def testold():
perceptron_h = myp.Perceptron(100)
perceptron_e = myp.Perceptron(100)
perceptron_u = myp.Perceptron(100)
trainingProteins = []
for aastr,targetstr in mydata.trainingData():
trainingProteins.append(Protein(aastr,targetstr))
testingProteins = []
for aastr,targetstr in mydata.testData():
testingProteins.append(Protein(aastr,targetstr))
inputs_targets = []
for p in trainingProteins:
inputs_targets.extend(zip(p.inputss,p.targetss))
def train():
random.shuffle(inputs_targets)
for inputs,targets in inputs_targets:
perceptron_h.train(inputs,targets['H'])
perceptron_e.train(inputs,targets['E'])
perceptron_u.train(inputs,targets['U'])
def test():
for protein in testingProteins:
print (protein.acids)
print (protein.types)
sys.stdout.write('\033[91m')
i = 0
for inputs,targets in zip(protein.inputss,protein.targetss):
i = i + 1
if i < 3:
continue
highestOutput = perceptron_h.test(inputs);
bestPrediction = 'H';
e = perceptron_e.test(inputs)
if e > highestOutput:
highestOutput = e;
bestPrediction = 'E'
u = perceptron_u.test(inputs)
if u > highestOutput:
highestOutput = u;
bestPrediction = '_'
sys.stdout.write(bestPrediction)
print('\033[0m')
for i in range(5):
print i
train()
test()
return 0
def testpybrain():
X = list()
yH = list()
yE = list()
yU = list()
perceptron = buildNetwork(100, 5, 3, bias=True)
trainingProteins = []
for aastr,targetstr in mydata.trainingData():
trainingProteins.append(Protein(aastr,targetstr))
testingProteins = []
for aastr,targetstr in mydata.testData():
testingProteins.append(Protein(aastr,targetstr))
inputs_targets = []
for p in trainingProteins:
inputs_targets.extend(zip(p.inputss,p.targetss))
ds = SupervisedDataSet(100,3)
for inputs,targets in inputs_targets:
ds.addSample(inputs,(targets['H'],targets['E'], targets['U']))
trainer = BackpropTrainer(perceptron, ds)
def test():
alltargets = StringIO()
allpredictions = StringIO()
for protein in testingProteins:
alltargets.write(protein.types)
i = 0;
for inputs in protein.inputss:
i = i + 1
if i < 3:
continue
highestOutput = 0
bestPrediction = 'H'
for prediction,value in zip(perceptron.activate(inputs),('H','E','_')):
if prediction > highestOutput:
highestOutput = prediction;
bestPrediction = value
allpredictions.write(bestPrediction)
ch = cc()
ce = cc()
cu = cc()
ch,ce,cu = calculatecoef(alltargets.getvalue(),allpredictions.getvalue())
yH.append(ch.performance())
yE.append(ce.performance())
yU.append(cu.performance())
print(ch.performance(),ce.performance(),cu.performance())
for i in range(1,100):
print i
X.append(i)
trainer.train()
test()
plt.plot(X,yH,c='red', label = "Alpha Helix")
plt.plot(X,yE,c='blue', label = "Beta Sheet")
plt.plot(X,yU,c='green', label = "Coil")
plt.xlabel("Iteration")
plt.ylabel("Correlation Coefficient")
plt.legend()
plt.show()
print(perceptron_h.weights)
print(perceptron_e.weights)
print(perceptron_u.weights)
return 0
def main(): #testpybrain() testingProteins = [] for aastr,targetstr in mydata.testData(): print aastr
def testnew():
X = list()
yH = list()
yE = list()
yU = list()
perceptron_h = myp.Perceptron(100)
perceptron_e = myp.Perceptron(100)
perceptron_u = myp.Perceptron(100)
trainingProteins = []
for aastr,targetstr in mydata.trainingData():
trainingProteins.append(Protein(aastr,targetstr))
testingProteins = []
for aastr,targetstr in mydata.testData():
testingProteins.append(Protein(aastr,targetstr))
inputs_targets = []
for p in trainingProteins:
inputs_targets.extend(zip(p.inputss,p.targetss))
def train():
random.shuffle(inputs_targets)
for inputs,targets in inputs_targets:
perceptron_h.train(inputs,targets['H'])
perceptron_e.train(inputs,targets['E'])
perceptron_u.train(inputs,targets['U'])
def test():
alltargets = StringIO()
allpredictions = StringIO()
for protein in testingProteins:
alltargets.write(protein.types)
i = 0;
for inputs,targets in zip(protein.inputss,protein.targetss):
i = i + 1
if i < 3:
continue
highestOutput = perceptron_h.test(inputs);
bestPrediction = 'H';
e = perceptron_e.test(inputs)
if e > highestOutput:
highestOutput = e;
bestPrediction = 'E'
u = perceptron_u.test(inputs)
if u > highestOutput:
highestOutput = u;
bestPrediction = '_'
allpredictions.write(bestPrediction)
ch,ce,cu = calculatecoef(alltargets.getvalue(),allpredictions.getvalue())
yH.append(ch.performance())
yE.append(ce.performance())
yU.append(cu.performance())
print('')
for i in range(1,100):
print i
X.append(i)
train()
test()
plt.plot(X,yH,c='red', label = "Alpha Helix")
plt.plot(X,yE,c='blue', label = "Beta Sheet")
plt.plot(X,yU,c='green', label = "Coil")
plt.xlabel("Iteration")
plt.ylabel("Correlation Coefficient")
plt.legend()
plt.show()
print(perceptron_h.weights)
print(perceptron_e.weights)
print(perceptron_u.weights)
return 0