54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
import json
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import random
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import numpy as np
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data = []
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with open("features_1k.json") as file_json:
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data = json.load(file_json)
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print(len(data))
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random.shuffle(data)
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def calculate_ratio(split_ratio, thresh_hold):
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# split_ratio = 0.2
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# thresh_hold = 0.7
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nTest = int (len(data) * split_ratio)
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nTrain = len(data) - nTest
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test_data = np.array([x["feature"] for x in data[:nTest]])
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train_data = np.array([x["feature"] for x in data[nTest:]])
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# print(split_ratio, test_data.shape, train_data.shape)
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min_dists = []
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for i, row in enumerate(test_data):
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row = np.reshape(row, (1, row.shape[0]))
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repeat_row = np.tile(row, (nTrain, 1))
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dists = repeat_row - train_data
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dists = np.sqrt(np.sum(np.square(dists), axis=1))
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# print(dists)
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min_dist_idx = np.argmin(dists)
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# print(i, min_dist_idx, dists[min_dist_idx])
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min_dists.append(dists[min_dist_idx])
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min_dists = np.array(min_dists)
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# print(min_dists.shape)
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# print(min_dists[min_dists > 0.75].shape)
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t1 = min_dists[min_dists > thresh_hold].shape[0]
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t2 = min_dists.shape[0]
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print('\tthresh:', thresh_hold, '\tratio:', t1/t2, '(', t1, '/', t2 ,')')
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return t1/t2
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for j in range(10, 60, 5):
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split = j / 100
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print('\nSplit test/train:', split*10, '/', 10-split*10, '====================================')
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best_thresh = 0
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best_ratio = 0
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for i in range(50, 110, 2):
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thresh = i / 100
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ratio = calculate_ratio(split, thresh)
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if ratio >= best_ratio:
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best_ratio = ratio
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best_thresh =thresh
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print('\tBEST THRESH: ', best_thresh, 'with ratio ', best_ratio)
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