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--- cap241:2018:rscript [2018/08/22 21:06] – created karine
+++ cap241:2018:rscript [2018/08/22 21:09] (atual) – karine
@@ Linha 1: / Linha 1: @@
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src="data:image/png;base64,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
-<!-- rnb-plot-end -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYWxpZ25tZW50JGxvY2FsQ29zdE1hdHJpeFxuYGBgIn0= -->
-<pre class="r"><code>alignment$localCostMatrix</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-output-begin eyJkYXRhIjoiICAgICAgWywxXSBbLDJdIFssM10gWyw0XSBbLDVdIFssNl0gWyw3XSBbLDhdIFssOV0gWywxMF1cbiBbMSxdICAgIDAgICAgMCAgICAzICAgIDMgICAgMyAgICAzICAgIDMgICAgMCAgICAwICAgICAwXG4gWzIsXSAgICAwICAgIDAgICAgMyAgICAzICAgIDMgICAgMyAgICAzICAgIDAgICAgMCAgICAgMFxuIFszLF0gICAgMCAgICAwICAgIDMgICAgMyAgICAzICAgIDMgICAgMyAgICAwICAgIDAgICAgIDBcbiBbNCxdICAgIDMgICAgMyAgICAwICAgIDAgICAgMCAgICAwICAgIDAgICAgMyAgICAzICAgICAzXG4gWzUsXSAgICAzICAgIDMgICAgMCAgICAwICAgIDAgICAgMCAgICAwICAgIDMgICAgMyAgICAgM1xuIFs2LF0gICAgMyAgICAzICAgIDAgICAgMCAgICAwICAgIDAgICAgMCAgICAzICAgIDMgICAgIDNcbiBbNyxdICAgIDMgICAgMyAgICAwICAgIDAgICAgMCAgICAwICAgIDAgICAgMyAgICAzICAgICAzXG4gWzgsXSAgICAzICAgIDMgICAgMCAgICAwICAgIDAgICAgMCAgICAwICAgIDMgICAgMyAgICAgM1xuIFs5LF0gICAgMCAgICAwICAgIDMgICAgMyAgICAzICAgIDMgICAgMyAgICAwICAgIDAgICAgIDBcblsxMCxdICAgIDAgICAgMCAgICAzICAgIDMgICAgMyAgICAzICAgIDMgICAgMCAgICAwICAgICAwXG4ifQ== -->
-<pre><code>      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
- [1,]    0    0    3    3    3    3    3    0    0     0
- [2,]    0    0    3    3    3    3    3    0    0     0
- [3,]    0    0    3    3    3    3    3    0    0     0
- [4,]    3    3    0    0    0    0    0    3    3     3
- [5,]    3    3    0    0    0    0    0    3    3     3
- [6,]    3    3    0    0    0    0    0    3    3     3
- [7,]    3    3    0    0    0    0    0    3    3     3
- [8,]    3    3    0    0    0    0    0    3    3     3
- [9,]    0    0    3    3    3    3    3    0    0     0
-[10,]    0    0    3    3    3    3    3    0    0     0</code></pre>
-<!-- rnb-output-end -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYWxpZ25tZW50JGRpc3RhbmNlXG5gYGAifQ== -->
-<pre class="r"><code>alignment$distance</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDBcbiJ9 -->
-<pre><code>[1] 0</code></pre>
-<!-- rnb-output-end -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYWxpZ25tZW50JHN0ZXBzVGFrZW5cbmBgYCJ9 -->
-<pre class="r"><code>alignment$stepsTaken</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-output-begin eyJkYXRhIjoiIFsxXSAzIDEgMSAxIDEgMSAxIDEgMiAxXG4ifQ== -->
-<pre><code> [1] 3 1 1 1 1 1 1 1 2 1</code></pre>
-<!-- rnb-output-end -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdChhbGlnbm1lbnQkaW5kZXgxLGFsaWdubWVudCRpbmRleDIsbWFpbj1cIldhcnBpbmcgZnVuY3Rpb25cIik7XG5gYGAifQ== -->
-<pre class="r"><code>plot(alignment$index1,alignment$index2,main=&quot;Warping function&quot;);</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MzIuNjMyOSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjcwMH0= -->
-<p><img 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/></p>
