corrected some typos

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