79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
import kmeans as km
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import som
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import numpy as np
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import utils
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import json
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METHODS = ['forgy', 'random_partition']
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SOM_INIT_METHODS = ['random', 'zeros']
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def get_datas_from_json():
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datas = []
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with open('data1.json', 'r') as d:
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datas.append(json.loads(d.read()))
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with open('data2.json', 'r') as d:
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datas.append(json.loads(d.read()))
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return datas
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def get_datas_random():
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datas = []
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for get_data in [utils.get_data1, utils.get_data2]:
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datas.append(get_data())
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return datas
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def main():
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datas = get_datas_from_json()
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rand = np.random.RandomState(0)
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index = 1
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print("Self-organizing map")
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for data in datas:
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print(f'Data set: {index}')
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utils.plot_data(data)
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for method in SOM_INIT_METHODS:
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print(f'Initialization method: {method}')
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errors = []
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for k in range(2, 21, 2):
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som_data = som.init_neurons(data, k, rand, method)
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soms_with_error = som.train_som(som_data, data, algorithm='kohonen')
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error = soms_with_error[-1][1]
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errors.append((k, error))
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soms,_ = zip(*soms_with_error)
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#som.plot_with_data(soms, data, f'_{method}_{k}_data{index}')
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utils.plot_error_data(errors)
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soms_with_errors = []
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for _ in range(100):
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som_data = som.init_neurons(data, k, rand, method)
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soms_with_error = som.train_som(som_data, data, algorithm='kohonen')
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soms_with_errors.append(soms_with_error[-1])
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som.print_som_stats(soms_with_errors, data)
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index += 1
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index = 1
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for data in datas:
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utils.plot_data(data)
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for method in METHODS:
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print(f'Method: {method}')
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kmeans_data = {}
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for k in range(2, 21):
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kmeans_with_err = km.kmeans(data, method, k)
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km.print_stats(k, [(iterations[-1], err)
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for iterations, err in kmeans_with_err])
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min_err = kmeans_with_err[0][1]
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kmeans = kmeans_with_err[0][0]
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for temp_kmeans, err in kmeans_with_err:
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if err < min_err:
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min_err = err
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kmeans = temp_kmeans
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kmeans_data[k] = (kmeans, min_err)
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km.plot_kmeans(kmeans, k, f'_{method}_{k}_{index}')
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error_data = [[i, kmeans_data[i][1]] for i in range(2, 21, 2)]
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utils.plot_error_data(error_data)
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index += 1
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if __name__ == '__main__':
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main()
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