def analyzeId(c_id):
startTime = time.time()
count2 =0
itr = 0
if (c_id>0):
network = NeuralNetwork(inputNodes = 5, hiddenNodes = 18, outputNodes = 1) #company id >0 for individual prediciton
averageError = 1
while (averageError>0.01 and itr<2000):
#for i in range(1000):
count1 =0
resultBackProp =[]
while (count1<295):
trainingData = getTrainingData(c_id,count1)
resultBackProp.append(network.train(trainingData))
count1 = count1 +1
averageError = sum(resultBackProp)/len(resultBackProp)
print ("avgError = ", averageError)
#==================================================================
# get data for testing
testError =[]
while (count2<95):
testData = getTestData(c_id,count2)
testError.append(network.accuracyTest(testData))
count2 = count2 + 1
testErrorAverage = sum(testError)/len(testError)
#====================================================================
# get data for tomorrow's prediction
predictionData = getPredictionData(c_id)
# get prediction result
returnPrice = network.test(predictionData)
# de-normalize and return predicted stock price
final = InitialCalculation()
predictedStockPrice = final.denormalizePrice(returnPrice)
# create return object, including the accuracy of the testing
returnData ={}
returnData['price'] = predictedStockPrice
#returnData['accuracy'] = accuracy
returnData['error'] = testErrorAverage
returnData['time'] = time.time() - startTime
print(returnData)
else:
nep_network = NeuralNetwork(inputNodes = 8, hiddenNodes = 20, outputNodes = 1) #company id =0 for nepse prediciton
averageError = 1
while (averageError>0.03 ):
#for i in range(1000):
count1 =0
resultBackProp =[]
#print(getTrainingData(c_id,count1))
while (count1<95):
trainingData = getTrainingData(c_id,count1)
resultBackProp.append(nep_network.trainNepse(trainingData))
count1 = count1 +1
averageError = sum(resultBackProp)/len(resultBackProp)
print ("avgError = ", averageError)
#==================================================================
# get data for testing
# considering 50 test data, we nedd 45 loops
testError =[]
while (count2<10):
testData = getTestData(c_id,count2)
testError.append(nep_network.accuracyTestNepse(testData))
count2 = count2 + 1
testErrorAverage = sum(testError)/len(testError)
#print ("test error =", testError)
#print("te average = ", testErrorAverage)
#accuracy = (1- testErrorAverage)*100
#====================================================================
# get data for tomorrow's prediction
predictionData = getPredictionData(c_id)
# get prediction result
returnPrice = nep_network.test(predictionData)
# de-normalize and return predicted stock price
final = InitialCalculation()
predictedStockPrice = final.denormalizePriceNepse(returnPrice)
# create return object, including the accuracy of the testing
returnData ={}
returnData['price'] = predictedStockPrice
#returnData['accuracy'] = accuracy
returnData['error'] = testErrorAverage
returnData['time'] = time.time() - startTime
print(returnData)
