation that I wrote are mathematical-deterministic.

[rohan472000]

yes but values that we are getting are also different.

[Diegomangasco]

yes but values that we are getting are also different.

Can you provide me the input you gave?
Because it seems strange that math operation, like dot product between matrices, give different results.

[rohan472000]

deftest_pca():
features=np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions=2expected_output=np.array([[6.92820323, 8.66025404, 10.39230485], [3., 3., 3.]])
output=principal_component_analysis(features, dimensions)
assertnp.allclose(expected_output, output), f"Expected {expected_output}, but got {output}"test_pca()

error

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]], but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@rohan472000@cclauss
I share some trials on my local machine:

It seems to be deterministic, maybe there are some problems with doctests (?)

[Diegomangasco]

@rohan472000@cclauss
I switched from doctest to an homemade test for pca, now all build tests pass.

[rohan472000]

I'm not sure about using different function to test the various cases comes under a good practice or not for this repo, other than that everything looks fine to me.

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[rohan472000]

why it is not running these???

[Shaquum]

**_> projected_data = linear_discriminant_analysis(features, labels, classes, 3)

except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." ### " {output}"

if name == "main":
import doctest

doctest.testmod()_**

[Shaquum]

projected_data = linear_discriminant_analysis(features, labels, classes, 3)
except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." {output}"

if name ==dismissed "main":

[Diegomangasco]

Describe your change:

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the commit message contains Fixes: #{$ISSUE_NO}.

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

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algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[rohan472000]

@Diegomangasco , run ruff . to rectify the failure of ruff

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[algorithms-keeper]

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

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[cclauss]

Pytest discovery is not finding/running these

See the GitHub Actions output.

machine_learning/data_transformations.py .. [ 54%]
machine_learning/decision_tree.py . [ 54%]
machine_learning/k_means_clust.py . [ 54%]
machine_learning/k_nearest_neighbours.py .. [ 55%]
machine_learning/linear_discriminant_analysis.py ....... [ 55%]
machine_learning/multilayer_perceptron_classifier.py . [ 55%]
machine_learning/scoring_functions.py ..... [ 56%]
machine_learning/self_organizing_map.py .. [ 56%]
machine_learning/similarity_search.py ... [ 56%]
machine_learning/support_vector_machines.py ... [ 56%]
machine_learning/word_frequency_functions.py .... [ 57%]
machine_learning/xgboost_classifier.py .. [ 57%]
machine_learning/xgboost_regressor.py ... [ 57%]
machine_learning/forecasting/run.py ..... [ 57%]
machine_learning/local_weighted_learning/local_weighted_learning.py .... [ 58%]

[rohan472000]

why it is not running these???

[Diegomangasco]

@rohan472000@cclauss@chriso345
I have found an interesting information on this thread: https://stats.stackexchange.com/questions/30348/is-it-acceptable-to-reverse-a-sign-of-a-principal-component-score.
I report here the answer:

The signs of the eigenvectors are essentially arbitrary; if a colleague were to run the same analyses on the same data but on a different computer it would not be surprising to see one or both eigenvectors (your PC1a, & PC2a) to have different signs. Computing the PCA using the same data on the same computer but via different software packages can also have the same effect.
As such you can quite happily change the sign of the eigenvectors without altering the PCA.

This can be helpful.

[rohan472000]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

Yes because the projection could be done in whatever direction (plus or minus sign), the important things are the values.

[Diegomangasco]

I think that the round errors may due to machine rounding.
Because all the operation that I wrote are mathematical-deterministic.

[rohan472000]

yes but values that we are getting are also different.

[Diegomangasco]

yes but values that we are getting are also different.

Can you provide me the input you gave?
Because it seems strange that math operation, like dot product between matrices, give different results.

[rohan472000]

deftest_pca():
features=np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions=2expected_output=np.array([[6.92820323, 8.66025404, 10.39230485], [3., 3., 3.]])
output=principal_component_analysis(features, dimensions)
assertnp.allclose(expected_output, output), f"Expected {expected_output}, but got {output}"test_pca()

error

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]], but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@rohan472000@cclauss
I share some trials on my local machine:

It seems to be deterministic, maybe there are some problems with doctests (?)

