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5.4 Unsupervised Network
5.4 Unsupervised Network
5.4.1 Illustrative Example
5.4.1 Illustrative Example
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session=StartExternalSession["Python"]
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ExternalSessionObject
Test
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5+6
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11
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x={{0.1961`,0.9806`},{-0.1961`,0.9806`},{0.9806`,0.1961`},{0.9806`,-0.1961`},{-0.5812`,-0.8137`},{-0.8137`,-0.5812`}};
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p1=ListPlot[x,PlotStyle{PointSize[0.025],RGBColor[1,0,0]},FrameTrue,AxesNone]
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<<NeuralNets`
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unsup=InitializeUnsupervisedNet[x,3];
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{unsup,fitrecord}=UnsupervisedNetFit[x,unsup,50,ReportFrequency5];
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UnsupervisedNetDistance[unsup,x]
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0.185534
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NetPlot[fitrecord,x]
Out[]=
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unsup[[1]]
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{{0.980144,0.0140458},{-0.703087,-0.69169},{-0.00995935,0.980387}}
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import numpy as np
from neupy import algorithms, environment
environment.reproducible()
from neupy import algorithms, environment
environment.reproducible()
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input_data = np.array([ [0.1961, 0.9806], [-0.1961, 0.9806], [0.9806, 0.1961], [0.9806, -0.1961], [-0.5812, -0.8137], [-0.8137, -0.5812], ])input_data
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NumericArray
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Normal[%]
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{{0.1961,0.9806},{-0.1961,0.9806},{0.9806,0.1961},{0.9806,-0.1961},{-0.5812,-0.8137},{-0.8137,-0.5812}}
Loading the network algorithm and parametrize the function,
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sofmnet = algorithms.SOFM( n_inputs=2, n_outputs=3, step=0.5, show_epoch=100, shuffle_data=True, verbose=True, learning_radius=0, features_grid=(3, 1), )
Main information
[ALGORITHM] SOFM
[OPTION] distance = ['euclid', 'neg_euclid_distance']
[OPTION] features_grid = [3, 1]
[OPTION] grid_type = ['rectangle', 'find_neighbours_on_rect_grid', 'find_step_scaler_on_rect_grid']
[OPTION] learning_radius = 0
[OPTION] n_inputs = 2
[OPTION] n_outputs = 3
[OPTION] reduce_radius_after = 100
[OPTION] reduce_std_after = 100
[OPTION] reduce_step_after = 100
[OPTION] show_epoch = 100
[OPTION] shuffle_data = True
[OPTION] signals = None
[OPTION] std = 1
[OPTION] step = 0.5
[OPTION] verbose = True
[OPTION] weight = Normal(mean=0, std=0.01)
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sofmnet.train(input_data,epochs=100)
#1 : [0 ns] train: 0.502381
#100 : [0 ns] train: 0.117317
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sofmnet.weight[0:2, :]
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NumericArray
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Normal[%]
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list1
5.4.2 Iris Data Set
5.4.2 Iris Data Set
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import numpy as np
from sklearn import datasets
from neupy import algorithms, environment
environment.reproducible()
from sklearn import datasets
from neupy import algorithms, environment
environment.reproducible()
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iris = datasets.load_iris()
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X=iris.data[:,[2,3]]
y=iris.target
y=iris.target
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np.savetxt('E:\\dataX.txt',X,fmt='%.2e')
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y
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sofmnet.train(X,epochs=1500)
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sofmnet.weight[0:2, :]
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list1
5.4.3 Voronoi Mesh
5.4.3 Voronoi Mesh
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import numpy as np
from sklearn import datasets
from neupy import algorithms, environment
environment.reproducible()
from sklearn import datasets
from neupy import algorithms, environment
environment.reproducible()
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from numpy import array, matrix
from scipy.io import mmread, mmwrite
from numpy import array, matrix
from scipy.io import mmread, mmwrite
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caki=mmread('cuki.mtx')
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sofmnet.train(caki,epochs=50)
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list1
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sofmnet.weight[0:2, :]
5.4.4 Robust Regression
5.4.4 Robust Regression
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caki=mmread('cuki.mtx')
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sofmnet.train(caki,epochs=1000)
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sofmnet.weight[0:2, :]
5.4.5 Kohonen Map
5.4.5 Kohonen Map
5.4.6 Fitting Sphere to Point Cloud Data
5.4.6 Fitting Sphere to Point Cloud Data