precision = tp / (tp + fp)
f_score = (2 * (precision * recall)) / (precision + recall)
print("The recall is: {0}".format(recall))
print("The precision is: {0}".format(precision))
print("The F-score is: {0}".format(f_score))
if __name__ == '__main__':
#location containing the training set
location = os.path.dirname(os.path.abspath(__file__))
training_set_location = os.path.join(location,
'extras/files/training_set1.csv')
#read training data
training_set = read_csv(training_set_location)
#remove the data lables
labels = training_set.pop(0)
neuron = NeuralNetwork(training_set, 1, 0.1, 0.2)
print(neuron.weights)
neuron.output_weights()
print(neuron.normalized)
neuron.iterate()
#neuron.test()
#backprop
# neuron = NeuralNetwork(training_set, 2, 0.1, 0.8, bias = 0, hidden_layer = 2)
# print(neuron.weights)
to_consider = results[:neigbours] #since its an ordered list
to_consider = [x[-1] for x in to_consider]
voting_results = {data: to_consider.count(data) for data in to_consider}
max_key = max(voting_results)
return max_key
if __name__ == '__main__':
file_loation = os.path.dirname(os.path.abspath(__file__))
location = os.path.join(file_loation, 'extras/files/training_set1.csv')
#get the training set
training_set = read_csv(location)
#remove the data lables
labels = training_set.pop(0)
#get the users input
query_set, neigbours = get_input(labels)
#calculate euclidean distances
response = get_euclidean_distances(training_set, query_set)
#voting
winning_label = vote(response, neigbours)
print("The query set belongs to the label '{0}'.".format(winning_label))