-<!-- rnb-plot-end -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdChkdHc6OmR0dyhYLCBZLCBrZWVwLmludGVybmFscyA9IFRSVUUsIHN0ZXAucGF0dGVybiA9cmFiaW5lckp1YW5nU3RlcFBhdHRlcm4oNixcImNcIikpLFxuICAgICB0eXBlPVwidHdvd2F5XCIsb2Zmc2V0PS0yKVxuYGBgIn0= -->
-<pre class="r"><code>plot(dtw::dtw(X, Y, keep.internals = TRUE, step.pattern =rabinerJuangStepPattern(6,&quot;c&quot;)),
-     type=&quot;twoway&quot;,offset=-2)</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MzIuNjMyOSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjcwMH0= -->
-<p><img 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/></p>
-<!-- rnb-plot-end -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuZHR3UGxvdFR3b1dheShkdHcoWCwgWSwgc3RlcCA9IGFzeW1tZXRyaWNQMSwga2VlcCA9IFQpKVxuYGBgIn0= -->
-<pre class="r"><code>dtwPlotTwoWay(dtw(X, Y, step = asymmetricP1, keep = T))</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MzIuNjMyOSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjcwMH0= -->
-<p><img 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/></p>
-<!-- rnb-plot-end -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucmFiaW5lckp1YW5nU3RlcFBhdHRlcm4oNixcImNcIilcbmBgYCJ9 -->
-<pre class="r"><code>rabinerJuangStepPattern(6,&quot;c&quot;)</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-output-begin eyJkYXRhIjoiU3RlcCBwYXR0ZXJuIHJlY3Vyc2lvbjpcbmdbaSxqXSA9IG1pbihcbiAgICAgZ1tpLTMsai0yXSArICAgICBkW2ktMixqLTFdICsgICAgIGRbaS0xLGogIF0gKyAgICAgZFtpICAsaiAgXSAsXG4gICAgIGdbaS0xLGotMV0gKyAgICAgZFtpICAsaiAgXSAsXG4gICAgIGdbaS0yLGotM10gKyAgICAgZFtpLTEsai0yXSArICAgICBkW2kgICxqLTFdICsgMCAqIGRbaSAgLGogIF0gLFxuICApXG5cbiBOb3JtYWxpemF0aW9uIGhpbnQ6IE5cbiJ9 -->
-<pre><code>Step pattern recursion:
-g[i,j] = min(
-     g[i-3,j-2] +     d[i-2,j-1] +     d[i-1,j  ] +     d[i  ,j  ] ,
-     g[i-1,j-1] +     d[i  ,j  ] ,
-     g[i-2,j-3] +     d[i-1,j-2] +     d[i  ,j-1] + 0 * d[i  ,j  ] ,
-  )
- Normalization hint: N</code></pre>
-<!-- rnb-output-end -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdChyYWJpbmVySnVhbmdTdGVwUGF0dGVybig2LFwiY1wiKSlcbmBgYCJ9 -->
-<pre class="r"><code>plot(rabinerJuangStepPattern(6,&quot;c&quot;))</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MzIuNjMyOSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjcwMH0= -->
-<p><img 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-<!-- rnb-plot-end -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBQbG90XG56b286OnBsb3Quem9vKGNiaW5kKFgsIFkpLCBwbG90LnR5cGUgPSBcIm11bHRpcGxlXCIsIGNvbCA9IGMoXCJyZWRcIiwgXCJibHVlXCIpLCBsd2QgPSAyKVxuYGBgIn0= -->
-<pre class="r"><code># Plot
-zoo::plot.zoo(cbind(X, Y), plot.type = &quot;multiple&quot;, col = c(&quot;red&quot;, &quot;blue&quot;), lwd = 2)</code></pre>
-<!-- rnb-source-end -->
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-<p><img 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-<pre class="r"><code>#Creates time series
-ts1_x &lt;- c(1,2,3,4,5,6,7,8,9,10)
-ts1_y &lt;- c(1,1,1,4,4,4,4,4,1,1)
-ts1 &lt;- zoo::zoo(ts1_y, ts1_x);
-#Creates time series
-ts2_x &lt;- c(1,2,3,4,5,6,7,8,9,10)
-ts2_y &lt;- c(1,1,4,4,4,4,4,1,1,1)
-ts2 &lt;- zoo::zoo(ts1_x, ts1_y);</code></pre>
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-<pre><code>some methods for &lt;U+393C&gt;&lt;U+3E33&gt;zoo&lt;U+393C&gt;&lt;U+3E34&gt; objects do not work if the index entries in &lt;U+393C&gt;&lt;U+3E31&gt;order.by&lt;U+393C&gt;&lt;U+3E32&gt; are not unique</code></pre>
