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* Replacing the generator with numpy vector operations from lu_decomposition.
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This reverts commit ad217c6616.
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/home/runner/work/Python/Python/machine_learning/k_means_clust.py:236: FutureWarning: The provided callable <function sum at 0x7f20c02034c0> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
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And
/home/runner/work/Python/Python/machine_learning/k_means_clust.py:236: FutureWarning: The provided callable <function mean at 0x7f3d7db1c5e0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
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* Replacing the generator with numpy vector operations from lu_decomposition.
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Now instead of gcd it is used: greatest_common_divisor.
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* Replacing the generator with numpy vector operations from lu_decomposition.
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* Replacing the generator with numpy vector operations from lu_decomposition.
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This reverts commit ad217c6616.
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* Replacing the generator with numpy vector operations from lu_decomposition.
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* Conolidated two scripts reverse_letters.py and reverse_long_words.py into one because of similar functionality
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* In place of calculating the factorial several times we can run a loop k times to calculate the combination
for example:
5 C 3 = 5! / (3! * (5-3)! )
= (5*4*3*2*1)/[(3*2*1)*(2*1)]
=(5*4*3)/(3*2*1)
so running a loop k times will reduce the time complexity to O(k)
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