[Diegomangasco]

@rohan472000@cclauss
I switched from doctest to an homemade test for pca, now all build tests pass.

[rohan472000]

I'm not sure about using different function to test the various cases comes under a good practice or not for this repo, other than that everything looks fine to me.

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[rohan472000]

why it is not running these???

[Shaquum]

**_> projected_data = linear_discriminant_analysis(features, labels, classes, 3)

except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." ### " {output}"

if name == "main":
import doctest

doctest.testmod()_**

[Shaquum]

projected_data = linear_discriminant_analysis(features, labels, classes, 3)
except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." {output}"

if name ==dismissed "main":

[Diegomangasco]

Describe your change:

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the commit message contains Fixes: #{$ISSUE_NO}.

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[rohan472000]

@Diegomangasco , run ruff . to rectify the failure of ruff

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[algorithms-keeper]

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

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[cclauss]

Pytest discovery is not finding/running these

See the GitHub Actions output.

machine_learning/data_transformations.py .. [ 54%]
machine_learning/decision_tree.py . [ 54%]
machine_learning/k_means_clust.py . [ 54%]
machine_learning/k_nearest_neighbours.py .. [ 55%]
machine_learning/linear_discriminant_analysis.py ....... [ 55%]
machine_learning/multilayer_perceptron_classifier.py . [ 55%]
machine_learning/scoring_functions.py ..... [ 56%]
machine_learning/self_organizing_map.py .. [ 56%]
machine_learning/similarity_search.py ... [ 56%]
machine_learning/support_vector_machines.py ... [ 56%]
machine_learning/word_frequency_functions.py .... [ 57%]
machine_learning/xgboost_classifier.py .. [ 57%]
machine_learning/xgboost_regressor.py ... [ 57%]
machine_learning/forecasting/run.py ..... [ 57%]
machine_learning/local_weighted_learning/local_weighted_learning.py .... [ 58%]

[rohan472000]

why it is not running these???

[Diegomangasco]

@rohan472000@cclauss@chriso345
I have found an interesting information on this thread: https://stats.stackexchange.com/questions/30348/is-it-acceptable-to-reverse-a-sign-of-a-principal-component-score.
I report here the answer:

The signs of the eigenvectors are essentially arbitrary; if a colleague were to run the same analyses on the same data but on a different computer it would not be surprising to see one or both eigenvectors (your PC1a, & PC2a) to have different signs. Computing the PCA using the same data on the same computer but via different software packages can also have the same effect.
As such you can quite happily change the sign of the eigenvectors without altering the PCA.

This can be helpful.

[rohan472000]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

Yes because the projection could be done in whatever direction (plus or minus sign), the important things are the values.

[Diegomangasco]

I think that the round errors may due to machine rounding.
Because all the operation that I wrote are mathematical-deterministic.

[rohan472000]

yes but values that we are getting are also different.

[Diegomangasco]

yes but values that we are getting are also different.

Can you provide me the input you gave?
Because it seems strange that math operation, like dot product between matrices, give different results.

[rohan472000]

deftest_pca():
features=np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions=2expected_output=np.array([[6.92820323, 8.66025404, 10.39230485], [3., 3., 3.]])
output=principal_component_analysis(features, dimensions)
assertnp.allclose(expected_output, output), f"Expected {expected_output}, but got {output}"test_pca()

error

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]], but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@rohan472000@cclauss
I share some trials on my local machine:

It seems to be deterministic, maybe there are some problems with doctests (?)

[Diegomangasco]

@rohan472000@cclauss
I switched from doctest to an homemade test for pca, now all build tests pass.

[rohan472000]

I'm not sure about using different function to test the various cases comes under a good practice or not for this repo, other than that everything looks fine to me.

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[rohan472000]

why it is not running these???