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-<p>Plot time series</p>
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-<pre class="r"><code>zoo::plot.zoo(cbind(ts2_y, ts1_y), plot.type = &quot;multiple&quot;, col = c(&quot;red&quot;, &quot;blue&quot;), lwd = 2)</code></pre>
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-<p><img src="data:image/png;base64,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/></p>
-<!-- rnb-plot-end -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuVFNkaXN0OjpFdWNsaWRlYW5EaXN0YW5jZSh0czFfeSwgdHMyX3kpXG5gYGAifQ== -->
-<pre class="r"><code>TSdist::EuclideanDistance(ts1_y, ts2_y)</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDQuMjQyNjQxXG4ifQ== -->
-<pre><code>[1] 4.242641</code></pre>
-<!-- rnb-output-end -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuVFNkaXN0OjpEVFdEaXN0YW5jZSh0czFfeSwgdHMyX3kpXG5gYGAifQ== -->
-<pre class="r"><code>TSdist::DTWDistance(ts1_y, ts2_y)</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-output-begin eyJkYXRhIjoiWzFdIDBcbiJ9 -->
-<pre><code>[1] 0</code></pre>
-<!-- rnb-output-end -->
-<!-- rnb-chunk-end -->
-<!-- rnb-text-begin -->
-<p>Read the time series from csv file and plot all</p>
-<!-- rnb-text-end -->
-<!-- rnb-chunk-begin -->
-<!-- rnb-source-begin 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 -->
-<pre class="r"><code>#read time series from file
-timeseries.zoo &lt;- read.zoo(&quot;timeseries.csv&quot;, header = TRUE, sep = &quot;,&quot;)
-#zoo::plot.zoo(z, main = &quot;Set of time series&quot;)
-#Define a color for each group (4 groups)
-colors&lt;-c(&quot;#FF0000FF&quot;,&quot;#FF0000FF&quot;,&quot;#FF0000FF&quot;,&quot;#FF0000FF&quot;,
-          &quot;#00FF9FFF&quot;,&quot;#00FF9FFF&quot;,&quot;#00FF9FFF&quot;,&quot;#00FF9FFF&quot;,
-          &quot;#009FFFFF&quot;,&quot;#009FFFFF&quot;,&quot;#009FFFFF&quot;,&quot;#009FFFFF&quot;,
-          &quot;#DFFF00FF&quot;,&quot;#DFFF00FF&quot;,&quot;#DFFF00FF&quot;,&quot;#DFFF00FF&quot;)</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-chunk-end -->
-<!-- rnb-text-begin -->
-<!-- rnb-text-end -->
-<!-- rnb-chunk-begin -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI1Bsb3QgYWxsIHRpbWUgc2VyaWVzXG56b286OnBsb3Quem9vKHRpbWVzZXJpZXMuem9vLCBwbG90LnR5cGUgPSBcIm11bHRpcGxlXCIsIGNvbCA9IGNvbG9ycywgbHdkID0gMiwgbWFpbj0gXCJTZXQgb2YgdGltZSBzZXJpZXNcIilcbmBgYCJ9 -->
-<pre class="r"><code>#Plot all time series
-zoo::plot.zoo(timeseries.zoo, plot.type = &quot;multiple&quot;, col = colors, lwd = 2, main= &quot;Set of time series&quot;)</code></pre>
-<!-- rnb-source-end -->
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" /></p>
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-<p>Self-organizing maps using Kohonen package</p>
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-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuIyBTT01cbnNvbWdyaWQ9a29ob25lbjo6c29tZ3JpZCh4ZGltPTQsIHlkaW09NCwgdG9wbz1cInJlY3Rhbmd1bGFyXCIsIG5laWdoYm91cmhvb2QuZmN0ID0gXCJnYXVzc2lhblwiKVxuc29tPC1rb2hvbmVuOjpzdXBlcnNvbSh0KGFzLm1hdHJpeCh0aW1lc2VyaWVzLnpvbykpLHNvbWdyaWQscmxlbiA9IDEwMCwgIGFscGhhPTEpXG5gYGAifQ== -->
