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Improvements in FCM_FTS

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This commit is contained in:
Petrônio Cândido 2020-01-29 16:39:23 -03:00
parent dd5ac3de20
commit 5804f3d5f2
5 changed files with 63 additions and 18 deletions
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18
pyFTS/fcm/GA.py
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@ -35,7 +35,10 @@ def genotype():
"""
num_concepts = parameters['num_concepts']
order = parameters['order']
ind = dict(weights=[np.random.normal(0,1.,(num_concepts,num_concepts)) for k in range(order)])
ind = dict(
weights=[np.random.normal(0, 1., (num_concepts,num_concepts)) for k in range(order)],
bias=[np.random.normal(0, 1., num_concepts) for k in range(order)]
)
return ind
@ -77,6 +80,7 @@ def phenotype(individual, train):
model = fts.FCM_FTS(partitioner=partitioner, order=order)
model.fcm.weights = individual['weights']
model.fcm.bias = individual['bias']
return model
@ -165,6 +169,15 @@ def crossover(parents):
descendent['weights'][k] = np.array(new_weight).reshape(weights1.shape)
new_bias = []
bias1 = parents[0]['bias'][k]
bias2 = parents[1]['bias'][k]
for row, a in enumerate(weights1):
new_bias.append(.7 * bias1[row] + .3 * bias2[row])
descendent['bias'][k] = np.array(new_bias).reshape(bias1.shape)
return descendent
@ -194,6 +207,9 @@ def mutation(individual, pmut):
individual['weights'][k][row, col] += np.random.normal(0, .5, 1)
individual['weights'][k][row, col] = np.clip(individual['weights'][k][row, col], -1, 1)
individual['bias'][k][row] += np.random.normal(0, .5, 1)
return individual

41
pyFTS/fcm/GD.py
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@ -6,31 +6,46 @@ def GD(data, model, **kwargs):
momentum = kwargs.get('momentum', None)
iterations = kwargs.get('iterations', 1)
num_concepts = model.partitioner.partitions
weights = [np.random.normal(0,.01,(num_concepts,num_concepts)) for k in range(model.order)]
last_gradient = [None for k in range(model.order) ]
weights = [np.random.normal(0,.1,(num_concepts,num_concepts)) for k in range(model.order)]
bias = [np.random.normal(0,.1,num_concepts) for k in range(model.order)]
last_gradientW = [None for k in range(model.order) ]
last_gradientB = [None for k in range(model.order)]
for it in np.arange(iterations):
for i in np.arange(model.order, len(data)):
#i = np.random.randint(model.order, len(data)-model.order)
sample = data[i-model.order : i]
target = data[i]
target = model.partitioner.fuzzyfy(data[i], mode='vector')
#target = data[i]
model.fcm.weights = weights
model.fcm.bias = bias
inputs = model.partitioner.fuzzyfy(sample, mode='vector')
activations = model.fcm.activate(inputs)
#activations = model.fcm.activate(inputs)
activations = [model.fcm.activation_function(inputs[k]) for k in np.arange(model.order)]
forecast = model.predict(sample)[0]
error = target - forecast #)**2
if str(error) == 'nan' or error == np.nan or error == np.Inf:
print('error')
print(error)
error = target - model.partitioner.fuzzyfy(forecast, mode='vector') #)**2
#error = target - forecast
#if str(error) == 'nan' or error == np.nan or error == np.Inf:
#print(error)
print(np.dot(error,error))
for k in np.arange(model.order):
deriv = error * model.fcm.activation_function(activations[k], deriv=True)
#deriv = error * activations[k]
if momentum is not None:
if last_gradient[k] is None:
last_gradient[k] = deriv*inputs[k]
if last_gradientW[k] is None:
last_gradientW[k] = deriv * inputs[k]
last_gradientB[k] = deriv
tmp_gradw = (momentum * last_gradientW[k]) + alpha*deriv*inputs[k]
weights[k] -= tmp_gradw
last_gradientW[k] = tmp_gradw
tmp_gradB = (momentum * last_gradientB[k]) + alpha * deriv
bias[k] -= tmp_gradB
last_gradientB[k] = tmp_gradB
tmp_grad = (momentum * last_gradient[k]) + alpha*deriv*inputs[k]
weights[k] -= tmp_grad
last_gradient[k] = tmp_grad
else:
weights[k] -= alpha*deriv*inputs[k]
bias[k] -= alpha*deriv
return weights

3
pyFTS/fcm/common.py
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@ -8,11 +8,12 @@ class FuzzyCognitiveMap(object):
self.order = kwargs.get('order',1)
self.concepts = kwargs.get('partitioner',None)
self.weights = []
self.bias = []
self.activation_function = kwargs.get('activation_function', Activations.sigmoid)
def activate(self, concepts):
dot_products = np.zeros(len(self.concepts))
for k in np.arange(0, self.order):
dot_products += np.dot(np.array(concepts[k]).T, self.weights[k])
dot_products += np.dot(np.array(concepts[k]).T, self.weights[k]) + self.bias[k]
return self.activation_function( dot_products )

1
pyFTS/fcm/fts.py
View File

@ -18,6 +18,7 @@ class FCM_FTS(hofts.HighOrderFTS):
GA.parameters['partitioner'] = self.partitioner
ret = GA.execute(data, **kwargs)
self.fcm.weights = ret['weights']
self.fcm.bias = ret['bias']
elif method == 'GD':
self.fcm.weights = GD.GD(data, self, **kwargs)

18
pyFTS/tests/fcm_fts.py
View File

@ -8,7 +8,7 @@ import pandas as pd
from pyFTS.fcm import fts as fcm_fts
from pyFTS.partitioners import Grid
from pyFTS.common import Util
from pyFTS.common import Util, Membership
df = pd.read_csv('https://query.data.world/s/56i2vkijbvxhtv5gagn7ggk3zw3ksi', sep=';')
@ -18,12 +18,24 @@ data = df['glo_avg'].values[:]
train = data[:7000]
test = data[7000:7500]
fs = Grid.GridPartitioner(data=train, npart=5)
fs = Grid.GridPartitioner(data=train, npart=5, func=Membership.trimf)
model = fcm_fts.FCM_FTS(partitioner=fs, order=2, activation_function = Activations.relu)
model.fit(train, method='GD', alpha=0.5, momentum=None, iteractions=1 )
model.fit(train, method='GD', alpha=0.02, momentum=0.8, iteractions=3 )
'''
model.fit(train, method='GA', ngen=15, #number of generations
mgen=7, # stop after mgen generations without improvement
npop=15, # number of individuals on population
pcruz=.5, # crossover percentual of population
pmut=.3, # mutation percentual of population
window_size = 7000,
train_rate = .8,
increment_rate =.2,
experiments=1
)
'''
Util.persist_obj(model, 'fcm_fts10c')
'''