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Timur Gilmullin
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examples updated
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‎README.md‎

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<a name="Chapter_2_2_3"></a>***Usage examples***
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Start learning with user's ethalon data file and neuronet options Config=(3,[3,2],2), 10 epochs, 0.1 learning rate and 0.05 momentum, epsilon is 0.01 and stop learning if errors less than 5%, update information in log every 5 epochs:
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Start learning with user's ethalon data file and neuronet options Config=(3,[3,2],2), 10 epochs, 0.05 learning rate and 0.05 momentum, epsilon is 0.1 and stop learning if errors less than 10%, update information in log every epochs and reload previous network for re-train:
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python FuzzyClassificator.py --ethalons ethalons.dat --learn config=3,3,2,2 epochs=10 rate=0.1 momentum=0.05 epsilon=0.01 stop=5 --separator=TAB --debug-level=DEBUG --update 5
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python FuzzyClassificator.py --ethalons ethalons.dat --separator=TAB --debug-level=DEBUG --update 1 --reload --learn config=3,3,2,2 epochs=10 rate=0.05 momentum=0.05 epsilon=0.1 stop=10
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Classify all candidates from file candidates.dat and show result in report.txt:
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‎network.xml‎

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<name val="'FeedForwardNetwork-11'"/>
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<Modules>
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<LinearLayer class="pybrain.structure.modules.linearlayer.LinearLayer" inmodule="True" name="in">
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<dim val="3"/>
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<name val="'in'"/>
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<dim val="3"/>
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</LinearLayer>
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<LinearLayer class="pybrain.structure.modules.linearlayer.LinearLayer" name="out" outmodule="True">
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<dim val="2"/>
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<name val="'out'"/>
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<dim val="2"/>
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</LinearLayer>
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<BiasUnit class="pybrain.structure.modules.biasunit.BiasUnit" name="bias">
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<name val="'bias'"/>
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</BiasUnit>
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<SigmoidLayer class="pybrain.structure.modules.sigmoidlayer.SigmoidLayer" name="hidden0">
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<dim val="3"/>
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<name val="'hidden0'"/>
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<dim val="3"/>
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</SigmoidLayer>
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<SigmoidLayer class="pybrain.structure.modules.sigmoidlayer.SigmoidLayer" name="hidden1">
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<dim val="2"/>
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<name val="'hidden1'"/>
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<dim val="2"/>
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</SigmoidLayer>
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</Modules>
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<Connections>
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<FullConnection class="pybrain.structure.connections.full.FullConnection" name="FullConnection-10">
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<outmod val="hidden1"/>
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<FullConnection class="pybrain.structure.connections.full.FullConnection" name="FullConnection-5">
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<inmod val="bias"/>
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<Parameters>[-0.86787217651794413, -0.54301359163853313]</Parameters>
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</FullConnection>
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<FullConnection class="pybrain.structure.connections.full.FullConnection" name="FullConnection-8">
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<outmod val="out"/>
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<inmod val="bias"/>
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<Parameters>[1.7861249723535833, -0.82229846024474862]</Parameters>
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</FullConnection>
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<FullConnection class="pybrain.structure.connections.full.FullConnection" name="FullConnection-9">
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<outmod val="hidden0"/>
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<inmod val="bias"/>
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<Parameters>[1.7778506370286433, 0.40177446486743496, 1.9104742366245315]</Parameters>
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<Parameters>[0.9702490763962287, -1.3751288551583383]</Parameters>
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</FullConnection>
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<FullConnection class="pybrain.structure.connections.full.FullConnection" name="FullConnection-6">
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<inmod val="bias"/>
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<outmod val="hidden0"/>
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<inmod val="in"/>
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<Parameters>[-4.0528905402316608, -1.4943316175965866, 0.37357367028747152, -0.46306752995546463, 0.085328742514023836, -2.0584035160368543, 0.74178373372621209, 0.63338843442727699, 0.47873754476787067]</Parameters>
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<Parameters>[1.5713987471074589, 0.26744082414749287, -0.15935527654412501]</Parameters>
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</FullConnection>
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<FullConnection class="pybrain.structure.connections.full.FullConnection" name="FullConnection-7">
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<inmod val="bias"/>
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<outmod val="hidden1"/>
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<Parameters>[-0.32792431250557758, -0.56992225619849479]</Parameters>
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</FullConnection>
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<FullConnection class="pybrain.structure.connections.full.FullConnection" name="FullConnection-10">
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<inmod val="in"/>
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<outmod val="hidden0"/>
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<Parameters>[-3.6512689789180146, -4.6717535538501851, 3.5808154220330946, 0.30840465928158733, 2.4858151275380966, 1.1575143203684097, 0.16753053997254838, 0.97217472370952629, -1.4628411022414951]</Parameters>
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</FullConnection>
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<FullConnection class="pybrain.structure.connections.full.FullConnection" name="FullConnection-9">
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<inmod val="hidden0"/>
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<Parameters>[3.3310720418774316, -0.2640761170495155, -0.53677142682084711, 2.6955276062223374, 0.84021477903692032, -0.82859356911070714]</Parameters>
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<outmod val="hidden1"/>
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<Parameters>[1.5494153022367876, 1.4595006737618332, 0.39391805063122148, 6.0762498128048703, -1.8153434334374616, -1.781675177028873]</Parameters>
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</FullConnection>
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<FullConnection class="pybrain.structure.connections.full.FullConnection" name="FullConnection-5">
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<outmod val="out"/>
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<FullConnection class="pybrain.structure.connections.full.FullConnection" name="FullConnection-8">
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<inmod val="hidden1"/>
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<Parameters>[-2.3144653206863857, -0.26278946684203763, 0.54626283148298993, 1.9816772633824209]</Parameters>
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<outmod val="out"/>
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<Parameters>[0.83933184237219483, -2.2795477326402094, 0.83002378236688179, 2.1853926307954001]</Parameters>
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</FullConnection>
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</Connections>
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</Network>

‎report.txt‎

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Header: [input1 input2 input3] [1st_class_output 2nd_class_output]
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-----------------------------------------------------------------------
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Input: ['0.12', '0.32', 'Min'] Output: ['Low', 'Med']
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Input: ['0.32', '0.35', 'Low'] Output: ['Med', 'Med']
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Input: ['0.54', '0.57', 'Med'] Output: ['High', 'Low']
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Input: ['0.12', '0.32', 'Min'] Output: ['Med', 'Med']
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Input: ['0.32', '0.35', 'Low'] Output: ['High', 'Low']
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Input: ['0.54', '0.57', 'Med'] Output: ['Max', 'Min']
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Input: ['0.65', '0.68', 'High'] Output: ['Max', 'Min']
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Input: ['0.76', '0.79', 'Max'] Output: ['Max', 'Min']

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