66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
import matplotlib
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matplotlib.use('Agg')
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import datetime
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from keras.utils import plot_model
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import copy
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import numpy as np
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import decimal
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try:
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from src.PltData import PltData
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from src.Functions import Functions
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from src.NeuralNetwork import NeuralNetwork
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from src.FeatureReduction import FeatureReduction
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except ImportError:
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from PltData import PltData
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from NeuralNetwork import NeuralNetwork
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from FeatureReduction import FeatureReduction
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import Functions
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def getRangeAroundNumber(myNumber, rangeWidth):
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'''
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Gibt einen Zahlen Bereich rund um myNumber aus
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:param myNumber:
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:return:
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'''
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try:
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myNumber = myNumber.real
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except TypeError:
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myNumber = myNumber
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d = decimal.Decimal(myNumber.real)
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ex = 3
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print(myNumber*(10**ex))
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start = myNumber*(10**ex)-rangeWidth
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stop = myNumber * (10**ex) + rangeWidth
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data = np.arange(start, stop, 1)
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data = data / (10**ex)
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return data
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def evalSomething(numberOfNeurons, activationFunctions, featureReduction,
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numberLoops, loss):
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nn = NeuralNetwork(numberOfNeurons, activationFunctions, featureReduction,
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numberLoops, loss, printVectors=False)
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nn.addLayers()
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nn.loadModel()
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weights = nn.model.get_weights()
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data = np.array([nn.featureReductionFunction.calc(weights, nn.numberOfNeurons[0])])
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fp = data[0][0]
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#data = getRangeAroundNumber(fp, 30)
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data = np.arange(-10000, 10000, 1)
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start = min(data)
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stop = max(data)
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step = abs((start-stop)/len(data))
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text = nn.getDescription() +"\nFixpunkt: "+ str(fp)
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nn.evaluate(data, str(start)+"_"+str(stop)+"_"+str(step), text= text + "\nStart: " +str(start) +"\nStop "+ str(stop) + "\nStep "+ str(step))
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v = np.array([1,2,3,24])
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for i in v:
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evalSomething(numberOfNeurons=[1, i, 1], activationFunctions=["sigmoid", "linear"], featureReduction='rfft',
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numberLoops=100000, loss='mean_squared_error')
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i-=1
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