[Shaquum]

**_> projected_data = linear_discriminant_analysis(features, labels, classes, 3)

except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." ### " {output}"

if name == "main":
import doctest

doctest.testmod()_**

[Shaquum]

projected_data = linear_discriminant_analysis(features, labels, classes, 3)
except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." {output}"

if name ==dismissed "main":

[Diegomangasco]

Describe your change:

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the commit message contains Fixes: #{$ISSUE_NO}.

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[rohan472000]

@Diegomangasco , run ruff . to rectify the failure of ruff

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[algorithms-keeper]

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

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[cclauss]

Pytest discovery is not finding/running these

See the GitHub Actions output.

machine_learning/data_transformations.py .. [ 54%]
machine_learning/decision_tree.py . [ 54%]
machine_learning/k_means_clust.py . [ 54%]
machine_learning/k_nearest_neighbours.py .. [ 55%]
machine_learning/linear_discriminant_analysis.py ....... [ 55%]
machine_learning/multilayer_perceptron_classifier.py . [ 55%]
machine_learning/scoring_functions.py ..... [ 56%]
machine_learning/self_organizing_map.py .. [ 56%]
machine_learning/similarity_search.py ... [ 56%]
machine_learning/support_vector_machines.py ... [ 56%]
machine_learning/word_frequency_functions.py .... [ 57%]
machine_learning/xgboost_classifier.py .. [ 57%]
machine_learning/xgboost_regressor.py ... [ 57%]
machine_learning/forecasting/run.py ..... [ 57%]
machine_learning/local_weighted_learning/local_weighted_learning.py .... [ 58%]

[rohan472000]

why it is not running these???

[Diegomangasco]

@rohan472000@cclauss@chriso345
I have found an interesting information on this thread: https://stats.stackexchange.com/questions/30348/is-it-acceptable-to-reverse-a-sign-of-a-principal-component-score.
I report here the answer:

The signs of the eigenvectors are essentially arbitrary; if a colleague were to run the same analyses on the same data but on a different computer it would not be surprising to see one or both eigenvectors (your PC1a, & PC2a) to have different signs. Computing the PCA using the same data on the same computer but via different software packages can also have the same effect.
As such you can quite happily change the sign of the eigenvectors without altering the PCA.

This can be helpful.

[rohan472000]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

Yes because the projection could be done in whatever direction (plus or minus sign), the important things are the values.

[Diegomangasco]

I think that the round errors may due to machine rounding.
Because all the operation that I wrote are mathematical-deterministic.

[rohan472000]

yes but values that we are getting are also different.

[Diegomangasco]

yes but values that we are getting are also different.

Can you provide me the input you gave?
Because it seems strange that math operation, like dot product between matrices, give different results.

[rohan472000]

deftest_pca():
features=np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions=2expected_output=np.array([[6.92820323, 8.66025404, 10.39230485], [3., 3., 3.]])
output=principal_component_analysis(features, dimensions)
assertnp.allclose(expected_output, output), f"Expected {expected_output}, but got {output}"test_pca()

error

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]], but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@rohan472000@cclauss
I share some trials on my local machine:

It seems to be deterministic, maybe there are some problems with doctests (?)

[Diegomangasco]

@rohan472000@cclauss
I switched from doctest to an homemade test for pca, now all build tests pass.

[rohan472000]

I'm not sure about using different function to test the various cases comes under a good practice or not for this repo, other than that everything looks fine to me.

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[rohan472000]

why it is not running these???

[Shaquum]

**_> projected_data = linear_discriminant_analysis(features, labels, classes, 3)

except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." ### " {output}"

if name == "main":
import doctest

doctest.testmod()_**

[Shaquum]

projected_data = linear_discriminant_analysis(features, labels, classes, 3)
except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." {output}"

if name ==dismissed "main":

[Diegomangasco]

Describe your change:

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the commit message contains Fixes: #{$ISSUE_NO}.

