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musicinformationretrieval-com
Commit 64d7a49e authored by Steve Tjoa's avatar Steve Tjoa
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dtw

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......@@ -11773,7 +11773,7 @@ div#notebook {
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[74]:</div>
<div class="prompt input_prompt">In&nbsp;[1]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
......@@ -11844,7 +11844,7 @@ div#notebook {
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[123]:</div>
<div class="prompt input_prompt">In&nbsp;[2]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
......@@ -11860,7 +11860,7 @@ div#notebook {
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[127]:</div>
<div class="prompt input_prompt">In&nbsp;[3]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
......@@ -11878,13 +11878,13 @@ div#notebook {
<div class="output_area">
<div class="prompt output_prompt">Out[127]:</div>
<div class="prompt output_prompt">Out[3]:</div>
<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.legend.Legend at 0x11ce5d910&gt;</pre>
<pre>&lt;matplotlib.legend.Legend at 0x1175965d0&gt;</pre>
</div>
</div>
......@@ -11898,334 +11898,342 @@ div#notebook {
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>
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......@@ -12264,7 +12272,7 @@ REREtN4/u6cPBCJ5QIcAAAAASUVORK5CYII=
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[128]:</div>
<div class="prompt input_prompt">In&nbsp;[4]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">distance</span><span class="o">.</span><span class="n">euclidean</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
......@@ -12280,7 +12288,7 @@ REREtN4/u6cPBCJ5QIcAAAAASUVORK5CYII=
<div class="output_area">
<div class="prompt output_prompt">Out[128]:</div>
<div class="prompt output_prompt">Out[4]:</div>
......@@ -12297,7 +12305,7 @@ REREtN4/u6cPBCJ5QIcAAAAASUVORK5CYII=
</div>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">distance</span><span class="o">.</span><span class="n">euclidean</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">12</span><span class="p">])</span>
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<p>Another choice is the <strong>Manhattan or cityblock distance</strong>:</p>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">distance</span><span class="o">.</span><span class="n">cityblock</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">])</span>
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<pre>7</pre>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">distance</span><span class="o">.</span><span class="n">cityblock</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">12</span><span class="p">])</span>
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<pre>17</pre>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">distance</span><span class="o">.</span><span class="n">cosine</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">distance</span><span class="o">.</span><span class="n">cosine</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">scipy</span><span class="o">.</span><span class="n">spatial</span><span class="o">.</span><span class="n">distance</span><span class="o">.</span><span class="n">cosine</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