-<pre class="r"><code># SOM
-somgrid=kohonen::somgrid(xdim=4, ydim=4, topo=&quot;rectangular&quot;, neighbourhood.fct = &quot;gaussian&quot;)
-som&lt;-kohonen::supersom(t(as.matrix(timeseries.zoo)),somgrid,rlen = 100,  alpha=1)</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-chunk-end -->
-<!-- rnb-text-begin -->
-<!-- rnb-text-end -->
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-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdChzb20sXCJjb2Rlc1wiLGNvZGVSZW5kZXJpbmc9XCJsaW5lc1wiKVxuYGBgIn0= -->
-<pre class="r"><code>plot(som,&quot;codes&quot;,codeRendering=&quot;lines&quot;)</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MzIuNjMyOSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjcwMH0= -->
-<p><img src="data:image/png;base64,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" /></p>
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-<p>The time series were allocated in these neurons. Each number represents the number of neuron.</p>
-<p>Example: the position 1 of vector represents the time series 1 in a dataset, this time series was allocated in neuron 9.</p>
-<!-- rnb-text-end -->
-<!-- rnb-chunk-begin -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxubmV1cm9uX3RpbWVzZXJpZXM8LXNvbSR1bml0LmNsYXNzaWZcbmluZm9fdGltZVNlcmllczwtIGRhdGEuZnJhbWUodGltZV9zZXJpZXM9KHJvd25hbWVzKHQodGltZXNlcmllcy56b28pKSksIFxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICBuZXVyb249YXMuaW50ZWdlcihzb20kdW5pdC5jbGFzc2lmKSlcbiAgICAgICAgICAgICAgICAgICAgICAgIFxuY29kZXM8LXpvbzo6em9vKHQoc29tJGNvZGVzW1sxXV0pKVxuICAgICAgICAgICAgICAgICAgICAgICAgICAgICBcbmBgYCJ9 -->
-<pre class="r"><code>neuron_timeseries&lt;-som$unit.classif
-info_timeSeries&lt;- data.frame(time_series=(rownames(t(timeseries.zoo))),
-                             neuron=as.integer(som$unit.classif))
-codes&lt;-zoo::zoo(t(som$codes[[1]]))
-                             </code></pre>
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-<!-- rnb-text-begin -->
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-<pre class="r"><code>zoo::plot.zoo(codes, plot.type = &quot;multiple&quot;, col = &quot;black&quot;, lwd = 2, main= &quot;Weight of neurons&quot;)</code></pre>
-<!-- rnb-source-end -->
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-<p><img 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" /></p>
-<!-- rnb-plot-end -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI3Bsb3QgYWxsIHRpbWUgc2VyaWVzIChuZXVyb25zKVxuZ2dwbG90Mjo6YXV0b3Bsb3QoKGNvZGVzKSwgZmFjZXQgPSBOVUxMKSArIHlsYWIoXCJ5XCIpICt4bGFiKFwidGltZVwiKSArIGdlb21fbGluZShzaXplID0gMSkgKyBnZ3RpdGxlKFwiV2VpZ2h0IG9mIG5ldXJvbnNcIilcbmBgYCJ9 -->
-<pre class="r"><code>#plot all time series (neurons)
-ggplot2::autoplot((codes), facet = NULL) + ylab(&quot;y&quot;) +xlab(&quot;time&quot;) + geom_line(size = 1) + ggtitle(&quot;Weight of neurons&quot;)</code></pre>
-<!-- rnb-source-end -->
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-<p><img 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" /></p>
-<!-- rnb-plot-end -->
-<!-- rnb-source-begin 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 -->
-<pre class="r"><code>library(proxy)
-library(stats)
-## use hierarchical clustering to cluster the codebook vectors.