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

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[rohan472000]

@Diegomangasco , run ruff . to rectify the failure of ruff

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[algorithms-keeper]

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

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[cclauss]

Pytest discovery is not finding/running these

See the GitHub Actions output.

machine_learning/data_transformations.py .. [ 54%]
machine_learning/decision_tree.py . [ 54%]
machine_learning/k_means_clust.py . [ 54%]
machine_learning/k_nearest_neighbours.py .. [ 55%]
machine_learning/linear_discriminant_analysis.py ....... [ 55%]
machine_learning/multilayer_perceptron_classifier.py . [ 55%]
machine_learning/scoring_functions.py ..... [ 56%]
machine_learning/self_organizing_map.py .. [ 56%]
machine_learning/similarity_search.py ... [ 56%]
machine_learning/support_vector_machines.py ... [ 56%]
machine_learning/word_frequency_functions.py .... [ 57%]
machine_learning/xgboost_classifier.py .. [ 57%]
machine_learning/xgboost_regressor.py ... [ 57%]
machine_learning/forecasting/run.py ..... [ 57%]
machine_learning/local_weighted_learning/local_weighted_learning.py .... [ 58%]

[rohan472000]

why it is not running these???

[Diegomangasco]

@rohan472000@cclauss@chriso345
I have found an interesting information on this thread: https://stats.stackexchange.com/questions/30348/is-it-acceptable-to-reverse-a-sign-of-a-principal-component-score.
I report here the answer:

The signs of the eigenvectors are essentially arbitrary; if a colleague were to run the same analyses on the same data but on a different computer it would not be surprising to see one or both eigenvectors (your PC1a, & PC2a) to have different signs. Computing the PCA using the same data on the same computer but via different software packages can also have the same effect.
As such you can quite happily change the sign of the eigenvectors without altering the PCA.

This can be helpful.

[rohan472000]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

Yes because the projection could be done in whatever direction (plus or minus sign), the important things are the values.

[Diegomangasco]

I think that the round errors may due to machine rounding.
Because all the operation that I wrote are mathematical-deterministic.

[rohan472000]

yes but values that we are getting are also different.

[Diegomangasco]

yes but values that we are getting are also different.

Can you provide me the input you gave?
Because it seems strange that math operation, like dot product between matrices, give different results.

[rohan472000]

deftest_pca():
features=np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions=2expected_output=np.array([[6.92820323, 8.66025404, 10.39230485], [3., 3., 3.]])
output=principal_component_analysis(features, dimensions)
assertnp.allclose(expected_output, output), f"Expected {expected_output}, but got {output}"test_pca()

error

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]], but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@rohan472000@cclauss
I share some trials on my local machine:

It seems to be deterministic, maybe there are some problems with doctests (?)

[Diegomangasco]

@rohan472000@cclauss
I switched from doctest to an homemade test for pca, now all build tests pass.

[rohan472000]

I'm not sure about using different function to test the various cases comes under a good practice or not for this repo, other than that everything looks fine to me.

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[rohan472000]

why it is not running these???

[Shaquum]

**_> projected_data = linear_discriminant_analysis(features, labels, classes, 3)

except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." ### " {output}"

if name == "main":
import doctest

doctest.testmod()_**

[Shaquum]

projected_data = linear_discriminant_analysis(features, labels, classes, 3)
except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." {output}"

if name ==dismissed "main":

[Diegomangasco]

Describe your change:

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the commit message contains Fixes: #{$ISSUE_NO}.

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[rohan472000]

@Diegomangasco , run ruff . to rectify the failure of ruff

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

---

algorithms-keeper commands and options

algorithms-keeper actions can be triggered by commenting on this PR:

• @algorithms-keeper review to trigger the checks for only added pull request files
• @algorithms-keeper review-all to trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.

NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.

