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diff --git a/python/ann.py b/python/ann.py
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+from numpy import *
+from math import sqrt
+from copy import deepcopy
+from time import time
+
+class ANN:
+	
+	"""ANN with one hidden layer, one output and full connections in between consecutive layers.
+	Initial weights are chosen from a normal distribution.
+	Activation function is tanh."""
+	
+	INIT_SIGMA = 0.02
+	REL_STOP_MARGIN = 0.01
+	MAX_ITERATIONS = 1000000
+	ACTIVATION = tanh
+	D_ACTIVATION = lambda x: 1 - tanh(x)**2 # Derivative of tanh
+	VEC_ACTIVATION = vectorize(ACTIVATION)
+	VEC_D_ACTIVATION = vectorize(D_ACTIVATION)
+	STEP_SIZE = 0.1
+	
+	def __init__(self, input_size, hidden_size):
+		
+		#self.input_size = input_size
+		#self.hidden_size = hidden_size
+		self.hidden_weights = random.normal(0, ANN.INIT_SIGMA, (hidden_size, input_size))
+		self.output_weights = random.normal(0, ANN.INIT_SIGMA, hidden_size)
+	
+	def get_weights(self):
+		return self.hidden_weights, self.output_weights
+	
+	def predict(self, input_vector):
+		
+		# Predicts the output for this input vector
+		# input_vector will be normalized
+		
+		input_vector = input_vector/linalg.norm(input_vector)
+		return ANN.ACTIVATION(dot(self.output_weights, ANN.VEC_ACTIVATION(dot(self.hidden_weights, input_vector))))
+	
+	@staticmethod
+	def frob_norm(a, b):
+		
+		# Calculates the total Frobenius norm of both matrices A and B
+		return sqrt(linalg.norm(a)**2 + linalg.norm(b)**2)
+	
+	def train(self, examples):
+		
+		#print("Training")
+		start = time()
+		
+		# examples is a list of (input, output)-tuples
+		# input will be normalized
+		# We stop when the weights have converged within some relative margin
+		
+		for example in examples:
+			example[0] = example[0]/linalg.norm(example[0])
+		
+		iteration = 0
+		while True:
+			
+			
+			# Store old weights to check for convergence later
+			prev_hidden_weights = deepcopy(self.hidden_weights)
+			prev_output_weights = deepcopy(self.output_weights)
+			
+			for k in range(len(examples)):
+				
+				input_vector, output = examples[k]
+				
+				# Calculate outputs
+				hidden_input = dot(self.hidden_weights, input_vector)
+				hidden_output = ANN.VEC_ACTIVATION(hidden_input)
+				final_input = dot(self.output_weights, hidden_output)
+				predicted_output = ANN.ACTIVATION(final_input)
+				
+				#print("Output:", output)
+				#print("Predicted output:", predicted_output)
+				
+				# Used in calculations
+				prediction_error = output - predicted_output
+				output_derivative = ANN.D_ACTIVATION(final_input)
+				
+				# Adjust output weights and calculate requested hidden change
+				requested_hidden_change = prediction_error*output_derivative*self.output_weights
+				self.output_weights = self.output_weights + ANN.STEP_SIZE*prediction_error*hidden_output
+				
+				#print("After adjusting output weights:", ANN.ACTIVATION(dot(self.output_weights, hidden_output)))
+				
+				# Backpropagate requested hidden change to adjust hidden weights
+				self.hidden_weights = self.hidden_weights + ANN.STEP_SIZE*outer(requested_hidden_change*(ANN.VEC_D_ACTIVATION(hidden_input)), input_vector)
+				
+				#print("After adjusting hidden weights:", ANN.ACTIVATION(dot(self.output_weights, ANN.VEC_ACTIVATION(dot(self.hidden_weights, input_vector)))))
+				
+				# Check stop criteria
+				iteration += 1
+				if iteration >= ANN.MAX_ITERATIONS:
+					break
+			
+			# Check stop criteria
+			if iteration >= ANN.MAX_ITERATIONS:
+				break
+			diff = ANN.frob_norm(self.hidden_weights - prev_hidden_weights, self.output_weights - prev_output_weights)
+			base = ANN.frob_norm(self.hidden_weights, self.output_weights)
+			#if base > 0 and diff/base < ANN.REL_STOP_MARGIN:
+			#	break
+		
+		print(time() - start)
+		print("Stopped training after %s iterations."%iteration)
+
+# TESTING
+
+def print_difference(ann1, ann2):
+	
+	# Prints the differences in weights in between two ANN's with identical topology
+	
+	hidden_weights1, output_weights1 = ann1.get_weights()
+	hidden_weights2, output_weights2 = ann2.get_weights()
+	hidden_diff = hidden_weights1 - hidden_weights2
+	output_diff = output_weights1 - output_weights2
+	
+	print(hidden_diff)
+	print(output_diff)
+	print("Frobenius norms:")
+	print("Hidden weights difference:", linalg.norm(hidden_diff))
+	print("Output weights difference:", linalg.norm(output_diff))
+	print("Both:", ANN.frob_norm(hidden_diff, output_diff))
+
+def RMSE(ann, examples):
+	
+	total = 0
+	for input_vector, output in examples:
+		total += (output - ann.predict(input_vector))**2
+	return sqrt(total/len(examples))
+
+def generate_examples(amount, input_size, evaluate):
+	# evaluate is a function mapping an input vector onto a numerical value
+	examples = []
+	inputs = random.normal(0, 100, (amount, input_size))
+	for i in range(amount):
+		input_vector = inputs[i]
+		examples.append([input_vector, evaluate(input_vector)])
+	return examples
+
+def test():
+	
+	# Test the ANN by having it model another ANN with identical topology but unknown weights
+	
+	input_size = 5
+	hidden_size = 3
+	real = ANN(input_size, hidden_size)
+	model = ANN(input_size, hidden_size)
+	
+	# Generate training data
+	training_data = generate_examples(10000, input_size, real.predict)
+	validation_data = generate_examples(10000, input_size, real.predict)
+	
+	# Print initial difference, train, then print new difference
+	print("Initial difference:")
+	print_difference(real, model)
+	print("Initial RMSE (on training data):", RMSE(model, training_data))
+	print("Initial RMSE (on validation data):", RMSE(model, validation_data))
+	model.train(training_data)
+	print("After training:")
+	print_difference(real, model)
+	print("After training RMSE (on training data):", RMSE(model, training_data))
+	print("After training RMSE (on validation data):", RMSE(model, validation_data))
+
+if __name__ == "__main__":
+	test()
+
+