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......@@ -12491,7 +12574,7 @@ $$ \mathrm{argmin} \ \{ d(x[i-1], y[j]), d(x[i], y[j-1]), d(x[i-1], j-1]) \} $$
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<div class=" highlight hl-ipython2"><pre><span></span><span class="k">def</span> <span class="nf">dtw_table</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
......@@ -12524,7 +12607,7 @@ $$ \mathrm{argmin} \ \{ d(x[i-1], y[j]), d(x[i], y[j-1]), d(x[i-1], j-1]) \} $$
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">table</span> <span class="o">=</span> <span class="n">dtw_table</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
......@@ -12546,7 +12629,7 @@ $$ \mathrm{argmin} \ \{ d(x[i-1], y[j]), d(x[i], y[j-1]), d(x[i-1], j-1]) \} $$
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<div class=" highlight hl-ipython2"><pre><span></span><span class="k">print</span> <span class="s1">&#39; &#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%4d</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">n</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">y</span><span class="p">)</span>
......@@ -12628,7 +12711,7 @@ $$ \mathrm{argmin} \ \{ d(x[i-1], y[j]), d(x[i], y[j-1]), d(x[i-1], j-1]) \} $$
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<div class=" highlight hl-ipython2"><pre><span></span><span class="k">def</span> <span class="nf">dtw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">table</span><span class="p">):</span>
......@@ -12658,7 +12741,7 @@ $$ \mathrm{argmin} \ \{ d(x[i-1], y[j]), d(x[i], y[j-1]), d(x[i-1], j-1]) \} $$
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">path</span> <span class="o">=</span> <span class="n">dtw</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">table</span><span class="p">)</span>
......@@ -12675,7 +12758,7 @@ $$ \mathrm{argmin} \ \{ d(x[i-1], y[j]), d(x[i], y[j-1]), d(x[i-1], j-1]) \} $$
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......@@ -12722,7 +12805,7 @@ $$ \mathrm{argmin} \ \{ d(x[i-1], y[j]), d(x[i], y[j-1]), d(x[i-1], j-1]) \} $$
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<div class=" highlight hl-ipython2"><pre><span></span><span class="nb">sum</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">y</span><span class="p">[</span><span class="n">j</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span> <span class="ow">in</span> <span class="n">path</span> <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">j</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">)</span>
......@@ -12738,7 +12821,7 @@ $$ \mathrm{argmin} \ \{ d(x[i-1], y[j]), d(x[i], y[j-1]), d(x[i-1], j-1]) \} $$
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......@@ -12764,7 +12847,7 @@ $$ \mathrm{argmin} \ \{ d(x[i-1], y[j]), d(x[i], y[j-1]), d(x[i-1], j-1]) \} $$
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<div class=" highlight hl-ipython2"><pre><span></span><span class="n">table</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
