A python console application to simulate Self Organizing Map computation
Easily set-up the computation for SOM by overriding these values in app.py
# Number of iterations
iterations = 10
# Patterns per batch, (1 for online learning)
pattern_per_batch = 1
# Learning Rate
alpha = 1
# Radius between neighbors
radius = 1
# Number of clusters
clusters = 4
# Number of features
features = 3
sigma0 = 1
# Weights
weight = np.array([
[1.,2.,1.], [2.,-2.,2.], [1.,-1.,1.], [1.,3.,1.]
])
# Patterns
temp_patterns = np.array([
[-1,1,3],
[1,-2,1]
])After configuring the variables for SOM computation, its time to run the computation.
First, run the function generate_patterns(number_of_iterations, patterns_per_batch)
The first argument, number_of_iterations is the amount of iterations to be done. The variable iterations will be passed here.
The second argument, patterns_per_batch is the amount of pattern in a batch. The variable pattern_per_batch will be passed here.
Note: In case of online learning, you'd want to put 1 pattern per batch.
After running generate_patterns, run the function online_learning to do the actual computations and prints out the results.
Here goes the sample results from a SOM calculation (2 iterations, online learning):
Current Iteration: 1
Calculating data: 1
===================================================
Distance 1 : √(-1-1.0)^2 + (1-2.0)^2 + (3-1.0)^2 = √9.0 = 3.0
Distance 2 : √(-1-2.0)^2 + (1--2.0)^2 + (3-2.0)^2 = √19.0 = 4.358898943540674
Distance 3 : √(-1-1.0)^2 + (1--1.0)^2 + (3-1.0)^2 = √12.0 = 3.4641016151377544
Distance 4 : √(-1-1.0)^2 + (1-3.0)^2 + (3-1.0)^2 = √12.0 = 3.4641016151377544
Winner: 1
Weights to Update: [1, 2]
Updating weight: 1
-------------------------
Sigma 1 : 0.6065306597126334
Neighbor strength: 1.0
W1 = [1. 2. 1.] + (1.0)(1)([-1 1 3] - [1. 2. 1.])
[[-1. 1. 3.]
[ 2. -2. 2.]
[ 1. -1. 1.]
[ 1. 3. 1.]]
Updating weight: 2
-------------------------
Sigma 2 : 0.6065306597126334
Neighbor strength: 0.2568813653134702
W2 = [ 2. -2. 2.] + (0.2568813653134702)(1)([-1 1 3] - [ 2. -2. 2.])
[[-1. 1. 3. ]
[ 1.2293559 -1.2293559 2.25688137]
[ 1. -1. 1. ]
[ 1. 3. 1. ]]
Ending current iteration, Updating weight...
Final weight:
[[-1. 1. 3. ]
[ 1.2293559 -1.2293559 2.25688137]
[ 1. -1. 1. ]
[ 1. 3. 1. ]]
Current Iteration: 2
Calculating data: 1
===================================================
Distance 1 : √(1--1.0)^2 + (-2-1.0)^2 + (1-3.0)^2 = √17.0 = 4.123105625617661
Distance 2 : √(1-1.2293559040595894)^2 + (-2--1.2293559040595894)^2 + (1-2.25688136531347)^2 = √2.226247219807057 = 1.4920613994762604
Distance 3 : √(1-1.0)^2 + (-2--1.0)^2 + (1-1.0)^2 = √1.0 = 1.0
Distance 4 : √(1-1.0)^2 + (-2-3.0)^2 + (1-1.0)^2 = √25.0 = 5.0
Winner: 3
Weights to Update: [2, 3, 4]
Updating weight: 2
-------------------------
Sigma 2 : 0.36787944117144233
Neighbor strength: 0.024859183199194095
W2 = [ 1.2293559 -1.2293559 2.25688137] + (0.024859183199194095)(1)([ 1 -2 1] - [ 1.2293559 -1.2293559 2.25688137])
[[-1. 1. 3. ]
[ 1.2236543 -1.24851349 2.22563632]
[ 1. -1. 1. ]
[ 1. 3. 1. ]]
Updating weight: 3
-------------------------
Sigma 3 : 0.36787944117144233
Neighbor strength: 1.0
W3 = [ 1. -1. 1.] + (1.0)(1)([ 1 -2 1] - [ 1. -1. 1.])
[[-1. 1. 3. ]
[ 1.2236543 -1.24851349 2.22563632]
[ 1. -2. 1. ]
[ 1. 3. 1. ]]
Updating weight: 4
-------------------------
Sigma 4 : 0.36787944117144233
Neighbor strength: 0.024859183199194095
W4 = [1. 3. 1.] + (0.024859183199194095)(1)([ 1 -2 1] - [1. 3. 1.])
[[-1. 1. 3. ]
[ 1.2236543 -1.24851349 2.22563632]
[ 1. -2. 1. ]
[ 1. 2.87570408 1. ]]
Ending current iteration, Updating weight...
Final weight:
[[-1. 1. 3. ]
[ 1.2236543 -1.24851349 2.22563632]
[ 1. -2. 1. ]
[ 1. 2.87570408 1. ]]