fixed raster plots

Author: iJaddu

and_net.py

@@ -81,12 +81,12 @@ def run_net(self, input, time = run_time):
         outIn = []
         for i in range(time):
             #Change to and, or, ^
-            if(self.neurons[0].output[i] ^ self.neurons[1].output[i]):
+            if(self.neurons[0].output[i] and self.neurons[1].output[i]):
                 outIn.append(1)
             else:
                 outIn.append(0)
         #Conversion factor scales # of spikes from output
         #AND: .7
         #OR: .06
         #XOR: .6
-        self.neurons[3].sim(self.spikes_to_current(outIn, .6), time)
+        self.neurons[3].sim(self.spikes_to_current(outIn, .7), time)

training.py

@@ -1,5 +1,5 @@
 from and_net import and_net
-#import matplotlib.pyplot as plt
+import matplotlib.pyplot as plt
 
 net = and_net([], [])
 net.create_simple_net()
@@ -16,42 +16,42 @@
 j = 0
 k = 0
 
-"""
-def raster_plot(net):
-    plt.figure()
-    plt.title('Raster plot for network activity')
+
+def raster_plot(net, length):
+    # plt.title('Raster plot for network activity')
+    plt.figure('Raster plot for network activity')
 
     plt.subplot(3, 1, 1)
     plt.xlabel('Time')
     plt.ylabel('Input Neuron 1')
-    for i in range(1000):
+    # length = len(net.neurons[0].output)
+    plt.xlim(0, 100)
+    for i in range(length):
         if net.neurons[0].output[i] == 1:
-            #fmt = '[|][-][k]'
-            #plt.plot(fmt)
             plt.axvline(i)
 
     plt.subplot(3, 1, 2)
     plt.xlabel('Time')
     plt.ylabel('Input Neuron 2')
-    for j in range(1000):
+    plt.xlim(0, 100)
+    # length = len(net.neurons[1].output)
+    for j in range(length):
         if net.neurons[1].output[j] == 1:
-            #fmt = '[|][-][k]'
-            #plt.plot(fmt)
             plt.axvline(j)
 
     plt.subplot(3, 1, 3)
     plt.xlabel('Time')
     plt.ylabel('Output Neuron')
-    for k in range(1000):
+    plt.xlim(0, 100)
+    # length = len(net.neurons[3].output)
+    for k in range(length):
         if net.neurons[3].output[k] == 1:
-            #fmt = '[|][-][k]'
-            #plt.plot(fmt)
             plt.axvline(k)
 
     plt.show()
-"""
 
-#lines = open(input("Enter Filepath: "), "r").readlines()
+
+# lines = open(input("Enter Filepath: "), "r").readlines()
 lines = open("data.txt", "r").readlines()
 runs = len(lines)
 for i in range(runs):
@@ -68,23 +68,23 @@ def raster_plot(net):
     net.run_net(data[r])
     time = len(net.neurons[0].output)
     w_max = 1
-    for i in range(int(time/100)):
-        #Finds rate for each neuron in a time step of 100
-        v_x = sum(net.neurons[0].output[100*i:100*(i+1)])
-        v_y = sum(net.neurons[1].output[100*i:100*(i+1)])
-        v_o = sum(net.neurons[3].output[100*i:100*(i+1)])
-        #Sets a_corr value
+    for i in range(int(time / 100)):
+        # Finds rate for each neuron in a time step of 100
+        v_x = sum(net.neurons[0].output[100 * i:100 * (i + 1)])
+        v_y = sum(net.neurons[1].output[100 * i:100 * (i + 1)])
+        v_o = sum(net.neurons[3].output[100 * i:100 * (i + 1)])
+        # Sets a_corr value
         a_corr_x = w_max - net.connections[0].weight
         a_corr_y = w_max - net.connections[1].weight
-        #If the rate is below 5, set value to 0
+        # If the rate is below 5, set value to 0
         if v_x <= 10:
             v_x = 0
         if v_y <= 10:
             v_y = 0
         if v_o <= 10:
             v_o = 0
-        #Hebb with postsynaptic LTP/LTD threshold
-        #TODO change .01 to something else?
+        # Hebb with postsynaptic LTP/LTD threshold
+        # TODO change .01 to something else?
         net.connections[0].weight += a_corr_x * v_x * (v_o - .01)
         net.connections[1].weight += a_corr_y * v_y * (v_o - .01)
         if net.connections[0].weight < 0:
@@ -98,9 +98,14 @@ def raster_plot(net):
     x_spikes = sum(net.neurons[0].output)
     y_spikes = sum(net.neurons[1].output)
     o_spikes = sum(net.neurons[3].output)
-    #Change operator to and, or, ^
-    print((data[r][0] ^ data[r][1]), "||", net.spikes_to_binary(net.neurons[3]), "\nX =", 
-    net.connections[0].weight, x_spikes, "\nY =", net.connections[1].weight, y_spikes, "\n", o_spikes, "\n")
+    # Change operator to and, or, ^
+    print((data[r][0] and data[r][1]), "||", net.spikes_to_binary(net.neurons[3]), "\nX =",
+          net.connections[0].weight, x_spikes, "\nY =", net.connections[1].weight, y_spikes, "\n", o_spikes, "\n")
+
+    raster_plot(net, time)
     for i in range(4):
         net.neurons[i].clear()
-#raster_plot(net)
+
+# net.run_net(data[r])
+# time = len(net.neurons[0].output)
+#raster_plot(net, time)