......@@ -12780,7 +12863,7 @@ $$ \mathrm{argmin} \ \{ d(x[i-1], y[j]), d(x[i], y[j-1]), d(x[i-1], j-1]) \} $$
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dtw.ipynb
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"cells": [
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
......@@ -62,8 +64,10 @@
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"execution_count": 2,
"metadata": {
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"outputs": [],
"source": [
"x = [0, 4, 4, 0, -4, -4, 0]\n",
......@@ -74,24 +78,24 @@
},
{
"cell_type": "code",
"execution_count": 127,
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x11ce5d910>"
"<matplotlib.legend.Legend at 0x1175965d0>"
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"execution_count": 3,
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},
{
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bAPD06IXjoZ/gnPoASrsLN9R7FYUNXx1gw9cHsZoNPHpnHPF9gqcJ/3y09ksQ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\nuqizAvKd7R2lWyr2G81wuApYB7yUz7uuOpnm8PN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XY/tcfRO+00nQxg1YF75N0DdfAeBu0xb71BnY7p+OEhVVfWhjzLihqZnxiTPlzFuZQX6x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\n46ulKyvFvOIDrAv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1BXzGTifG3TsxpqXWFCl7dqFzu6sP8TRp4i1SEpNwJfbFlZiEp2Wr\nevn2WstXCpcLaO0JCkSSsfokY/X9XMbbMrxN+wBThvfkxnhZnbCuAv0cNhzYh+Xdd7AsW4K+5CyK\nwYBj1BhsMx7Aef3gBmnmD/SM1XSqsIKXV2aSV1hBYvcWzBrTG0vQT6eJSsbqC6iMPR4Mhw54i5Sq\n6V7G7Ex0dnv1IYrViis2vqpIScaZkISncxfVXjO0lq8qzflCCNEY3RjfhpZNrby6Oot3N+4m90w5\nd9/cDb1eeytKCd9yd+1O+fN/o/yPz2BZvcLbC7N+Leb1a3H1jMY2/QEq756EEhbu66GKC+w8XMjr\na7KpqHQxakBHxg/pgl6Dq8YJ7dOfOokxtaYnxZiRhv5scfXXFYMBd3Tv6ulezsRk3NG9ajfPi2py\nxUWoQjJWn2SsvktlfLrI+2nsyYIK4ro255dj+2A1yx+aq9HozmFFwfjjdqwL3sK8fi06pxNPSCiV\nd0/CNmOW981KPWt0GdeDranHWbJpH3o9TBsRzfWxrS95vGSsPn/JWFdchDE9DVN6avW0L8Opk7WO\ncXXuUrPCV2JfXDGxEOzbnkmt5StTxS6gtScoEEnG6pOM1Xe5jCvsLt78KJvsQ4W0bRHCYxPjiGpi\nvejxorbGfA7rTp/GumQRlkULMOSeAMBx/WBsM2fhGDHauyFcPWjMGV8tt8fD8s372ZJ6nLBgE4+O\nj6V7u8s3OkvG6tNkxjYbxuxM7wpfVUWK8eCBWoe4W7aqtQyxKyERpWkzHw344rSWrxQuF9DaExSI\nJGP1Scbqu5KM3R4PH3y+n807jhNq9b7Z6dFeOxt5aZmcw4DLRdD/NmJd+DZB27YC4G7VuqaZ/xqb\nbyXjK1Nhd/LGRznkHCqkXWQIcybE0eIKP4SQjNXn84xdLgx7dtcUKempGHfloHO5qg/xhEfgik/E\nleTtSXElJTf4Yhx15fN8LyCFywW09gQFIslYfZKx+q4m4y/STrBk014Apo7oyeA4//iD5UtyDtdm\n2LcXy7tvY1m+FH1pCYrRSOXosdhnzsI5YFCdGnMl48vLK6rg5RWZnCqsIL5rcx68ymmfkrH6GjRj\nRUF/+JB3uldqivffrAx0FRU1h5jNuGLiapYhTkzG3aUr+OkGxVo7h6U5XwghVHZTYltaNgvm9TVZ\nLPxkNyfPVDDxpq7StC+umLt7D8pf+Dvlf3oWy6oPsS6Yj+Wj1Vg+Wo2rVx9sMx7APvEeCA319VAD\nxq4jRby+Jotyu4sR13Vg4hD5nW1sdHl5VT0pOzBVXU3RFxVVf13R63H37FW9wpcrMQlXrz71Np1T\nXB254iJUIRmrTzJWX10yzqtaQrWun942JnIOX4aiYPrhOywL3sK8YR06lwtPWDj2e+7FPv0B3D16\nXvYhJOOLO/8q6bQR0dwQd+km/IuRjNVXXxnrSs5izEivtfu84cTxWse4O3aqtVeKMzYeQkKu+Xtr\nmdbOYZkqdgGtPUGBSDJWn2SsvrpmfP58+baRITx+FfPlGxM5h6+cPu8UlvfexbJ4YfUqRY7BQ7DN\nmIVjxKiLLp0qGf9UffelScbqq1PGdjvGnKyaZYjTUzHs34fuvLe9nhaRNcsQJyXjik9Cad68nkev\nfVo7h6VwuYDWnqBAJBmrTzJW37VkfOEKRbPHSdP+heQcrgOnk6BPP8G6cD5BX28DwN2mLfapM7Dd\nPx0lKqrW4ZJxbReuBDhnYhyR1/ihgmSsvstm7HZj2LvHW6Sca6DfmY3O6aw+xBMahishsXqvFFdi\nEp627RpkI1it09o5LIXLBbT2BAUiyVh9krH66iPjq90TojGRc/