corrected some typos

doc/pub/week43/html/week43-bs.html

@@ -1458,10 +1458,11 @@ predicted<span style="color: #666666">=</span>np<span style="color: #666666">.</
 trainScore <span style="color: #666666">=</span> model<span style="color: #666666">.</span>evaluate(trainX, trainY, verbose<span style="color: #666666">=0</span>)
 <span style="color: #008000">print</span>(trainScore)
 
-index <span style="color: #666666">=</span> df<span style="color: #666666">.</span>index<span style="color: #666666">.</span>values
-plt<span style="color: #666666">.</span>plot(index,df)
-plt<span style="color: #666666">.</span>plot(index,predicted)
-plt<span style="color: #666666">.</span>axvline(df<span style="color: #666666">.</span>index[Tp], c<span style="color: #666666">=</span><span style="color: #BA2121">&quot;r&quot;</span>)
+
+df <span style="color: #666666">=</span> pd<span style="color: #666666">.</span>DataFrame(x)
+pred <span style="color: #666666">=</span> pd<span style="color: #666666">.</span>DataFrame(predicted)
+plt<span style="color: #666666">.</span>plot(df,c<span style="color: #666666">=</span><span style="color: #BA2121">&quot;b&quot;</span>)
+plt<span style="color: #666666">.</span>plot(pred,c<span style="color: #666666">=</span><span style="color: #BA2121">&quot;r&quot;</span>)
 plt<span style="color: #666666">.</span>show()
 </pre>
 </div>
@@ -1544,24 +1545,24 @@ y_tot <span style="color: #666666">=</span> np<span style="color: #666666">.</sp
 <p>The way the recurrent neural networks are trained in this program
 differs from how machine learning algorithms are usually trained.
 Typically a machine learning algorithm is trained by learning the
-relationship between the x data and the y data.  In this program, the
+relationship between the \( x \) data and the \( y \) data.  In this program, the
 recurrent neural network will be trained to recognize the relationship
-in a sequence of y values.  This is type of data formatting is
-typically used time series forcasting, but it can also be used in any
+in a sequence of \( y \) values.  This is type of data formatting is
+typically used for time series forecasting, but it can also be used in any
 extrapolation (time series forecasting is just a specific type of
 extrapolation along the time axis).  This method of data formatting
-does not use the x data and assumes that the y data are evenly spaced.
+does not use the \( x \) data and assumes that the \( y \) data are evenly spaced.
 </p>
 
 <p>For a standard machine learning algorithm, the training data has the
-form of (x,y) so the machine learning algorithm learns to assiciate a
-y value with a given x value.  This is useful when the test data has x
+form of \( (x,y) \) so the machine learning algorithm learns to associate a
+\( y \) value with a given \( x \) value.  This is useful when the test data has \( x \)
 values within the same range as the training data.  However, for this
 application, the x values of the test data are outside of the x values
 of the training data and the traditional method of training a machine
 learning algorithm does not work as well.  For this reason, the
 recurrent neural network is trained on sequences of y values of the
-form ((y1, y2), y3), so that the network is concerned with learning
+form \( ((y1, y2), y3) \), so that the network is concerned with learning
 the pattern of the y data and not the relation between the x and y
 data.  As long as the pattern of y data outside of the training region
 stays relatively stable compared to what was inside the training

doc/pub/week43/html/week43-reveal.html

@@ -1417,10 +1417,11 @@ predicted=np.concatenate((trainPredict,testPredict),axis=<span style="color: #B4
 trainScore = model.evaluate(trainX, trainY, verbose=<span style="color: #B452CD">0</span>)
 <span style="color: #658b00">print</span>(trainScore)
 
-index = df.index.values
-plt.plot(index,df)
-plt.plot(index,predicted)
-plt.axvline(df.index[Tp], c=<span style="color: #CD5555">&quot;r&quot;</span>)
+
+df = pd.DataFrame(x)
+pred = pd.DataFrame(predicted)
+plt.plot(df,c=<span style="color: #CD5555">&quot;b&quot;</span>)
+plt.plot(pred,c=<span style="color: #CD5555">&quot;r&quot;</span>)
 plt.show()
 </pre>
 </div>
@@ -1503,24 +1504,24 @@ y_tot = np.array([-<span style="color: #B452CD">0.03077640549</span>, -<span sty
 <p>The way the recurrent neural networks are trained in this program
 differs from how machine learning algorithms are usually trained.
 Typically a machine learning algorithm is trained by learning the
-relationship between the x data and the y data.  In this program, the
+relationship between the \( x \) data and the \( y \) data.  In this program, the
 recurrent neural network will be trained to recognize the relationship
-in a sequence of y values.  This is type of data formatting is
-typically used time series forcasting, but it can also be used in any
+in a sequence of \( y \) values.  This is type of data formatting is
+typically used for time series forecasting, but it can also be used in any
 extrapolation (time series forecasting is just a specific type of
 extrapolation along the time axis).  This method of data formatting
-does not use the x data and assumes that the y data are evenly spaced.
+does not use the \( x \) data and assumes that the \( y \) data are evenly spaced.
 </p>
 
