Merge branch 'master' into release1.7
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commit
06d1bf6e6a
@ -1,4 +1,5 @@
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from pyFTS.common.transformations.transformation import Transformation
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import numpy as np
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class ROI(Transformation):
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@ -20,4 +21,4 @@ class ROI(Transformation):
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def inverse(self, data, param=None, **kwargs):
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modified = [(param[i - 1] * data[i]) + param[i - 1] for i in np.arange(1, len(data))]
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return modified
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return modified
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@ -6,14 +6,14 @@ import pandas as pd
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from typing import Tuple
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from typing import List
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from pyFTS.common.transformations.transformation import Transformation
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import SimpSOM as sps
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class SOMTransformation(Transformation):
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def __init__(self,
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grid_dimension: Tuple,
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**kwargs):
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import SimpSOM as sps
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# SOM attributes
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self.load_file = kwargs.get('loadFile')
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@ -1,4 +1,8 @@
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from pyFTS.common.transformations.transformation import Transformation
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from pandas import datetime
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from sklearn.linear_model import LinearRegression
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import numpy as np
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import pandas as pd
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class LinearTrend(Transformation):
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@ -24,8 +28,6 @@ class LinearTrend(Transformation):
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'''Regression model'''
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def train(self, data, **kwargs):
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from pandas import datetime
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from sklearn.linear_model import LinearRegression
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x = data[self.index_field].values
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@ -79,4 +81,4 @@ class LinearTrend(Transformation):
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ret = pd.Series(ret)
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ret = pd.to_numeric(ret, downcast='integer')
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return np.array(ret)
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return np.array(ret)
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@ -47,11 +47,11 @@ class Variable:
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:param kwargs:
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:return:
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"""
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fs = kwargs.get('partitioner', Grid.GridPartitioner)
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mf = kwargs.get('func', Membership.trimf)
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np = kwargs.get('npart', 10)
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fs = kwargs.pop('partitioner', Grid.GridPartitioner)
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mf = kwargs.pop('func', Membership.trimf)
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np = kwargs.pop('npart', 10)
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data = kwargs.get('data', None)
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kw = kwargs.get('partitioner_specific', {})
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kw = kwargs.pop('partitioner_specific', {})
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self.partitioner = fs(data=data[self.data_label].values, npart=np, func=mf,
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transformation=self.transformation, prefix=self.alias,
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variable=self.name, **kw)
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@ -661,22 +661,29 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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return tmp
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def visualize_distributions(model, **kwargs):
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import matplotlib.pyplot as plt
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from matplotlib import gridspec
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import seaborn as sns
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def highorder_fuzzy_markov_chain(model):
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ordered_sets = model.partitioner.ordered_sets
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ftpg_keys = sorted(model.flrgs.keys(), key=lambda x: model.flrgs[x].get_midpoint(model.sets))
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lhs_probs = [model.flrg_lhs_unconditional_probability(model.flrgs[k])
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for k in ftpg_keys]
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lhs_probs = np.array([model.flrg_lhs_unconditional_probability(model.flrgs[k])
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for k in ftpg_keys])
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mat = np.zeros((len(ftpg_keys), len(ordered_sets)))
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for row, w in enumerate(ftpg_keys):
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for col, k in enumerate(ordered_sets):
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if k in model.flrgs[w].RHS:
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mat[row, col] = model.flrgs[w].rhs_unconditional_probability(k)
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return ftpg_keys, ordered_sets, lhs_probs, mat
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def visualize_distributions(model, **kwargs):
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import matplotlib.pyplot as plt
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from matplotlib import gridspec
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import seaborn as sns
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ftpg_keys, ordered_sets, lhs_probs, mat = highorder_fuzzy_markov_chain(model)
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size = kwargs.get('size', (5,10))
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@ -695,3 +702,5 @@ def visualize_distributions(model, **kwargs):
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ax.set_xticklabels(ordered_sets)
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ax.grid(True)
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ax.xaxis.set_tick_params(rotation=90)
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