def predict(path, weightPath1, weightPath2, method):
predictionPath = "predictions.txt"
predictions = []
limit = 500
# load
weight1 = np.load(weightPath1)
weight2 = np.load(weightPath2)
method = method.lower()
if(method == 'fasta'):
proteins = fileUtils.readFasta(path)
for proteinId in range(len(proteins)):
sequences = sequenceUtils.openReadingFrames(proteins[proteinId])
for pos in range(len(sequences)):
# lav sekvens om til binær
inputLayer = sequenceUtils.createInputLayer(sequences[pos])
# forward
outputLayer = forward(inputLayer, weight1, weight2)[1]
outputLayer = logTransform.invTransform(outputLayer)
if(outputLayer <= limit):
# plus one, since both are zero indexed
predictions.append([proteinId + 1, pos + 1])
np.savetxt(predictionPath, np.array(predictions), fmt = '%d', delimiter = '\t')
print("There is {} predicted epitopes.".format(len(predictions)))
machine0 = []
# all affinity predictions
machine1 = []
# predicted epitopes
machine = [[], [], []]
weight1 = np.load("weight1.npy")
weight2 = np.load("weight2.npy")
# predict
allSequences = [[], [], [], [], [], [], [], [], []]
for proteinId in range(len(hivProteins)):
allSequences[proteinId] = sequenceUtils.openReadingFrames(hivProteins[proteinId])
for pos in range(len(allSequences[proteinId])):
## machine
# save sequence
machine0.append(allSequences[proteinId][pos])
# lav sekvens om til binær
inputLayer = sequenceUtils.createInputLayer(allSequences[proteinId][pos])
# forward
outputLayer = forward(inputLayer, weight1, weight2)[1]
outputLayer = logTransform.invTransform(outputLayer)
# save prediction
