Diagnosa alergi
137
Data = Columns 1 through 15 1.0000 1.0000 1.0000 0 1.0000 1.0000 1.0000 1.0000 1.0000 0 0 0 0 0 1.0000 1.0000 1.0000 0 1.0000 0 0 0 0 0 0 0 0 0 1.0000 0 1.0000 0 0 1.0000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.0000 1.0000 0 0 0 0 1.0000 0 0 0 0 0 0 0 0 0 0 0 0 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0 0 1.0000 1.0000 0 0 0 0 0 1.0000 0 0 1.0000 1.0000 0 1.0000 0 0 0 0 0 0 0 0 1.0000 0 0 1.0000 1.0000 0 0 0 0 0 0 0 0 0
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Transcript of Diagnosa alergi
Data =
Columns 1 through 15
1.0000 1.0000 1.0000 0 1.0000 1.0000 1.0000 1.0000 1.0000 0 0 0 0 0 1.0000
1.0000 1.0000 0 1.0000 0 0 0 0 0 0 0 0 0 1.0000 0
1.0000 0 0 1.0000 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 1.0000 1.0000 0 0
0 0 1.0000 0 0 0 0 0 0 0 0 0 0 0 0
1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0 0 1.0000 1.0000 0 0 0 0
0 1.0000 0 0 1.0000 1.0000 0 1.0000 0 0 0 0 0 0 0
0 1.0000 0 0 1.0000 1.0000 0 0 0 0 0 0 0 0 0
Columns 16 through 30
1.0000 1.0000 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1.0000 0 0 0 0 0 1.0000 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1.0000 1.0000 1.0000 0 0 0
0 0 0 1.0000 1.0000 1.0000 1.0000 1.0000 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1.0000 1.0000 1.0000
0 1.0000 0 0 0 0 0 0 0 0 0 0 0 0 0
Columns 31 through 33
0 0 0
0 0 0.2000
0 0 0.4000
0 0 0.6000
0 0 0.7000
0 0 0.8000
1.0000 0 0.9000
1.0000 1.0000 1.0000
P =
1 1 1 0 0 1 0 0
1 1 0 0 0 1 1 1
1 0 0 0 1 1 0 0
0 1 1 0 0 1 0 0
1 0 0 0 0 1 1 1
1 0 0 0 0 1 1 1
1 0 0 0 0 1 0 0
1 0 0 0 0 0 1 0
1 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0
0 0 0 0 0 1 0 0
0 0 0 1 0 0 0 0
0 0 0 1 0 0 0 0
0 1 0 0 0 0 0 0
1 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0
1 0 0 0 0 0 0 1
0 0 1 0 0 0 0 0
0 0 0 0 1 0 0 0
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0 0 0 0 1 0 0 0
0 0 0 0 1 0 0 0
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0 0 1 0 0 0 0 0
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0 0 0 1 0 0 0 0
0 0 0 0 0 0 1 0
0 0 0 0 0 0 1 0
0 0 0 0 0 0 1 0
0 0 0 0 0 0 1 1
0 0 0 0 0 0 0 1
T =
0 0.2000 0.4000 0.6000 0.7000 0.8000 0.9000 1.0000
pn =
0.9354 0.9354 0.9354 -0.9354 -0.9354 0.9354 -0.9354 -0.9354
0.7246 0.7246 -1.2076 -1.2076 -1.2076 0.7246 0.7246 0.7246
1.2076 -0.7246 -0.7246 -0.7246 1.2076 1.2076 -0.7246 -0.7246
-0.7246 1.2076 1.2076 -0.7246 -0.7246 1.2076 -0.7246 -0.7246
0.9354 -0.9354 -0.9354 -0.9354 -0.9354 0.9354 0.9354 0.9354
0.9354 -0.9354 -0.9354 -0.9354 -0.9354 0.9354 0.9354 0.9354
1.6202 -0.5401 -0.5401 -0.5401 -0.5401 1.6202 -0.5401 -0.5401
1.6202 -0.5401 -0.5401 -0.5401 -0.5401 -0.5401 1.6202 -0.5401