-# Cut dendrogram in 4 groups
-hc &lt;- stats::hclust(dist(t(codes)),method = &quot;ward.D2&quot;)
-#Cuts a tree, e.g., as resulting from hclust, into several groups either
-#by specifying the desired number(s) of groups or the cut height(s).
-som_cluster &lt;- stats::cutree(hc, 4)
-library(dendextend)
-#plot dendrogram
-dend &lt;- hc%&gt;% as.dendrogram %&gt;%
-  set(&quot;branches_k_color&quot;, k = 4) %&gt;% set(&quot;branches_lwd&quot;, 1.2) %&gt;%
-  set(&quot;labels_cex&quot;, 0.8) %&gt;% set(&quot;labels_colors&quot;, k = 4) %&gt;%
-  set(&quot;leaves_pch&quot;, 19) %&gt;% set(&quot;leaves_cex&quot;, 0.5)
-ggd1 &lt;- as.ggdend(dend)
-ggplot(ggd1, horiz = FALSE) + ggtitle(&quot;Dendrogram&quot;)</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbWzEsIlJlbW92ZWQgMTUgcm93cyBjb250YWluaW5nIG1pc3NpbmcgdmFsdWVzIChnZW9tX3BvaW50KS4iXV0sImhlaWdodCI6NDMyLjYzMjksInNpemVfYmVoYXZpb3IiOjAsIndpZHRoIjo3MDB9 -->
-<p><img src="data:image/png;base64,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/></p>
-<!-- rnb-plot-end -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuI0NsdXN0ZXIgaXMgY3JlYXRlZCBieSBhIHNldCBvZiBuZXVyb25zLiBcbmNsdXN0ZXI8LWRhdGEuZnJhbWUoY2x1c3Rlcj1zb21fY2x1c3RlcixuZXVyb249IDE6ZGltKGNvZGVzKVsyXSlcbiNUaW1lc2VyaWVzLCBuZXVyb24gYW5kIGNsdXN0ZXJcbmNsdXN0ZXJfaW5mbzwtbWVyZ2UoY2x1c3RlcixpbmZvX3RpbWVTZXJpZXMsIGJ5PVwibmV1cm9uXCIpXG5jbHVzdGVyX2luZm9bb3JkZXIoY2x1c3Rlcl9pbmZvJHRpbWVfc2VyaWVzKSxdXG5gYGAifQ== -->
-<pre class="r"><code>#Cluster is created by a set of neurons.
