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。
通常(0,1)标注化处理的公式为:
<数学> <语义> <我>
即将样本点的数值减去最小值,再除以样本点数值最大与最小的差,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
进口numpy np
进口matplotlib。pyplot作为plt
def noramlization(数据):
minVals=data.min (0)
maxVals=data.max (0)=maxVals - minVals范围
normData=https://www.yisu.com/zixun/np.zeros (np.shape(数据)
m=data.shape [0]
normData=数据- np。瓷砖(minVals (m, 1))
normData=normData/np。瓷砖(范围,(m, 1))
返回normData,范围,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。散射(x: 0, x(: 1)标志=?’,c=r, s=24)
plt.显示（）
print (len (x [: 0]))
打印(len (x [: 1)))
打印(newdata)
将数据进行归一化处理后,并使用matplotlib绘制出处理后的散点图分布如下:
详解python实现数据归一化处理的方式:(0,1)标准化