2.4749 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536
-0.3536 -0.3536 -0.3536 -0.3536 -0.3536 2.4749 -0.3536 -0.3536
-0.3536 -0.3536 -0.3536 -0.3536 -0.3536 2.4749 -0.3536 -0.3536
-0.3536 -0.3536 -0.3536 2.4749 -0.3536 -0.3536 -0.3536 -0.3536
-0.3536 -0.3536 -0.3536 2.4749 -0.3536 -0.3536 -0.3536 -0.3536
-0.3536 2.4749 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536
2.4749 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536
2.4749 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536
1.6202 -0.5401 -0.5401 -0.5401 -0.5401 -0.5401 -0.5401 1.6202
-0.3536 -0.3536 2.4749 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536
-0.3536 -0.3536 -0.3536 -0.3536 2.4749 -0.3536 -0.3536 -0.3536
-0.3536 -0.3536 -0.3536 -0.3536 2.4749 -0.3536 -0.3536 -0.3536
-0.3536 -0.3536 -0.3536 -0.3536 2.4749 -0.3536 -0.3536 -0.3536
-0.3536 -0.3536 -0.3536 -0.3536 2.4749 -0.3536 -0.3536 -0.3536
-0.3536 -0.3536 -0.3536 -0.3536 2.4749 -0.3536 -0.3536 -0.3536
-0.3536 -0.3536 2.4749 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536
-0.3536 -0.3536 -0.3536 2.4749 -0.3536 -0.3536 -0.3536 -0.3536
-0.3536 -0.3536 -0.3536 2.4749 -0.3536 -0.3536 -0.3536 -0.3536
-0.3536 -0.3536 -0.3536 2.4749 -0.3536 -0.3536 -0.3536 -0.3536
-0.3536 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536 2.4749 -0.3536
-0.3536 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536 2.4749 -0.3536
-0.3536 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536 2.4749 -0.3536
-0.5401 -0.5401 -0.5401 -0.5401 -0.5401 -0.5401 1.6202 1.6202
-0.3536 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536 -0.3536 2.4749
meanp =
0.5000
0.6250
0.3750
0.3750
0.5000
0.5000
0.2500
0.2500
0.1250
0.1250
0.1250
0.1250
0.1250
0.1250
0.1250
0.1250
0.2500
0.1250
0.1250
0.1250
0.1250
0.1250
0.1250
0.1250
0.1250
0.1250
0.1250
0.1250
0.1250
0.1250
0.2500
0.1250
stdp =
0.5345
0.5175
0.5175
0.5175
0.5345
0.5345
0.4629
0.4629
0.3536
0.3536
0.3536
0.3536
0.3536
0.3536
0.3536
0.3536
0.4629
0.3536
0.3536
0.3536
0.3536
0.3536
0.3536
0.3536
0.3536
0.3536
0.3536
0.3536
0.3536
0.3536
0.4629
0.3536
tn =
-1.6453 -1.0730 -0.5007 0.0715 0.3577 0.6438 0.9299 1.2161
meant =
0.5750
stdt =
0.3495
net =
Neural Network object:
architecture:
numInputs: 1
numLayers: 3
biasConnect: [1; 1; 1]
inputConnect: [1; 0; 0]
layerConnect: [0 0 0; 1 0 0; 0 1 0]
outputConnect: [0 0 1]
targetConnect: [0 0 1]
numOutputs: 1 (read-only)
numTargets: 1 (read-only)
numInputDelays: 0 (read-only)
numLayerDelays: 0 (read-only)
subobject structures:
inputs: {1x1 cell} of inputs
layers: {3x1 cell} of layers
outputs: {1x3 cell} containing 1 output
targets: {1x3 cell} containing 1 target
biases: {3x1 cell} containing 3 biases
