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note about numpy in-place ops
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‎tutorials/matrix_cheatsheet.md‎

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@@ -235,6 +235,8 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
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<td height="59" bgcolor="#0066cc" style="border: none; padding: 0in">
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<p align="center"><font color="#ffffff"><font face="Arial"><font size="5" style="font-size: 18pt"><b>Task</b></font></font></font></p>
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<td colspan="6" height="70" bgcolor="#ffffff" style="border: none; padding: 0in">
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<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>CREATING
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MATRICES </b></font></font><br>[<a href='#cheat_overview'>back to cheat sheet overview</a>]</p>
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MATRICES</b></font></font></p>
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<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
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a = np.array([[1],[2],[3]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
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a<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2],<br>&nbsp;&nbsp;
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&nbsp; &nbsp; [3]])</font></font></p>
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<p align="left"><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font>
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<font face="Andale Mono"><font size="1" style="font-size: 8pt">a</font></font><font color="#2fff12">
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</font><font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">=
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np.array([1,2,3]).reshape(1,3)</font></font></font></p>
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<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><br></font></font><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font>
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<font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">b.shape<br>(1,
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3)</font></font></font></p>
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<p align="left"><br>
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</p>
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<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
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<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
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b = np.array([1,2,3])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
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b<br>array([1, 2, 3])</font></font></p>
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<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
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note that numpy doesn't have <br># explicit “row-vectors”, but
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1-D <br># arrays</font></font></p>
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<p style="margin-bottom: 0in"><font face="Liberation Serif, serif"><font size="3" style="font-size: 12pt"><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font><font color="#2fff12">
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</font><font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">b.shape</font></font></font></font></font></p>
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<p style="margin-bottom: 0in"><font face="Andale Mono"><font size="1" style="font-size: 8pt">(3,)</font></font></p>
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</p>
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<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
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<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
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np.ones([3,2])<br>array([[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp;
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np.ones((3,2))<br>array([[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp;
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[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 1.,&nbsp; 1.]])</font></font></p>
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<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>ACCESSING
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MATRIX ELEMENTS</b></font></font><br>[<a href='#cheat_overview'>back to cheat sheet overview</a>]</p>
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MATRIX ELEMENTS</b></font></font></p>
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<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>MANIPULATING
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SHAPE AND DIMENSIONS</b></font></font><br>[<a href='#cheat_overview'>back to cheat sheet overview</a>]</p>
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SHAPE AND DIMENSIONS</b></font></font></p>
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A = np.array([[1,2,3],[4,5,6],[7,8,9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
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A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 9],<br>&nbsp;&nbsp;
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total_elements = A.shape[0] * A.shape[1]<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
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B = A.reshape(1, total_elements)&nbsp;<br><br># or
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to&nbsp;change the array in place<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
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total_elements = np.prod(A.shape)</font></font></p>
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B = A.reshape(1, total_elements)&nbsp;<br><br># alternative
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shortcut:<br># A.reshape(1,-1)<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
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<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>BASIC
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MATRIX OPERATIONS</b></font></font></p>
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<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
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Note that NumPy was optimized for<br># in-place assignments<br>#
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‎tutorials/matrix_cheatsheet_only.html‎

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<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Generating
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a Gaussian dataset:<br><br>creating random vectors from the
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multivariate normal<br>distribution given mean and covariance

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