float(string[0]) / 10.0,
float(string[1]) / 10.0,
float(string[2]) / 10.0,
float(string[3]) / 10.0
]]
if string[4].rstrip() == "Iris-setosa":
x += [[1]]
else:
x += [[0]]
testSet += [x]
trainingFile.close()
testFile.close()
#TRAINING
nn.backPropagation(trainingSet, n, 4, nhidden, 1)
#TESTING
p = 0
for x, t in testSet:
O = nn.evaluate(x)
if ((1 - O[0]) < 0.1):
if (t[0] == 1):
p += 1
else:
if (t[0] == 0):
p += 1
print(str(p) + " out of " + str(len(testSet)) + " tests")
else:
x += [[0]]
testSet += [x]
trainingFile.close()
testFile.close()
errorPlot = []
mediaX = []
mediaY = []
#TRAINING
if len(sys.argv) > 6 and sys.argv[6] == ".":
nvalues =[0.5, 0.3, 0.1, 0.05, 0.01]
for i in range(5):
for j in range(5):
nn.backPropagation(trainingSet, nvalues[i] , 2, nhidden, 1)
if (mediaY == []):
mediaY = numpy.zeros(len(nn.errorX))
mediaX = nn.errorX
mediaY = sumaVector(mediaY, nn.errorY)
mediaY = [y/5 for y in mediaY]
errorPlot += [[mediaX,mediaY]]
print(">>"+str(len(errorPlot)))
i = 0
for x,y in errorPlot:
print(">>")
print (x)
print (y)
plt.plot(x, y, '-', label=str(nvalues[i]))
i+=1
from nn import NN
nn = NN()
train = [[[0, 0], [0]], [[0, 1], [1]], [[1, 0], [1]], [[1, 1], [0]]]
for x in train:
print(x)
nn.backPropagation(train, 0.5, 2, 3, 1)
print(nn.evaluate([0, 0]))
print(nn.evaluate([0, 1]))
print(nn.evaluate([1, 0]))
print(nn.evaluate([1, 1]))
else:
x += [[0]]
testSet += [x]
trainingFile.close()
testFile.close()
errorPlot = []
mediaX = []
mediaY = []
#TRAINING
if len(sys.argv) > 6 and sys.argv[6] == ".":
nvalues = [0.5, 0.3, 0.1, 0.05, 0.01]
for i in range(5):
for j in range(5):
nn.backPropagation(trainingSet, nvalues[i], 2, nhidden, 1)
if (mediaY == []):
mediaY = numpy.zeros(len(nn.errorX))
mediaX = nn.errorX
mediaY = sumaVector(mediaY, nn.errorY)
mediaY = [y / 5 for y in mediaY]
errorPlot += [[mediaX, mediaY]]
print(">>" + str(len(errorPlot)))
i = 0
for x, y in errorPlot:
print(">>")
print(x)
print(y)
plt.plot(x, y, '-', label=str(nvalues[i]))
i += 1
from nn import NN
nn = NN()
train = [[[0, 0], [0]], [[0, 1], [1]], [[1, 0], [1]], [[1, 1], [0]]]
for x in train:
print(x)
nn.backPropagation(train, 0.5, 2, 3, 1)
print(nn.evaluate([0, 0]))
print(nn.evaluate([0, 1]))
print(nn.evaluate([1, 0]))
print(nn.evaluate([1, 1]))
#TEST SET
for line in testFile:
string = line.split(",", 5)
x = [[float(string[0])/10.0,float(string[1])/10.0,float(string[2])/10.0,float(string[3])/10.0]]
if string[4].rstrip() == "Iris-setosa":
x += [[1]]
else:
x += [[0]]
testSet += [x]
trainingFile.close()
testFile.close()
#TRAINING
nn.backPropagation(trainingSet, n, 4, nhidden, 1)
#TESTING
p = 0
for x,t in testSet:
O = nn.evaluate(x)
if((1 - O[0]) < 0.1):
if (t[0] == 1):
p+=1
else:
if (t[0] == 0):
p+=1
print(str(p)+" out of "+str(len(testSet))+" tests")