[algorithms-keeper]

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

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[cclauss]

Pytest discovery is not finding/running these

See the GitHub Actions output.

machine_learning/data_transformations.py .. [ 54%]
machine_learning/decision_tree.py . [ 54%]
machine_learning/k_means_clust.py . [ 54%]
machine_learning/k_nearest_neighbours.py .. [ 55%]
machine_learning/linear_discriminant_analysis.py ....... [ 55%]
machine_learning/multilayer_perceptron_classifier.py . [ 55%]
machine_learning/scoring_functions.py ..... [ 56%]
machine_learning/self_organizing_map.py .. [ 56%]
machine_learning/similarity_search.py ... [ 56%]
machine_learning/support_vector_machines.py ... [ 56%]
machine_learning/word_frequency_functions.py .... [ 57%]
machine_learning/xgboost_classifier.py .. [ 57%]
machine_learning/xgboost_regressor.py ... [ 57%]
machine_learning/forecasting/run.py ..... [ 57%]
machine_learning/local_weighted_learning/local_weighted_learning.py .... [ 58%]

[rohan472000]

why it is not running these???

[Diegomangasco]

@rohan472000@cclauss@chriso345
I have found an interesting information on this thread: https://stats.stackexchange.com/questions/30348/is-it-acceptable-to-reverse-a-sign-of-a-principal-component-score.
I report here the answer:

The signs of the eigenvectors are essentially arbitrary; if a colleague were to run the same analyses on the same data but on a different computer it would not be surprising to see one or both eigenvectors (your PC1a, & PC2a) to have different signs. Computing the PCA using the same data on the same computer but via different software packages can also have the same effect.
As such you can quite happily change the sign of the eigenvectors without altering the PCA.

This can be helpful.

[rohan472000]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@Diegomangasco , I also tried but getting -ve sign and some different outputs everytime.

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]],
but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

Yes because the projection could be done in whatever direction (plus or minus sign), the important things are the values.

[Diegomangasco]

I think that the round errors may due to machine rounding.
Because all the operation that I wrote are mathematical-deterministic.

[rohan472000]

yes but values that we are getting are also different.

[Diegomangasco]

yes but values that we are getting are also different.

Can you provide me the input you gave?
Because it seems strange that math operation, like dot product between matrices, give different results.

[rohan472000]

deftest_pca():
features=np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions=2expected_output=np.array([[6.92820323, 8.66025404, 10.39230485], [3., 3., 3.]])
output=principal_component_analysis(features, dimensions)
assertnp.allclose(expected_output, output), f"Expected {expected_output}, but got {output}"test_pca()

error

AssertionError: Expected [[ 6.92820323 8.66025404 10.39230485]
[ 3. 3. 3. ]], but got [[ -6.92820323 -8.66025404 -10.39230485]
[ -2.2719232 -2.2719232 -2.2719232 ]]

[Diegomangasco]

@rohan472000@cclauss
I share some trials on my local machine:

It seems to be deterministic, maybe there are some problems with doctests (?)

[Diegomangasco]

@rohan472000@cclauss
I switched from doctest to an homemade test for pca, now all build tests pass.

[rohan472000]

I'm not sure about using different function to test the various cases comes under a good practice or not for this repo, other than that everything looks fine to me.

[rohan472000]

"""

features = np.array([[1, 2, 3], [4, 5, 6]])
labels = np.array([0, 1, 0])
covariance_between_classes(features, labels, 2)
output : array([[-1.5, -1.5],[-1.5, -1.5]])

"""

[rohan472000]

why it is not running these???

[Shaquum]

**_> projected_data = linear_discriminant_analysis(features, labels, classes, 3)

except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." ### " {output}"

if name == "main":
import doctest

doctest.testmod()_**

[Shaquum]

projected_data = linear_discriminant_analysis(features, labels, classes, 3)
except AssertionError:
pass
else:
raise AssertionError("Did not raise AssertionError for dimensions > features")

def test_principal_component_analysis() -> None:
features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
dimensions = 2
expected_output = np.array([[6.92820323, 8.66025404, 10.39230485], [3.0, 3.0, 3.0]])
output = principal_component_analysis(features, labels)
assert np.allclose(
expected_output, output
), f"Expected {expected_output}." {output}"

if name ==dismissed "main":