我在Linux CentOS 7机器上使用Python 2.7.5.我正在尝试应用概率神经网络(PNN)我的数据集来解决二进制分类问题.
我正在使用以下Python包:numpy,sklearn,neupy.algorithms.我正在尝试使用这个用于虹膜数据集的示例.
问题是我的PNN总是预测零值(被归类为零的元素),我无法理解为什么......
这是我的数据集("dataset_file.csv").有34个功能和1个标签目标(最后一列,可能是0或1):
47,1,0,1,0,20,1,0,1,24,1,1,0,2,1,8050,9,1,274,60,258,65,7,3.2,105,289,0,0,79,1,0,0,0,34,0
55,1,0,1,0,45,1,0,0,1,1,1,1,3,0,11200,7,0,615,86,531,97,5.4,2.6,96,7541,1.6,0.8,6,1,1,1,1,42,0
29,1,1,1,0,23,0,1,0,1,0,0,0,2,1,5300,12,1,189,30,203,72,7,3.5,93,480,0,0,90,1,0,0,0,43,1
39,1,0,1,0,10,1,0,0,3,0,1,1,0,1,7910,14,1,462,28,197,50,8,4.5,93,459,5,2.8,45,1,1,0,0,21,0
47,1,0,1,0,10,1,1,1,1.5,1,1,0,3,1,9120,4,0,530,71,181,60,6.2,3.8,83,213,3.6,1.95,53,1,1,0,0,11,0
57,1,0,1,0,50,0,1,0,24,1,0,1,3,1,16000,9,0,330,78,172,74,5.9,2.9,112,332,4.1,2.1,82,1,1,0,0,23,1
44,1,0,1,0,15,1,1,0,0.5,1,1,1,2,0,5800,14,0,275,44,155,105,7.2,3.5,84,360,3.44,1.6,55,1,1,0,0,24,0
49,1,3,1,0,25,1,1,1,1,0,1,0,3,1,8200,12,1,441,74,237,111,6.2,3.6,79,211,0,0,91,1,0,0,0,43,0
56,1,0,1,0,5,1,0,0,3,1,0,0,3,1,5100,7,1,188,58,185,62,7.8,3.9,112,472,0,0,83,1,0,0,0,34,0
33,1,4,1,0,20,1,0,1,3,0,0,0,3,1,7300,10,1,329,40,139,80,6.9,3.7,89,122,3.4,1.2,75,1,1,0,0,33,0
22,0,0,1,0,15,1,0,0,1,1,1,1,0,1,3700,8,0,617,53,267,128,6.2,3.8,91,3060,3.1,1.9,63,1,1,0,0,54,0
82,0,5,1,0,60,1,0,1,3,1,1,1,0,0,8900,11,1,275,83,255,93,5.9,3.1,95,455,4.8,1.9,68,1,1,0,0,55,0
49,0,2,1,0,20,1,0,1,2,1,0,0,0,1,8500,6,1,292,84,241,79,6.8,3.9,100,158,3.4,1.25,75,1,1,1,0,65,0
51,1,4,1,0,51,1,1,1,2,1,0,1,0,1,18300,14,1,522,91,268,105,6.1,3.1,98,758,4.2,2.5,19,1,1,1,1,67,0
61,1,2,1,0,20,1,0,0,3,1,0,0,3,1,6600,9,1,563,101,268,78,6.4,3.7,115,694,5.2,3,29,1,1,1,1,77,0
48,0,1,1,0,28,1,0,0,12,1,0,0,3,1,9100,22,0,114,18,165,63,7.2,3.6,103,429,0,0,84,1,0,0,0,34,0
57,0,0,1,0,40,1,0,1,1,0,0,0,3,1,8100,8,0,264,15,120,69,6.8,3.4,91,390,0,0,91,1,0,0,0,23,0
57,0,0,1,0,25,1,0,0,12,0,1,0,0,0,6900,16,0,847,111,289,78,5.3,2.4,105,162,3.1,1.9,68,1,1,1,0,78,0
47,1,4,1,0,40,0,1,1,6,1,1,1,2,1,21500,10,0,632,121,219,108,7.5,2.8,149,1158,3.17,1.77,8,1,1,1,0,58,1
52,0,0,1,0,30,1,1,1,2,1,0,1,0,0,14600,5,1,405,88,280,140,5.8,3.1,121,983,3.9,1.8,17,1,1,1,1,76,0
50,1,2,1,0,16,1,1,0,1,1,1,1,0,1,12200,9,1,280,7,176,71,7.4,4.2,105,293,4.5,2.7,68,1,1,0,0,67,0
63,1,4,1,0,18,1,0,1,0.5,1,1,1,3,0,16400,8,0,479,93,140,64,5.8,3.7,226,1286,6.22,3.6,18,1,1,0,0,19,0
54,0,0,1,0,20,0,0,1,8,0,0,1,0,1,7200,10,0,366,71,284,73,6.4,3.7,114,384,4.1,2.8,65,1,1,0,0,24,1
31,0,3,1,0,10,0,1,0,1,1,1,1,1,1,3800,8,0,568,102,236,59,6.4,3.7,99,387,0,0,78,1,0,0,0,45,1
44,0,6,1,0,10,1,1,0,2,1,1,1,0,1,7700,15,1,274,44,139,62,6.7,4.1,93,129,0,0,76,1,0,0,0,24,0
50,0,6,1,0,20,1,0,0,3,1,1,0,0,1,5200,6,0,403,90,224,79,6.3,3.1,109,151,3.1,1.4,79,1,0,0,0,34,0
61,1,3,1,0,30,0,1,0,3,1,0,1,2,1,11500,7,0,668,88,178,65,6.7,3.08,104,680,4.1,2.5,22,1,1,0,0,23,1
这是我的Python代码:
import numpy as np
from sklearn import datasets
from sklearn.metrics import matthews_corrcoef
from sklearn.cross_validation import StratifiedKFold
from neupy.algorithms import PNN
fileName="dataset_file.csv"
TARGET_COLUMN=35
from numpy import genfromtxt
input_dataset_data = genfromtxt(fileName, delimiter=',', skip_header=0, usecols=(range(0, TARGET_COLUMN-1)))
#print(input_dataset_data)
input_dataset_target = genfromtxt(fileName, …