 <p>For a standard machine learning algorithm, the training data has the
-form of (x,y) so the machine learning algorithm learns to assiciate a
-y value with a given x value.  This is useful when the test data has x
+form of \( (x,y) \) so the machine learning algorithm learns to associate a
+\( y \) value with a given \( x \) value.  This is useful when the test data has \( x \)
 values within the same range as the training data.  However, for this
 application, the x values of the test data are outside of the x values
 of the training data and the traditional method of training a machine
 learning algorithm does not work as well.  For this reason, the
 recurrent neural network is trained on sequences of y values of the
-form ((y1, y2), y3), so that the network is concerned with learning
+form \( ((y1, y2), y3) \), so that the network is concerned with learning
 the pattern of the y data and not the relation between the x and y
 data.  As long as the pattern of y data outside of the training region
 stays relatively stable compared to what was inside the training

doc/pub/week43/html/week43-solarized.html

@@ -1393,10 +1393,11 @@ predicted=np.concatenate((trainPredict,testPredict),axis=<span style="color: #B4
 trainScore = model.evaluate(trainX, trainY, verbose=<span style="color: #B452CD">0</span>)
 <span style="color: #658b00">print</span>(trainScore)
 
-index = df.index.values
-plt.plot(index,df)
-plt.plot(index,predicted)
-plt.axvline(df.index[Tp], c=<span style="color: #CD5555">&quot;r&quot;</span>)
+
+df = pd.DataFrame(x)
+pred = pd.DataFrame(predicted)
+plt.plot(df,c=<span style="color: #CD5555">&quot;b&quot;</span>)
+plt.plot(pred,c=<span style="color: #CD5555">&quot;r&quot;</span>)
 plt.show()
 </pre>
 </div>
@@ -1479,24 +1480,24 @@ y_tot = np.array([-<span style="color: #B452CD">0.03077640549</span>, -<span sty
 <p>The way the recurrent neural networks are trained in this program
 differs from how machine learning algorithms are usually trained.
 Typically a machine learning algorithm is trained by learning the
-relationship between the x data and the y data.  In this program, the
+relationship between the \( x \) data and the \( y \) data.  In this program, the
 recurrent neural network will be trained to recognize the relationship
-in a sequence of y values.  This is type of data formatting is
-typically used time series forcasting, but it can also be used in any
+in a sequence of \( y \) values.  This is type of data formatting is
+typically used for time series forecasting, but it can also be used in any
 extrapolation (time series forecasting is just a specific type of
 extrapolation along the time axis).  This method of data formatting
-does not use the x data and assumes that the y data are evenly spaced.
+does not use the \( x \) data and assumes that the \( y \) data are evenly spaced.
 </p>
 
 <p>For a standard machine learning algorithm, the training data has the
-form of (x,y) so the machine learning algorithm learns to assiciate a
-y value with a given x value.  This is useful when the test data has x
+form of \( (x,y) \) so the machine learning algorithm learns to associate a
+\( y \) value with a given \( x \) value.  This is useful when the test data has \( x \)
 values within the same range as the training data.  However, for this
 application, the x values of the test data are outside of the x values
 of the training data and the traditional method of training a machine
 learning algorithm does not work as well.  For this reason, the
 recurrent neural network is trained on sequences of y values of the
-form ((y1, y2), y3), so that the network is concerned with learning
+form \( ((y1, y2), y3) \), so that the network is concerned with learning
 the pattern of the y data and not the relation between the x and y
 data.  As long as the pattern of y data outside of the training region
 stays relatively stable compared to what was inside the training

doc/pub/week43/html/week43.html

@@ -1470,10 +1470,11 @@ predicted<span style="color: #666666">=</span>np<span style="color: #666666">.</
 trainScore <span style="color: #666666">=</span> model<span style="color: #666666">.</span>evaluate(trainX, trainY, verbose<span style="color: #666666">=0</span>)
 <span style="color: #008000">print</span>(trainScore)
 
