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added alignment.png to align_files folder
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docs/tutorials/align.rst

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@@ -21,14 +21,16 @@ three distinct datasets. Each has a similar shape (an S), but are scaled
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and rotated differently. Aligning these datasets finds the
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transformation that minimizes the distance between them.
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.. image:: align_files/alignment.png
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Import Hypertools
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-----------------
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.. code:: ipython3
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import hypertools as hyp
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import numpy as np
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%matplotlib inline
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Load your data
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# average into two groups
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group1 = np.mean(data[:17], 0)
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group2 = np.mean(data[18:], 0)
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# plot
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geo = hyp.plot([group1[:100, :], group2[:100, :]])
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.. code:: ipython3
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aligned_data = hyp.align(data)
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# average into two groups
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group1 = np.mean(aligned_data[:17], 0)
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group2 = np.mean(aligned_data[18:], 0)
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# plot
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geo = hyp.plot([group1[:100, :], group2[:100, :]])
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@@ -103,15 +105,14 @@ to 'SRM'.
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.. code:: ipython3
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aligned_data = hyp.align(data, align='SRM')
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# average into two groups
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group1 = np.mean(aligned_data[:17], 0)
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group2 = np.mean(aligned_data[18:], 0)
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# plot
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geo = hyp.plot([group1[:100, :], group2[:100, :]])
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.. image:: align_files/align_15_0.png
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docs/tutorials/cluster.rst

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@@ -233,7 +233,7 @@ We can further examine the number of datapoints assigned each label.
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.. parsed-literal::
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Counter({0: 5024, 1: 1768, 2: 1332})
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Counter({0: 1768, 1: 5024, 2: 1332})
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docs/tutorials/geo.rst

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@@ -381,7 +381,7 @@ respectively.
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.. parsed-literal::
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<matplotlib.axes._subplots.Axes3DSubplot at 0x117f8df98>
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<matplotlib.axes._subplots.Axes3DSubplot at 0x10b115710>
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@@ -413,7 +413,7 @@ First, let's plot without making any changes.
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.. parsed-literal::
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<hypertools.datageometry.DataGeometry at 0x1184c1080>
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<hypertools.datageometry.DataGeometry at 0x10b4d8048>
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.. parsed-literal::
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<hypertools.datageometry.DataGeometry at 0x1184a6b70>
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<hypertools.datageometry.DataGeometry at 0x10ba49d30>
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