jaGPbsxrpwPuYPl6MvK0Uxmagc\ncwe2GQ/i6n8d6HSS8XnU2ntJMlZfrYwVBf3RI7X2SjFlpKOrKK8+XgkKwhUT6y1SEpJwJfXF3a27\n3zbPq01r57AULhfQ2hMUiCRj9UnG6quvjHMOF/JG1S7cI6/rwARpAAbkHK4vurJSzB8ux7pwPsY9\nuwFw9YnFNuMBwn45k3xbg/yZ17Q9R4t4dbW3Cf+2fu25++Zu9fY7KOexunT5+bQ4tIvyL772Finp\nqegLCqq/ruh0uHv0xJVYswyxq1cfMJt9OGr/orVzWAqXC2jtCQpEkrH6JGP11WfGpworeHlFBnlF\nNhK6teDBsb2xBDXupn05h+uZomD67hssC+Zj/ngdOrcbjEacvfp4l2yt2l/C3TP6oj0xgWhbRi7v\n/W8PAFOG9+TG+PpdqlzO4/qjKyvFmJmBsaonxZSWguHY0VrHuNt38E71OlekxMWjhF78ja64PK2d\nw1K4XEBrT1AgkozVJxmrr74zLrc7eX1NNruOFNEuMpQ5E2NpEdF4m/blHFaP/tRJLO+9S8jXX6Ck\npqKrrKz+mhIcjCs2vvrTaWdCEp5OnQNurr/Ho/Dh1v189uMxQq0mZo+LoWeHpvX+feQ8riOHA+PO\n7Jq9UtJSMOzdU7t5vnlznAlJmG8YxNkefXAmJKNERvpw0IFJa+ewFC4X0NoTFIgkY/VJxupTI2OX\n28OyLfvYmnqC8GATj06Io1vbiHr9Hv5CzmH1RUaGkZ9biHH3zvM+xU7FsHsnOo+n+jhP06bnNS17\nixmlZUsfjvza2Cpd/HddDpkHCmhT1YQfpdLKfnIeXwGPB8P+fd6pXlXLEBuzs9A5HNWHKMEhOOMT\nqvdKcSYm42nfQXq1GoDW8pUNKIUQQiOMBj1TbutJm+YhLNu8j78vTWXGyF4MjGnl66GJQGUy4YqN\nxxUbD9Nmem8rL8eYlVn1JjIFU2oKQVu3ELR1S/Xd3G3b1VqByZWQiBIW7qMf4sqdLrYxb2UmuWfK\nienSjIfGxhBskbc7DUZR0J84XmuvFGN6GvqymjfGismEq3dMzTLECUnefYkMBh8OXPgD+U0WQggf\nuCW5Ha2aBfP62mzmb9hJbkE5427sgj7ApusIjQoJwTVgIK4BA6tv0hUWVF+RObf3hfnjdZg/XgdU\nNUF36+69IpOY5O0x6BMLFouvfoqf2HusmFdXZ1FmczKsb3vuHtoVg6wkpapa501aCqa0VPT5p2sd\n4+reA0fC6OoiRWvnjfAfUrgIIYSP9OncjKenJvPyykw+/u4IuWfKmTVGmvaFbyjNmuMcOgzn0GFV\nNyjoc0/U6kEwpqdh2bcMy4fLvIec++Q8sWaambt7D598cv5VRi6Lq5rwp47oyU0JbRt8DAGvvBxT\nVkbVMsQ7vNMOjxyudYi7bTsqR4+tuVIXn4AS3jinw4r6Jz0uQhWSsfokY/U1VMZlNievr8li99Fi\nOkSFMmdiHM3CA//TSDmH1VfvGXs8GA7sx5i6o6aYuaBXwRMSiisu/md7FdTg8Sis/OIAn24/SojF\nyCPjYunVsf6b8C8mYM9jp7N2b1RqCoY9u2r3RjVpUnMFLrGvar1RAZuxRmgtX+lxEUIIDQu1mvjN\nPQks2bSXL9Nz+cuiHTw2PpaujbRpX2iYXo+7ew/c3XtQec9k723nVoc6r6fB9P23BH33TfXdPM2b\n11rC1pmQjNKixTUPx1bp4q11OWQcKKB182DmTIyjZdPga37cRsfjwXDoQK0ixZiThc5urz5EsVpx\n9bsu4FejE9omhYsQQmiA0aBn6vCetGkRwvIt+3hpaRozR0UzoI807QuNCwryFiQJSdhnPACctx/H\nub6H9FTMmz/DvPmz6rtd634cZ4ptvLwqkxP55fTp3IyH7+hDsMVU7z9eINKfzD1vCqC3p0lfcrb6\n64rBUN08f+6qWWPb/0dok5yBQgihETqdjmF929OqWTBvfpTNW+t3kltQwZ2DO0vTvvArSmgYzkE3\n4Bx0Q/VtujNnMKWn1CpmLOvWwLo13vvodLh7RntXmjpXzPSOgaCgnzz+3mPFvLYmi9IKJ7ckt2PS\nLd2kCf8idMVFGNPTvFfDqrI35J2qdYyrS1ccw4bX9CrFxIG18e4xJbRLChchhNCY2C7NeWpKX15e\nmcGGbw9zsqCcB0b3xhwkS4UK/6W0aIHj1uE4bh1edYOC/tjRmqlJ6amY0tMw7t6FZfkS7yFBQbhi\nYmvtMbPNFsqiz/bi8cCU4T25OVGa8KvZbN5lrtNTqqd9GQ8eqHWIu1VrKkfeXlOkxCegNGm4niAh\nroUULkIIoUFtWoTwzLR+vLY6i5Q9+eQXpzBnQuNo2heNhE6Hp0NHHB064hg7znub241h396qYmaH\n9413Viam1BSszAdgWFAw3Vp3I3zIICKOl+CK9OBp267x9Vq4XBh276pZJCEtFeOuHHRud/Uhnogm\nOG68uWYZ4sQkPK3b+HDQQlwbWVVMqEIyVp9krD4tZOxye3j/sz1syzhJREgQj02Io0sb7W8CeCW0\nkG+gC4iM7Xbc6RnseP8TrNnp9Mo/QOszx9Cd9/bFExlVtbJVzR4zSrPmDTK8BslYUdAfOljdk2JK\nS8GYlYHOZqs5xGLBFRNXq0hxd+4KATCFLiDOYw3TWr6yqpgQQvgpo0HPtBHRtGkRygef7+OlpanM\nHNWL63rX/5KjQmjRmUqFeTk6jrcbQu8b7qTrnTEUOG0YM9Jr7TFj/uxTzJ99Wn0/d8dO1cv0uhKT\ncMbGQ0iID3+SK6fPO1XVNJ/inUaXkYa+qKj664pejzu6d3Wx5kpMwhXdG0yyOIEIbFK4CCGExul0\nOm7r155Wzay8+VEO/12Xw8mCcsbeIE37IrDtP3GWV1dlUlLh5Oakttx7S3eMBj2KxYTzhhtx3nAj\n56456PLyqouYc8syW9auhrWrgao3+z171VyRSErWxJt9XclZjOlptXafN+SeqHWMu1Nn7DcN9V5R\nSkjGFRvnN0WYEPVJChchhPATcV1b8NS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LBgcHefPmDbdv3yYWi9HZ2Wk6yToejwePxwPA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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11cd40410>"
