Gradient descent training method for FCM_FTS
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@ -3,9 +3,9 @@ import numpy as np
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def step(x, deriv=False):
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if deriv:
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1 * (x == 0)
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return np.abs(1 * (x == 0))
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else:
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return 1 * (x > 0)
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return np.abs(1 * (x > 0))
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def sigmoid(x, deriv=False):
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@ -26,14 +26,14 @@ def softmax(x, deriv=False):
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def tanh(x, deriv=False):
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if deriv:
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pass
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return 1 - np.tanh(x)**2
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else:
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return np.tanh(x)
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def relu(x, deriv=False):
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if deriv:
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return 1. * (x > 0)
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return np.abs(1. * (x > 0))
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else:
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return x * (x > 0)
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return np.abs(x * (x > 0))
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@ -1,30 +1,36 @@
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import numpy as np
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def GD(data, model, alpha, momentum=0.5):
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def GD(data, model, **kwargs):
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alpha = kwargs.get('alpha', 0.1)
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momentum = kwargs.get('momentum', None)
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iterations = kwargs.get('iterations', 1)
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num_concepts = model.partitioner.partitions
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weights=[np.random.normal(0,.01,(num_concepts,num_concepts)) for k in range(model.order)]
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weights = [np.random.normal(0,.01,(num_concepts,num_concepts)) for k in range(model.order)]
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last_gradient = [None for k in range(model.order) ]
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for i in np.arange(model.order, len(data)):
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sample = data[i-model.order : i]
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target = data[i]
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model.fcm.weights = weights
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inputs = model.partitioner.fuzzyfy(sample, mode='vector')
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activations = [model.fcm.activation_function(inputs[k]) for k in np.arange(model.order)]
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forecast = model.predict(sample)[0]
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error = target - forecast #)**2
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if error == np.nan:
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pass
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print(error)
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for k in np.arange(model.order):
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deriv = error * model.fcm.activation_function(activations[k], deriv=True)
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if momentum is not None:
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if last_gradient[k] is None:
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last_gradient[k] = deriv*inputs[k]
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for it in np.arange(iterations):
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for i in np.arange(model.order, len(data)):
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#i = np.random.randint(model.order, len(data)-model.order)
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sample = data[i-model.order : i]
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target = data[i]
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model.fcm.weights = weights
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inputs = model.partitioner.fuzzyfy(sample, mode='vector')
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activations = model.fcm.activate(inputs)
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forecast = model.predict(sample)[0]
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error = target - forecast #)**2
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if str(error) == 'nan' or error == np.nan or error == np.Inf:
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print('error')
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print(error)
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for k in np.arange(model.order):
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deriv = error * model.fcm.activation_function(activations[k], deriv=True)
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if momentum is not None:
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if last_gradient[k] is None:
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last_gradient[k] = deriv*inputs[k]
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tmp_grad = (momentum * last_gradient[k]) + alpha*deriv*inputs[k]
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weights[k] -= tmp_grad
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else:
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weights[k] -= alpha*deriv*inputs[k]
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tmp_grad = (momentum * last_gradient[k]) + alpha*deriv*inputs[k]
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weights[k] -= tmp_grad
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last_gradient[k] = tmp_grad
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else:
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weights[k] -= alpha*deriv*inputs[k]
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return weights
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@ -11,14 +11,15 @@ class FCM_FTS(hofts.HighOrderFTS):
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self.fcm = common.FuzzyCognitiveMap(**kwargs)
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def train(self, data, **kwargs):
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'''
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GA.parameters['num_concepts'] = self.partitioner.partitions
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GA.parameters['order'] = self.order
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GA.parameters['partitioner'] = self.partitioner
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ret = GA.execute(data, **kwargs)
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self.fcm.weights = ret['weights']
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'''
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self.fcm.weights = GD.GD(data, self, alpha=0.01)
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method = kwargs.get('method','GA')
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if method == 'GA':
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GA.parameters['num_concepts'] = self.partitioner.partitions
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GA.parameters['order'] = self.order
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GA.parameters['partitioner'] = self.partitioner
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ret = GA.execute(data, **kwargs)
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self.fcm.weights = ret['weights']
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elif method == 'GD':
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self.fcm.weights = GD.GD(data, self, **kwargs)
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def forecast(self, ndata, **kwargs):
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ret = []
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@ -33,7 +34,10 @@ class FCM_FTS(hofts.HighOrderFTS):
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activation = self.fcm.activate(fuzzyfied)
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final = np.dot(midpoints, activation)/np.sum(activation)
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final = np.dot(midpoints, activation)/np.nanmax([1, np.sum(activation)])
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if str(final) == 'nan' or final == np.nan or final == np.Inf:
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print('error')
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ret.append(final)
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@ -18,27 +18,32 @@ data = df['glo_avg'].values[:]
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train = data[:7000]
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test = data[7000:7500]
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fs = Grid.GridPartitioner(data=train, npart=7)
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fs = Grid.GridPartitioner(data=train, npart=5)
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model = fcm_fts.FCM_FTS(partitioner=fs, order=2, activation_function = Activations.relu)
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model.fit(train,
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ngen=30, #number of generations
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mgen=7, # stop after mgen generations without improvement
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npop=10, # number of individuals on population
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pcruz=.5, # crossover percentual of population
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pmut=.3, # mutation percentual of population
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window_size = 7000,
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train_rate = .8,
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increment_rate =.2,
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experiments=1
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)
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model.fit(train, method='GD', alpha=0.02, momentum=0.8, iteractions=3 )
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Util.persist_obj(model, 'fcm_fts10c')
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'''
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model = Util.load_obj('fcm_fts05c')
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'''
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#forecasts = model.predict(test)
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#print(model)
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fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[15,5])
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ax.plot(test,label='Original')
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forecasts = model.predict(test)
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print(model)
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for w in np.arange(model.order):
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forecasts.insert(0,None)
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ax.plot(forecasts, label=model.shortname)
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plt.show()
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print("")
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