inputWeights: {3x1 cell} containing 1 input weight
layerWeights: {3x3 cell} containing 2 layer weights
functions:
adaptFcn: 'trains'
initFcn: 'initlay'
performFcn: 'mse'
trainFcn: 'traingdm'
parameters:
adaptParam: .passes
initParam: (none)
performParam: (none)
trainParam: .epochs, .goal, .lr, .max_fail,
.mc, .min_grad, .show, .time
weight and bias values:
IW: {3x1 cell} containing 1 input weight matrix
LW: {3x3 cell} containing 2 layer weight matrices
b: {3x1 cell} containing 3 bias vectors
other:
userdata: (user stuff)
net =
Neural Network object:
architecture:
numInputs: 1
numLayers: 3
biasConnect: [1; 1; 1]
inputConnect: [1; 0; 0]
layerConnect: [0 0 0; 1 0 0; 0 1 0]
outputConnect: [0 0 1]
targetConnect: [0 0 1]
numOutputs: 1 (read-only)
numTargets: 1 (read-only)
numInputDelays: 0 (read-only)
numLayerDelays: 0 (read-only)
subobject structures:
inputs: {1x1 cell} of inputs
layers: {3x1 cell} of layers
outputs: {1x3 cell} containing 1 output
targets: {1x3 cell} containing 1 target
biases: {3x1 cell} containing 3 biases
inputWeights: {3x1 cell} containing 1 input weight
layerWeights: {3x3 cell} containing 2 layer weights
functions:
adaptFcn: 'trains'
initFcn: 'initlay'
performFcn: 'mse'
trainFcn: 'traingdm'
parameters:
adaptParam: .passes
initParam: (none)
performParam: (none)
trainParam: .epochs, .goal, .lr, .max_fail,
.mc, .min_grad, .show, .time
weight and bias values:
IW: {3x1 cell} containing 1 input weight matrix
LW: {3x3 cell} containing 2 layer weight matrices
b: {3x1 cell} containing 3 bias vectors
other:
userdata: (user stuff)
net =
Neural Network object:
architecture:
numInputs: 1
numLayers: 3
biasConnect: [1; 1; 1]
inputConnect: [1; 0; 0]
layerConnect: [0 0 0; 1 0 0; 0 1 0]
outputConnect: [0 0 1]
targetConnect: [0 0 1]
numOutputs: 1 (read-only)
numTargets: 1 (read-only)
numInputDelays: 0 (read-only)
numLayerDelays: 0 (read-only)
subobject structures:
inputs: {1x1 cell} of inputs
layers: {3x1 cell} of layers
outputs: {1x3 cell} containing 1 output
targets: {1x3 cell} containing 1 target
biases: {3x1 cell} containing 3 biases
inputWeights: {3x1 cell} containing 1 input weight
layerWeights: {3x3 cell} containing 2 layer weights
functions:
adaptFcn: 'trains'
initFcn: 'initlay'
performFcn: 'mse'
trainFcn: 'traingdm'
parameters:
adaptParam: .passes
initParam: (none)
performParam: (none)
trainParam: .epochs, .goal, .lr, .max_fail,
.mc, .min_grad, .show, .time
weight and bias values:
IW: {3x1 cell} containing 1 input weight matrix
LW: {3x3 cell} containing 2 layer weight matrices
b: {3x1 cell} containing 3 bias vectors
other:
userdata: (user stuff)
net =
Neural Network object:
architecture:
numInputs: 1
numLayers: 3
biasConnect: [1; 1; 1]