-index <span style="color: #666666">=</span> df<span style="color: #666666">.</span>index<span style="color: #666666">.</span>values
-plt<span style="color: #666666">.</span>plot(index,df)
-plt<span style="color: #666666">.</span>plot(index,predicted)
-plt<span style="color: #666666">.</span>axvline(df<span style="color: #666666">.</span>index[Tp], c<span style="color: #666666">=</span><span style="color: #BA2121">&quot;r&quot;</span>)
+
+df <span style="color: #666666">=</span> pd<span style="color: #666666">.</span>DataFrame(x)
+pred <span style="color: #666666">=</span> pd<span style="color: #666666">.</span>DataFrame(predicted)
+plt<span style="color: #666666">.</span>plot(df,c<span style="color: #666666">=</span><span style="color: #BA2121">&quot;b&quot;</span>)
+plt<span style="color: #666666">.</span>plot(pred,c<span style="color: #666666">=</span><span style="color: #BA2121">&quot;r&quot;</span>)
 plt<span style="color: #666666">.</span>show()
 </pre>
 </div>
@@ -1556,24 +1557,24 @@ y_tot <span style="color: #666666">=</span> np<span style="color: #666666">.</sp
 <p>The way the recurrent neural networks are trained in this program
 differs from how machine learning algorithms are usually trained.
 Typically a machine learning algorithm is trained by learning the
-relationship between the x data and the y data.  In this program, the
+relationship between the \( x \) data and the \( y \) data.  In this program, the
 recurrent neural network will be trained to recognize the relationship
-in a sequence of y values.  This is type of data formatting is
-typically used time series forcasting, but it can also be used in any
+in a sequence of \( y \) values.  This is type of data formatting is
+typically used for time series forecasting, but it can also be used in any
 extrapolation (time series forecasting is just a specific type of
 extrapolation along the time axis).  This method of data formatting
-does not use the x data and assumes that the y data are evenly spaced.
+does not use the \( x \) data and assumes that the \( y \) data are evenly spaced.
 </p>
 
 <p>For a standard machine learning algorithm, the training data has the
-form of (x,y) so the machine learning algorithm learns to assiciate a
-y value with a given x value.  This is useful when the test data has x
+form of \( (x,y) \) so the machine learning algorithm learns to associate a
+\( y \) value with a given \( x \) value.  This is useful when the test data has \( x \)
 values within the same range as the training data.  However, for this
 application, the x values of the test data are outside of the x values
 of the training data and the traditional method of training a machine
 learning algorithm does not work as well.  For this reason, the
 recurrent neural network is trained on sequences of y values of the
-form ((y1, y2), y3), so that the network is concerned with learning
+form \( ((y1, y2), y3) \), so that the network is concerned with learning
 the pattern of the y data and not the relation between the x and y
 data.  As long as the pattern of y data outside of the training region
 stays relatively stable compared to what was inside the training

doc/src/week43/week43.do.txt

@@ -921,11 +921,13 @@ predicted=np.concatenate((trainPredict,testPredict),axis=0)
 trainScore = model.evaluate(trainX, trainY, verbose=0)
 print(trainScore)
 
-index = df.index.values
-plt.plot(index,df)
-plt.plot(index,predicted)
-plt.axvline(df.index[Tp], c="r")
+
+df = pd.DataFrame(x)
+pred = pd.DataFrame(predicted)
+plt.plot(df,c="b")
+plt.plot(pred,c="r")
 plt.show()
+
 !ec
 
 
@@ -976,23 +978,23 @@ y_tot = np.array([-0.03077640549, -0.08336233266, -0.1446729567, -0.2116753732,
 The way the recurrent neural networks are trained in this program
 differs from how machine learning algorithms are usually trained.
 Typically a machine learning algorithm is trained by learning the
-relationship between the x data and the y data.  In this program, the
+relationship between the $x$ data and the $y$ data.  In this program, the
 recurrent neural network will be trained to recognize the relationship
-in a sequence of y values.  This is type of data formatting is
-typically used time series forcasting, but it can also be used in any
+in a sequence of $y$ values.  This is type of data formatting is
+typically used for time series forecasting, but it can also be used in any
 extrapolation (time series forecasting is just a specific type of
 extrapolation along the time axis).  This method of data formatting
-does not use the x data and assumes that the y data are evenly spaced.
+does not use the $x$ data and assumes that the $y$ data are evenly spaced.
 
 For a standard machine learning algorithm, the training data has the
-form of (x,y) so the machine learning algorithm learns to assiciate a
-y value with a given x value.  This is useful when the test data has x
+form of $(x,y)$ so the machine learning algorithm learns to associate a
+$y$ value with a given $x$ value.  This is useful when the test data has $x$
 values within the same range as the training data.  However, for this
 application, the x values of the test data are outside of the x values
 of the training data and the traditional method of training a machine
 learning algorithm does not work as well.  For this reason, the
 recurrent neural network is trained on sequences of y values of the
-form ((y1, y2), y3), so that the network is concerned with learning
+form $((y1, y2), y3)$, so that the network is concerned with learning
 the pattern of the y data and not the relation between the x and y
 data.  As long as the pattern of y data outside of the training region
 stays relatively stable compared to what was inside the training