runtime comparison

2019-04-15 16:09发布

站内文章 / PIC单片机
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""" Table 3. Runtime comparison (in milliseconds) of PIC and spectral clustering algorithms on several real datasets. Dataset Size NCutE NCutI PIC Iris 150 17 61 1 PenDigits01 200 28 23 1 PenDigits17 200 30 36 1 PolBooks 102 7 22 1 UBMCBlog 404 104 32 1 AGBlog 1222 1095 70 3 20ngA 200 32 37 1 20ngB 400 124 56 3 20ngC 600 348 213 5 20ngD 800 584 385 10 Table 4. Runtime comparison (in milliseconds) of PIC and spectral clustering algorithms on synthetic datasets. Nodes Edges NCutE NCutI PIC 1k 10k 1885 177 1 5k 250k 154797 6939 7 10k 1000k 1111441 42045 34 50k 25000k - - 849 100k 100000k - - 2960 """ import matplotlib.pyplot as plt import numpy as np plt.style.use('ggplot') data = np.array([[17, 61, 1], [28, 23, 1], [30, 36, 1], [7, 22, 1], [104, 32, 1], [1095, 70, 3], [32, 37, 1], [124, 56, 3], [348, 213, 5], [584, 385, 10]]) n_rows, n_cols = data.shape width = 0.5 index = np.arange(n_rows) * (n_cols + 1) * width bars = [] for i in range(n_cols): bars.append(plt.bar(index + i * width, data[:, i], width)) for bar in bars: for item in bar: y = item.get_height() plt.text(item.get_x() + 0.5 * width, y + 1, int(y), ha='center', va='bottom', size=6) ticks = ["Iris", "PenDigits01", "PenDigits17", "PolBooks", "UBMCBlog", "AGBlog", "20ngA", "20ngB", "20ngC", "20ngD"] plt.xticks(index + 0.5 * n_cols * width, ticks, rotation=90) plt.tight_layout() plt.show()

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