-cluster&lt;-data.frame(cluster=som_cluster,neuron= 1:dim(codes)[2])
-#Timeseries, neuron and cluster
-cluster_info&lt;-merge(cluster,info_timeSeries, by=&quot;neuron&quot;)
-cluster_info[order(cluster_info$time_series),]</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-output-begin 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 -->
-<pre><code>   neuron cluster time_series
-       2       1        t1_1
-       2       1       t1_10
-      5       1        t1_2
-       1       1        t1_3
-       1       1        t1_4
-       2       1        t1_5
-       1       1        t1_6
-       1       1        t1_7
-       2       1        t1_8
-       2       1        t1_9
-      4       2        t2_1
-      3       2       t2_10
-      3       2        t2_2
-      4       2        t2_3
-      3       2        t2_4
-      4       2        t2_5
-      3       2        t2_6
-      4       2        t2_7
-      3       2        t2_8
-      4       2        t2_9
-     12       4        t3_1
-     12       4       t3_10
-     12       4        t3_2
-     16       4        t3_3
-     12       4        t3_4
-     12       4        t3_5
-     12       4        t3_6
-     16       4        t3_7
-     12       4        t3_8
-     16       4        t3_9
-     10       3        t4_1
-     14       3       t4_10
-      9       3        t4_2
-     13       3        t4_3
-     14       3        t4_4
-     10       3        t4_5
-      9       3        t4_6
-     13       3        t4_7
-     14       3        t4_8
-     13       3        t4_9</code></pre>
-<!-- rnb-output-end -->
-<!-- rnb-source-begin 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 -->
-<pre class="r"><code>#get the neurons that corresponds each group
-neuron_group1&lt;-which(som_cluster==1)
-neuron_group2&lt;-which(som_cluster==2)
-neuron_group3&lt;-which(som_cluster==3)
-neuron_group4&lt;-which(som_cluster==4)
-#Paint neurons and plot som with groups separated by hierarchical clustering.
-plot(som, type=&quot;codes&quot;,codeRendering=&quot;lines&quot;, bgcol = terrain.colors(4)[som_cluster], main = &quot;Clusters&quot;)</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-plot-begin eyJjb25kaXRpb25zIjpbXSwiaGVpZ2h0Ijo0MzIuNjMyOSwic2l6ZV9iZWhhdmlvciI6MCwid2lkdGgiOjcwMH0= -->
-<p><img 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" /></p>
-<!-- rnb-plot-end -->
-<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucGxvdChzb20sIHR5cGU9XCJtYXBwaW5nXCIsY29kZVJlbmRlcmluZz1cImxpbmVzXCIsIGJnY29sID0gdGVycmFpbi5jb2xvcnMoNClbc29tX2NsdXN0ZXJdLCBtYWluID0gXCJDbHVzdGVyc1wiKSBcbmtvaG9uZW46OmFkZC5jbHVzdGVyLmJvdW5kYXJpZXMoc29tLCBzb21fY2x1c3RlcilcbmBgYCJ9 -->
-<pre class="r"><code>plot(som, type=&quot;mapping&quot;,codeRendering=&quot;lines&quot;, bgcol = terrain.colors(4)[som_cluster], main = &quot;Clusters&quot;)
-kohonen::add.cluster.boundaries(som, som_cluster)</code></pre>
-<!-- rnb-source-end -->
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-<p><img 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-<!-- rnb-plot-end -->
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-<pre class="r"><code>#Plot the weight vector of neurons.
-#Are they similar with the time series set?
-v1&lt;-ggplot2::autoplot(codes[,neuron_group1], facet = NULL) + ylab(&quot;y&quot;) + geom_line(size = 1)
-v2&lt;-ggplot2::autoplot(codes[,neuron_group2], facet = NULL) + ylab(&quot;y&quot;) + geom_line(size = 1)
-v3&lt;-ggplot2::autoplot(codes[,neuron_group3], facet = NULL) + ylab(&quot;y&quot;) + geom_line(size = 1)
-v4&lt;-ggplot2::autoplot(codes[,neuron_group4], facet = NULL) + ylab(&quot;y&quot;) + geom_line(size = 1)
-gridExtra::grid.arrange(v1, v2,v3, v4, ncol=2,nrow=2, top=&quot;Weight of Neurons&quot;)</code></pre>
-<!-- rnb-source-end -->
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" /></p>
-<!-- rnb-plot-end -->
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-<pre class="r"><code>#plot the time series that are grouping in same cluster</code></pre>
-<!-- rnb-source-end -->
-<!-- rnb-chunk-end -->
-<!-- rnb-text-begin -->
-<!-- rnb-text-end -->
-<div 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