added mean absolute percentage error - #10464

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Describe your change:

I added mean absolute percentage error to the loss function directory. I used matrix operations instead of for loops to enhance the performance of the algorithm. Looking forward to getting this pull request merged and also to acquire the hacktoberfest and hacktoberfest-accepted badge..

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

Checklist:

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  • 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.
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  • 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 description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".

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Comment threadmachine_learning/loss_functions/mean_absolute_percentage_error.py Outdated
"""


def mean_absolute_percentage_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:

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

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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?

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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
I have added my code into loss_functions.py.

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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?

Comment threadmachine_learning/loss_functions.py Outdated
@Humzafazal72

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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?

Done as requested.

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Comment threadmachine_learning/loss_functions.py Outdated
Comment threadmachine_learning/loss_functions.py Outdated
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Comment threadmachine_learning/loss_functions.py Outdated
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added mean absolute percentage error - #10464
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Describe your change:
I added mean absolute percentage error to the loss function directory. I used matrix operations instead of for loops to enhance the performance of the algorithm. Looking forward to getting this pull request merged and also to acquire the hacktoberfest and hacktoberfest-accepted badge..
  •  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 description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".
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Comment threadmachine_learning/loss_functions/mean_absolute_percentage_error.py
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"""


def mean_absolute_percentage_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
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As there is no test file in this pull request nor any test function or class in the file machine_learning/loss_functions/mean_absolute_percentage_error.py, please provide doctest for the function mean_absolute_percentage_error
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?

I have added my code into loss_functions.py.
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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?
Comment threadmachine_learning/loss_functions.py
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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?
Done as requested.
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added mean absolute percentage error - #10464
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Describe your change:
I added mean absolute percentage error to the loss function directory. I used matrix operations instead of for loops to enhance the performance of the algorithm. Looking forward to getting this pull request merged and also to acquire the hacktoberfest and hacktoberfest-accepted badge..
  •  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 description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".
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Comment threadmachine_learning/loss_functions/mean_absolute_percentage_error.py
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"""


def mean_absolute_percentage_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
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As there is no test file in this pull request nor any test function or class in the file machine_learning/loss_functions/mean_absolute_percentage_error.py, please provide doctest for the function mean_absolute_percentage_error
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?

I have added my code into loss_functions.py.
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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?
Comment threadmachine_learning/loss_functions.py
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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?
Done as requested.
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added mean absolute percentage error - #10464
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Describe your change:
I added mean absolute percentage error to the loss function directory. I used matrix operations instead of for loops to enhance the performance of the algorithm. Looking forward to getting this pull request merged and also to acquire the hacktoberfest and hacktoberfest-accepted badge..
  •  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.
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  •  All functions and variable names follow Python naming conventions.
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Comment threadmachine_learning/loss_functions/mean_absolute_percentage_error.py
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As there is no test file in this pull request nor any test function or class in the file machine_learning/loss_functions/mean_absolute_percentage_error.py, please provide doctest for the function mean_absolute_percentage_error
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?

I have added my code into loss_functions.py.
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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?
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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?
Done as requested.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
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var __m = "youtube.com";
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added mean absolute percentage error - #10464
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Describe your change:
I added mean absolute percentage error to the loss function directory. I used matrix operations instead of for loops to enhance the performance of the algorithm. Looking forward to getting this pull request merged and also to acquire the hacktoberfest and hacktoberfest-accepted badge..
  •  Add an algorithm?
  •  Fix a bug or typo in an existing algorithm?
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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.
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  •  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 description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".
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Comment threadmachine_learning/loss_functions/mean_absolute_percentage_error.py
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def mean_absolute_percentage_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
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As there is no test file in this pull request nor any test function or class in the file machine_learning/loss_functions/mean_absolute_percentage_error.py, please provide doctest for the function mean_absolute_percentage_error
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?

I have added my code into loss_functions.py.
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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?
Comment threadmachine_learning/loss_functions.py
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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?
Done as requested.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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added mean absolute percentage error - #10464
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Describe your change:
I added mean absolute percentage error to the loss function directory. I used matrix operations instead of for loops to enhance the performance of the algorithm. Looking forward to getting this pull request merged and also to acquire the hacktoberfest and hacktoberfest-accepted badge..
  •  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 description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".
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Comment threadmachine_learning/loss_functions/mean_absolute_percentage_error.py
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"""


def mean_absolute_percentage_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
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As there is no test file in this pull request nor any test function or class in the file machine_learning/loss_functions/mean_absolute_percentage_error.py, please provide doctest for the function mean_absolute_percentage_error
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?

I have added my code into loss_functions.py.
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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?
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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?
Done as requested.
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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added mean absolute percentage error - #10464
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Describe your change:
I added mean absolute percentage error to the loss function directory. I used matrix operations instead of for loops to enhance the performance of the algorithm. Looking forward to getting this pull request merged and also to acquire the hacktoberfest and hacktoberfest-accepted badge..
  •  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 description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".
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Comment threadmachine_learning/loss_functions/mean_absolute_percentage_error.py
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"""


def mean_absolute_percentage_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
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As there is no test file in this pull request nor any test function or class in the file machine_learning/loss_functions/mean_absolute_percentage_error.py, please provide doctest for the function mean_absolute_percentage_error
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?

I have added my code into loss_functions.py.
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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?
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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?
Done as requested.
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, 'i');
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}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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added mean absolute percentage error - #10464
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Describe your change:
I added mean absolute percentage error to the loss function directory. I used matrix operations instead of for loops to enhance the performance of the algorithm. Looking forward to getting this pull request merged and also to acquire the hacktoberfest and hacktoberfest-accepted badge..
  •  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 description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".
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Comment threadmachine_learning/loss_functions/mean_absolute_percentage_error.py
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"""


def mean_absolute_percentage_error(y_true: np.ndarray, y_pred: np.ndarray) -> float:
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As there is no test file in this pull request nor any test function or class in the file machine_learning/loss_functions/mean_absolute_percentage_error.py, please provide doctest for the function mean_absolute_percentage_error
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?
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All loss function files were consolidated into machine_learning/loss_functions.py in #10737. Could you move your new code into that file?

I have added my code into loss_functions.py.
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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?
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Could you move your function to the bottom of the file, right below mean_squared_logarithmic_error?
Done as requested.
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