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在机器学习过程中,对数据的处理过程中,常常需要对数据进行归一化处理,下面介绍(0, 1)标准化的方式,简单的说,其功能就是将预处理的数据的数值范围按一定关系“压缩”到(0,1)的范围类。
10年积累的网站设计制作、做网站经验,可以快速应对客户对网站的新想法和需求。提供各种问题对应的解决方案。让选择我们的客户得到更好、更有力的网络服务。我虽然不认识你,你也不认识我。但先网站设计制作后付款的网站建设流程,更有高平免费网站建设让你可以放心的选择与我们合作。通常(0, 1)标注化处理的公式为:
即将样本点的数值减去最小值,再除以样本点数值大与最小的差,原理公式就是这么基础。
下面看看使用python语言来编程实现吧
import numpy as np import matplotlib.pyplot as plt def noramlization(data): minVals = data.min(0) maxVals = data.max(0) ranges = maxVals - minVals normData = np.zeros(np.shape(data)) m = data.shape[0] normData = data - np.tile(minVals, (m, 1)) normData = normData/np.tile(ranges, (m, 1)) return normData, ranges, minVals x = np.array([[78434.0829, 26829.86612], [78960.4042, 26855.13451], [72997.8308, 26543.79201], [74160.2849, 26499.56629], [75908.5746, 26220.11996], [74880.6989, 26196.03995], [74604.7169, 27096.87862], [79547.6796, 25986.68579], [74997.7791, 24021.50132], [74487.4915, 26040.18441], [77134.2636, 24647.274], [74975.2792, 24067.31441], [76013.5305, 24566.02273], [79191.518, 26840.29867], [80653.4589, 25937.22248], [79185.9935, 26996.18228], [74426.881, 24227.71439], [73246.4295, 26561.59268], [77963.1478, 25580.05298], [74469.8778, 26082.15448], [81372.3787, 26649.69232], [76826.8262, 24549.77367], [77774.2608, 25999.96037], [79673.1361, 25229.04353], [75251.7951, 24902.72185], [78458.073, 23924.15117], [82247.5439, 29671.33493], [82041.2247, 27903.34268], [80083.2029, 28692.35517], [80962.0043, 28519.81002], [79799.8328, 28740.27736], [80743.9947, 28862.75402], [80888.449, 29724.53706], [81768.4638, 30180.20618], [80283.8783, 30417.55057], [79460.7078, 29092.52867], [75514.1202, 28071.73721], [80595.5945, 30292.25917], [80750.4876, 29651.32254], [80020.662, 30023.70025], [82992.3395, 29466.83067], [80185.5946, 29943.15481], [81854.6163, 29846.18257], [81526.4017, 30218.27078], [79174.5312, 29960.69999], [78112.3051, 26467.57545], [80262.4121, 29340.23218], [81284.9734, 28257.71529], [81928.9905, 28752.84811], [80739.2727, 29288.85126], [83135.3435, 30223.4974], [83131.8223, 29049.10112], [82549.9076, 28910.15209], [81574.0822, 28326.55367], [80507.399, 28553.56851], [82956.2103, 29157.62372], [81909.7132, 29359.24497], [80893.5603, 29326.64155], [82520.1272, 30424.96703], [82829.8548, 31062.24418], [80532.1495, 29198.10407], [80112.7963, 29143.47905], [81175.0882, 28443.10574]]) newgroup, _, _ = noramlization(x) newdata = newgroup plt.scatter(x[:, 0], x[:, 1], marker='*', c='r', s=24) plt.show() print(len(x[:, 0])) print(len(x[:, 1])) print(newdata)