"<matplotlib.figure.Figure at 0x1174f4210>"
]
},
"metadata": {},
......@@ -127,7 +131,7 @@
},
{
"cell_type": "code",
"execution_count": 128,
"execution_count": 4,
"metadata": {},
"outputs": [
{
......@@ -136,7 +140,7 @@
"5.0"
]
},
"execution_count": 128,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
......@@ -147,7 +151,7 @@
},
{
"cell_type": "code",
"execution_count": 129,
"execution_count": 5,
"metadata": {},
"outputs": [
{
......@@ -156,7 +160,7 @@
"13.0"
]
},
"execution_count": 129,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
......@@ -165,6 +169,53 @@
"scipy.spatial.distance.euclidean([0, 0], [5, 12])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another choice is the **Manhattan or cityblock distance**:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.spatial.distance.cityblock(0, [3, 4])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"17"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scipy.spatial.distance.cityblock([0, 0], [5, 12])"
]
},
{
"cell_type": "markdown",
"metadata": {},
......@@ -174,7 +225,7 @@
},
{
"cell_type": "code",
"execution_count": 134,
"execution_count": 8,
"metadata": {},
"outputs": [
{
......@@ -183,7 +234,7 @@
"0.0"
]
},
"execution_count": 134,
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
......@@ -194,7 +245,7 @@
},
{
"cell_type": "code",
"execution_count": 132,
"execution_count": 9,
"metadata": {},
"outputs": [
{
......@@ -203,7 +254,7 @@
"1.0"
]
},
"execution_count": 132,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
......@@ -214,7 +265,7 @@
},
{
"cell_type": "code",
"execution_count": 133,
"execution_count": 10,
"metadata": {},
"outputs": [
{
......@@ -223,7 +274,7 @@
"2.0"
]
},
"execution_count": 133,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
......@@ -280,7 +331,7 @@
},
{
"cell_type": "code",
"execution_count": 135,
"execution_count": 11,
"metadata": {
"collapsed": true
},
......@@ -311,8 +362,10 @@
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"table = dtw_table(x, y)"
......@@ -327,7 +380,7 @@
},
{
"cell_type": "code",
"execution_count": 142,
"execution_count": 13,
"metadata": {},
"outputs": [
{
......@@ -388,7 +441,7 @@
},
{
"cell_type": "code",
"execution_count": 138,
"execution_count": 14,
"metadata": {
"collapsed": true
},
......@@ -416,7 +469,7 @@
},
{
"cell_type": "code",
"execution_count": 139,
"execution_count": 15,
"metadata": {},
"outputs": [
{
......@@ -437,7 +490,7 @@
" (7, 9)]"
]
},
"execution_count": 139,
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
......@@ -463,7 +516,7 @@
},
{
"cell_type": "code",
"execution_count": 144,
"execution_count": 16,
"metadata": {},
"outputs": [
{
......@@ -472,7 +525,7 @@
"10"
]
},
"execution_count": 144,
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
......@@ -490,7 +543,7 @@
},
{
"cell_type": "code",
"execution_count": 146,
"execution_count": 17,
"metadata": {},
"outputs": [
{
......@@ -499,7 +552,7 @@
"10.0"
]
},
"execution_count": 146,
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
......
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