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06-summing-up.html
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<!DOCTYPE html>
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<title>Software Carpentry: Advanced NumPy</title>
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<a href="index.html"><h1 class="title">Advanced NumPy</h1></a>
<h2 class="subtitle">Summing up</h2>
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<h2 id="quantile-normalisation"><span class="glyphicon glyphicon-pencil"></span>Quantile normalisation</h2>
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<p><a href="https://en.wikipedia.org/wiki/Quantile_normalization">Quantile Normalization</a> is a method to align distributions. Implement it using NumPy axis-wise operations and fancy indexing.</p>
<p>Hint: look for documentation for <code>np.sort</code>, and <code>np.argsort</code>.*</p>
<pre><code>def qnorm(x):
"""Quantile normalize an input matrix.
Parameters
----------
x : 2D array of float, shape (M, N)
The input data, with each column being a
distribution to normalize.
Returns
-------
xn : 2D array of float, shape (M, N)
The normalized data.
"""
return x
import numpy as np
data = np.array([[5, 4, 3],
[2, 1, 4],
[3, 5, 6],
[4, 2, 7]])
from numpy.testing import assert_array_almost_equal
assert_array_almost_equal(qnorm(data), results, decimal=2)</code></pre>
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