SpammerHellDontDelete at 03:47, 2 March 2010

2010-03-02T03:47:56Z

← Older revision		Revision as of 20:47, 1 March 2010
Line 3:		Line 3:
	#same-lengthed arrays		#same-lengthed arrays
	def dot_product(a, b):		def dot_product(a, b):
	sum ~~= 0~~		return sum(x*y for x,y in zip(a,b))
	~~i = 0~~
	~~while i < len~~(a):
	~~sum += a[i] * b[i]~~
	~~i += 1~~
	~~return sum~~

	class Perceptron:		class Perceptron:

SpammerHellDontDelete at 03:39, 7 May 2009

2009-05-07T03:39:33Z

← Older revision		Revision as of 20:39, 6 May 2009
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			<pre>
	#a dot product is the sum of the products of aligned-elements from two		#a dot product is the sum of the products of aligned-elements from two
	#same-lengthed arrays		#same-lengthed arrays
Line 63:		Line 64:
	train(data, subject)		train(data, subject)
	print "final bias weight: %s, final input weights: %s" % (subject.bias_weight, subject.weights)		print "final bias weight: %s, final input weights: %s" % (subject.bias_weight, subject.weights)
			</pre>

SpammerHellDontDelete: New page: #a dot product is the sum of the products of aligned-elements from two #same-lengthed arrays def dot_product(a, b): sum = 0 i = 0 while i < len(a): sum += a[i] * b[i] ...

2009-05-07T03:38:55Z

New page: #a dot product is the sum of the products of aligned-elements from two #same-lengthed arrays def dot_product(a, b): sum = 0 i = 0 while i < len(a): sum += a[i] * b[i] ...

New page

#a dot product is the sum of the products of aligned-elements from two
#same-lengthed arrays
def dot_product(a, b):
sum = 0
i = 0
while i < len(a):
sum += a[i] * b[i]
i += 1
return sum

class Perceptron:

#percieve based on a list of inputs
def percieve(self, inputs):
#if there is not a list of weights, make one of = length to list of inputs
if len(self.weights) < len(inputs):
for ip in inputs:
self.weights.append(self.defualt_weight)
#get the dot product of those inputs with the bias and the input's weights with the bias's weight
sum = dot_product(inputs + [self.bias], self.weights + [self.bias_weight])
return sum

#learn method compares output of percieve() to the expected output and adjusts weights by plus or minus
#the learn rate which is incrementally decreased by a factor of 0.99
def learn(self, inputs, expected):
if self.learn_rate < 0.001:
return
self.learn_rate = self.learn_rate * 0.999
print "learn rate is %s" % (self.learn_rate)
train_step = (expected - self.percieve(inputs)) * self.learn_rate
self.bias_weight += self.bias_weight * train_step
print "bias weight is: %s" % (self.bias_weight)
for i in xrange(len(inputs)):
self.weights[i] += inputs[i] * train_step
print "input %s weight is: %s" % (i, self.weights[i])

#this defines how a Perceptron object represents itself in the interpreter
def __str__(self):
return "Weights: %s. Threshhold: %s. Learn rate: %s." % (self.weights, self.threshhold, self.learn_rate)
#same as above
__repr__ = __str__

#defines initial values for a new Perceptron object
def __init__(self):
self.bias = 1.0
self.bias_weight = 0.0
self.defualt_weight = 0.0
self.weights = []
self.learn_rate = 1

#trains a perceptron according to data
def train(data, perceptron):
for case in data:
perceptron.learn(case[0],case[1])

#define a dataset to try and train a perceptron
data = [[[1.0],2.0],[[2.0],4.0],[[3.0],6.0]]

#do the damned thing
if __name__ == '__main__':
subject = Perceptron()
for i in range(100000):
train(data, subject)
print "final bias weight: %s, final input weights: %s" % (subject.bias_weight, subject.weights)

User:Elgreengeeto/Python Linear Perceptron - Revision history

SpammerHellDontDelete at 03:47, 2 March 2010

SpammerHellDontDelete at 03:39, 7 May 2009

SpammerHellDontDelete: New page: #a dot product is the sum of the products of aligned-elements from two #same-lengthed arrays def dot_product(a, b): sum = 0 i = 0 while i < len(a): sum += a[i] * b[i] ...