added graph and additional test

Author: edwardyang12

Summer20/NeuralNetwork/tfsin.py

@@ -5,8 +5,6 @@
 import random
 import matplotlib.pyplot as plt
 
-number = 100
-
 # randomPoints on the sin graph
 # number - number of points
 # angles - tuple for range of angles
@@ -24,38 +22,63 @@ def randomPoints(number, angles= (-360,360)):
 # only specific points on the sin graph
 # number - number of points
 # angles - target angle
-def selectPoints(number, angle=30):
+def selectPoints(number, angle=90):
         angleList = []
         sinList = []
+        options = [angle+90,angle+180,angle+270,angle-90,angle-180,angle-270]
         while(number>0):
+            angle = random.choice(options)
             radangle = angle * math.pi / 180 # convert to radians
             angleList.append([radangle])
             sinList.append([math.sin(radangle)])
-            angleList.append([-radangle])
-            sinList.append([math.sin(-radangle)])
-            angle = angle + 60
             number = number -1
         return angleList, sinList
 
-angles = (-360,360)
-angleList, sinList = randomPoints(10000, angles)
-#angleList, sinList = selectPoints(10000)
 
+
+# generate training data
+angles = (-360,360) # represents full system dynamics
+#angles = (0,90) # doesn't represent full system dynamics
+
+angleList, sinList = randomPoints(10000, angles) # good number of points and random points
+#angleList, sinList = randomPoints(100, angles) # sparse points and random points
+#angleList, sinList = selectPoints(10000) # good number of points but not representative points
+
+
+
+# neural network code
 model = models.Sequential()
 model.add(layers.Dense(10, activation='tanh', input_shape=(1,)))
 model.add(layers.Dense(1, activation=None))
 model.compile(optimizer='Adam',
                 loss=losses.MeanSquaredError(),
                 metrics=['mean_squared_error'])
+
 history = model.fit(np.array(angleList),np.array(sinList), epochs=200)
-plt.plot(history.history['mean_squared_error'], label='mean_squared_error')
-plt.xlabel('Epoch')
-plt.ylabel('MSE')
-plt.ylim([0.0, 0.2])
-plt.legend(loc='lower right')
 
+# plots out learning curve
+# plt.plot(history.history['mean_squared_error'], label='mean_squared_error')
+# plt.xlabel('Epoch')
+# plt.ylabel('MSE')
+# plt.ylim([0.0, 0.2])
+# plt.legend(loc='lower right')
+# plt.show()
+
+# generate test data
 angleTest, sinTest = randomPoints(10)
 print(model.predict(np.array(angleTest)))
 print(sinTest)
 
+
+# graph of the test, train points
+graph = plt.figure()
+ax = graph.add_subplot(111)
+
+x = np.linspace(-math.pi*2,math.pi*2,300)
+y = np.sin(x)
+
+plt.plot(x,y, label= 'y = sin(x)', markersize = 2, c='c')
+ax.scatter(angleList,sinList, label = 'training', c='b')
+ax.scatter(angleTest,model.predict(np.array(angleTest)), label = 'testing', c='r')
+plt.legend(loc='lower right')
 plt.show()