inputConnect: [1; 0; 0]
layerConnect: [0 0 0; 1 0 0; 0 1 0]
outputConnect: [0 0 1]
targetConnect: [0 0 1]
numOutputs: 1 (read-only)
numTargets: 1 (read-only)
numInputDelays: 0 (read-only)
numLayerDelays: 0 (read-only)
subobject structures:
inputs: {1x1 cell} of inputs
layers: {3x1 cell} of layers
outputs: {1x3 cell} containing 1 output
targets: {1x3 cell} containing 1 target
biases: {3x1 cell} containing 3 biases
inputWeights: {3x1 cell} containing 1 input weight
layerWeights: {3x3 cell} containing 2 layer weights
functions:
adaptFcn: 'trains'
initFcn: 'initlay'
performFcn: 'mse'
trainFcn: 'traingdm'
parameters:
adaptParam: .passes
initParam: (none)
performParam: (none)
trainParam: .epochs, .goal, .lr, .max_fail,
.mc, .min_grad, .show, .time
weight and bias values:
IW: {3x1 cell} containing 1 input weight matrix
LW: {3x3 cell} containing 2 layer weight matrices
b: {3x1 cell} containing 3 bias vectors
other:
userdata: (user stuff)
net =
Neural Network object:
architecture:
numInputs: 1
numLayers: 3
biasConnect: [1; 1; 1]
inputConnect: [1; 0; 0]
layerConnect: [0 0 0; 1 0 0; 0 1 0]
outputConnect: [0 0 1]
targetConnect: [0 0 1]
numOutputs: 1 (read-only)
numTargets: 1 (read-only)
numInputDelays: 0 (read-only)
numLayerDelays: 0 (read-only)
subobject structures:
inputs: {1x1 cell} of inputs
layers: {3x1 cell} of layers
outputs: {1x3 cell} containing 1 output
targets: {1x3 cell} containing 1 target
biases: {3x1 cell} containing 3 biases
inputWeights: {3x1 cell} containing 1 input weight
layerWeights: {3x3 cell} containing 2 layer weights
functions:
adaptFcn: 'trains'
initFcn: 'initlay'
performFcn: 'mse'
trainFcn: 'traingdm'
parameters:
adaptParam: .passes
initParam: (none)
performParam: (none)
trainParam: .epochs, .goal, .lr, .max_fail,
.mc, .min_grad, .show, .time
weight and bias values:
IW: {3x1 cell} containing 1 input weight matrix
LW: {3x3 cell} containing 2 layer weight matrices
b: {3x1 cell} containing 3 bias vectors
other:
userdata: (user stuff)
TRAINGDM, Epoch 0/2500, MSE 1.31351/0.01, Gradient 1.6224/1e-010
TRAINGDM, Epoch 33/2500, MSE 0.00848079/0.01, Gradient 0.145523/1e-010
TRAINGDM, Performance goal met.
a =
0.0459 0.1530 0.3850 0.6065 0.6741 0.7870 0.9007 0.9465
H =
1.0000 0 0.0459 -0.0459
2.0000 0.2000 0.1530 0.0470
3.0000 0.4000 0.3850 0.0150
4.0000 0.6000 0.6065 -0.0065
5.0000 0.7000 0.6741 0.0259
6.0000 0.8000 0.7870 0.0130
7.0000 0.9000 0.9007 -0.0007
8.0000 1.0000 0.9465 0.0535
ans =
1 0.00 0.05 -0.05
2 0.20 0.15 0.05
3 0.40 0.39 0.01
4 0.60 0.61 -0.01
5 0.70 0.67 0.03
6 0.80 0.79 0.01
7 0.90 0.90 -0.00
8 1.00 0.95 0.05
m1 =
0.9594
a1 =
0.0107
r1 =
0.9965
Datauji =
Columns 1 through 15
1.0000 1.0000 1.0000 0 1.0000 1.0000 1.0000 1.0000 1.0000 0 0 0 0 0 1.0000
