0. Background
NetLogo is a very useful tools for ABM, and Python is also a handful language for building proof of concept.
In this post I will show you how to call python language in NetLogo. For more information please follow here.
1. NetLogo version
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breed [data-points data-point]
breed [centroids centroid]
globals [
any-centroids-moved?
]
to setup
clear-all
set-default-shape data-points "circle"
set-default-shape centroids "x"
generate-clusters
reset-centroids
end
to generate-clusters
let cluster-std-dev 20 - num-clusters
let cluster-size num-data-points / num-clusters
repeat num-clusters [
let center-x random-xcor / 1.5
let center-y random-ycor / 1.5
create-data-points cluster-size [
setxy center-x center-y
set heading random 360
fd abs random-normal 0 (cluster-std-dev / 2) ;; Divide by two because abs doubles the width
]
]
end
to reset-centroids
set any-centroids-moved? true
ask data-points [ set color grey ]
let colors base-colors
ask centroids [die]
create-centroids num-centroids [
move-to one-of data-points
set size 5
set color last colors + 1
set colors butlast colors
]
clear-all-plots
reset-ticks
end
to go
if not any-centroids-moved? [stop]
set any-centroids-moved? false
assign-clusters
update-clusters
tick
end
to assign-clusters
ask data-points [set color [color] of closest-centroid - 2]
end
to update-clusters
let movement-threshold 0.1
ask centroids [
let my-points data-points with [ shade-of? color [ color ] of myself ]
if any? my-points [
let new-xcor mean [ xcor ] of my-points
let new-ycor mean [ ycor ] of my-points
if distancexy new-xcor new-ycor > movement-threshold [
set any-centroids-moved? true
]
setxy new-xcor new-ycor
]
]
update-plots
end
to-report closest-centroid
report min-one-of centroids [ distance myself ]
end
to-report square-deviation
report sum [ (distance myself) ^ 2 ] of data-points with [ closest-centroid = myself ]
end
; Copyright 2014 Uri Wilensky.
; See Info tab for full copyright and license.
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2. TensorFlow version
TensorFlow version: 1.14
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import numpy as np
import tensorflow as tf
num_points = 100
dimensions = 2
points = np.random.uniform(0, 1000, [num_points, dimensions])
def input_fn():
return tf.compat.v1.train.limit_epochs(
tf.convert_to_tensor(points, dtype=tf.float32), num_epochs=1)
num_clusters = 5
kmeans = tf.contrib.factorization.KMeansClustering(
num_clusters=num_clusters, use_mini_batch=False)
# train
num_iterations = 10
previous_centers = None
for _ in xrange(num_iterations):
kmeans.train(input_fn)
cluster_centers = kmeans.cluster_centers()
if previous_centers is not None:
print 'delta:', cluster_centers - previous_centers
previous_centers = cluster_centers
print 'score:', kmeans.score(input_fn)
print 'cluster centers:', cluster_centers
# map the input points to their clusters
cluster_indices = list(kmeans.predict_cluster_index(input_fn))
for i, point in enumerate(points):
cluster_index = cluster_indices[i]
center = cluster_centers[cluster_index]
print 'point:', point, 'is in cluster', cluster_index, 'centered at', center
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3. NetLogo with Python Extension version
Here’s the snapshot.
And here’s the code.
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extensions [ py ]
breed [data-points data-point]
breed [centroids centroid]
data-points-own [
cluster-id
]
centroids-own [
cluster-id
centx
centy
]
globals [
testoutput
centroid-list
]
to setup
clear-all
py:setup py:python
(py:run
"import tensorflow as tf"
"import numpy as np"
)
set testoutput py:runresult "1"
py:set "testoutput" testoutput
set-default-shape data-points "circle"
set-default-shape centroids "x"
generate-clusters
; For python
py:set "num_points" num-clusters
py:set "points" [list xcor ycor] of data-points
py:set "num_clusters" num-clusters
py:set "num_round" num-round
if debug = True
[
py:run "print('Points Cordinates:', points)" ;for debug
]
;reset-centroids
end
to generate-clusters
set testoutput py:runresult "testoutput + 1"
let cluster-std-dev cluster-range
let cluster-size num-data-points / num-clusters
repeat num-clusters [
let center-x random-xcor / 1.5
let center-y random-ycor / 1.5
create-data-points cluster-size [
setxy center-x center-y
set heading random 360
fd abs random-normal 0 (cluster-std-dev / 2)
]
]
end
to train
; Cluster center
(py:run
"points = np.asarray(points)"
"def input_fn():"
" return tf.compat.v1.train.limit_epochs(tf.convert_to_tensor(points, dtype=tf.float32), num_epochs=1)"
"kmeans = tf.contrib.factorization.KMeansClustering(num_clusters=num_clusters, use_mini_batch=False)"
"num_iterations = num_round"
"previous_centers = None"
"for _ in range(num_iterations):"
" kmeans.train(input_fn)"
" cluster_centers = kmeans.cluster_centers()"
" if previous_centers is not None:"
" print(('delta:', cluster_centers - previous_centers))"
" previous_centers = cluster_centers"
" print(('score:', kmeans.score(input_fn)))"
"print(('cluster centers:', cluster_centers))"
"# map the input points to their clusters"
"cluster_indices = list(kmeans.predict_cluster_index(input_fn))"
"print('cluster indices: ', cluster_indices)"
"for i, point in enumerate(points):"
" cluster_index = cluster_indices[i]"
" center = cluster_centers[cluster_index]"
" print(('point:', point, 'is in cluster', cluster_index, 'centered at', center))"
)
end
to show-shape
set centroid-list py:runresult "cluster_centers"
foreach centroid-list [
x -> create-centroids 1 [
set xcor ( item 0 x )
set ycor ( item 1 x )
set size 3
set color white
]
]
end
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Ref:
[1] https://www.altoros.com/blog/using-k-means-clustering-in-tensorflow/
[2] https://www.tensorflow.org/api_docs/python/tf/contrib/factorization/KMeansClustering