def main():
dataSet = [[[0, 0], [0]], [[0, 1], [1]], [[1, 0], [1]], [[1, 1], [0]]]
learningRate = 0.5
numberOfTrainingSamples = 100000
trainingData = [
dataSet[np.random.randint(0, 4, size=None, dtype=int)]
for i in range(numberOfTrainingSamples)
]
network = nn.NeuralNetwork(2, 2, 1)
network.train(learningRate, trainingData)
for test in dataSet:
prediction = network.predict(test[0])
print(
f"Input: {test[0]} | Target: {test[1]} | Predicted: {prediction}")
def main():
#retrieve data from CSV file
with open('iris_dataset.csv') as dataFile:
data = np.array(list(csv.reader(dataFile)))
#create lists for training and testing
trainingSet = []
testingSet = []
count = 0
for iris in data:
dataVector = [float(i) for i in iris[:4]]
if iris[4] == 'Iris-setosa':
classificationVector = [1, 0, 0]
elif iris[4] == 'Iris-versicolor':
classificationVector = [0, 1, 0]
else:
classificationVector = [0, 0, 1]
sortData = [dataVector, classificationVector]
if count % 2 == 0:
trainingSet.append(sortData)
else:
testingSet.append(sortData)
count = count + 1
learningRate = 1.2
testNetwork = nn.NeuralNetwork(4, 6, 3)
trainingSetPlus = trainingSet[:]
for i in range(100):
random.shuffle(trainingSetPlus)
trainingSetPlus = trainingSetPlus + trainingSet[:]
random.shuffle(trainingSetPlus)
for i in range(1000):
random.shuffle(trainingSet)
network = nn.NeuralNetwork(4, 6, 3)
network.train(learningRate, trainingSet)
#build confusion matrix
'''
+------------+---------+------------+-----------+
| | Setosa | Versicolor | Virginica |
+------------+---------+------------+-----------+
| Setosa | TP(S) | E(S,Ve) | E(S,Vi) |
| Versicolor | E(Ve,S) | TP(Ve) | E(Ve,Vi) |
| Virginica | E(Vi,S) | E(Vi,Ve) | TP(Vi) |
+------------|---------+------------+-----------|
'''
confusionMatrix = np.zeros(
shape=(3, 3)) #creates a 2D array (3x3 table) containing all zeros
for test in testingSet:
prediction = network.predict(test[0])
print(prediction)
#find classification
highestIndex = 0
for index in range(1, len(prediction)):
if prediction[index] > prediction[highestIndex]:
highestIndex = index
if highestIndex == 0:
prediction = 'Iris-setosa'
elif highestIndex == 1:
prediction = 'Iris-versicolor'
else:
prediction = 'Iris-virginica'
#make a prediction for the current test case
classification = test[1].index(1)
row = UpdateTable(classification)
col = UpdateTable(prediction)
confusionMatrix[row, col] = confusionMatrix[
row, col] + 1 #increment corresponding cell in the matrix
print(confusionMatrix)