1.0000 1.0000 0 1.0000 0 0 0 0 0 0 0 0 0 1.0000 0
1.0000 0 0 1.0000 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 1.0000 1.0000 0 0
0 0 1.0000 0 0 0 0 0 0 0 0 0 0 0 0
1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0 0 1.0000 1.0000 0 0 0 0
0 1.0000 0 0 1.0000 1.0000 0 1.0000 0 0 0 0 0 0 0
0 1.0000 0 0 1.0000 1.0000 0 0 0 0 0 0 0 0 0
Columns 16 through 30
1.0000 1.0000 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 1.0000 0 0 0 0 0 1.0000 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 1.0000 1.0000 1.0000 0 0 0
0 0 0 1.0000 1.0000 1.0000 1.0000 1.0000 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1.0000 1.0000 1.0000
0 1.0000 0 0 0 0 0 0 0 0 0 0 0 0 0
Columns 31 through 33
0 0 0
0 0 0.2000
0 0 0.4000
0 0 0.6000
0 0 0.7000
0 0 0.8000
1.0000 0 0.9000
1.0000 1.0000 1.0000
Q =
1 1 1 0 0 1 0 0
1 1 0 0 0 1 1 1
1 0 0 0 1 1 0 0
0 1 1 0 0 1 0 0
1 0 0 0 0 1 1 1
1 0 0 0 0 1 1 1
1 0 0 0 0 1 0 0
1 0 0 0 0 0 1 0
1 0 0 0 0 0 0 0
0 0 0 0 0 1 0 0
0 0 0 0 0 1 0 0
0 0 0 1 0 0 0 0
0 0 0 1 0 0 0 0
0 1 0 0 0 0 0 0
1 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0
1 0 0 0 0 0 0 1
0 0 1 0 0 0 0 0
0 0 0 0 1 0 0 0
0 0 0 0 1 0 0 0
0 0 0 0 1 0 0 0
0 0 0 0 1 0 0 0
0 0 0 0 1 0 0 0
0 0 1 0 0 0 0 0
0 0 0 1 0 0 0 0
0 0 0 1 0 0 0 0
0 0 0 1 0 0 0 0
0 0 0 0 0 0 1 0
0 0 0 0 0 0 1 0
0 0 0 0 0 0 1 0
0 0 0 0 0 0 1 1
0 0 0 0 0 0 0 1
TQ =
0 0.2000 0.4000 0.6000 0.7000 0.8000 0.9000 1.0000
bn =
-1.5139 -1.2075 -0.5435 0.0901 0.2834 0.6066 0.9318 1.0630
b =
0.0459 0.1530 0.3850 0.6065 0.6741 0.7870 0.9007 0.9465
ans =
1 0.00 0.05 7.2f
-4.590011e-002 2.00 0.20 7.2f
1.529989e-001 0.05 3.00 7.2f
4.000000e-001 0.39 0.01 7.2f
4 0.60 0.61 7.2f
-6.497552e-003 5.00 0.70 7.2f
6.740545e-001 0.03 6.00 7.2f
8.000000e-001 0.79 0.01 7.2f
7 0.90 0.90 7.2f
-6.579924e-004 8.00 1.00 7.2f
9.464997e-001 0.05
m2 =
0.9594
b1 =
0.0107
r2 =
0.9965
Data_prediksi =
Columns 1 through 15
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Columns 76 through 90
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Columns 106 through 120
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Columns 121 through 135
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0.5724 0.7726 0.5724 0.1823 0.2878 0.1901 0.1786 0.3543 0.8837 0.6060 0.6397 0.7208 0.4799 0.2772 0.2062
Columns 106 through 120
0.6868 0.1613 0.1904 0.2772 0.7618 0.4439 0.7190 0.1901 0.5849 0.3183 0.3543 0.1613 0.1537 0.2772 0.1823
Columns 121 through 135
0.5484 0.1424 0.3097 0.8957 0.6237 0.1823 0.2466 0.2062 0.1446 0.7371 0.1667 0.6859 0.1052 0.1613 0.1904
Columns 136 through 148
0.1446 0.4722 0.5882 0.2124 0.1446 0.1446 0.2124 0.1446 0.1446 0.2124 0.1446 0.1446 0.2124
