Question

我在徘徊如何加载我的数据集，以便从libsvm python实现中读取它。我的数据是250X500矩阵，第一列是数据集标签。我使用以下代码来读取数据：

with open("dataset3.txt") as textFile:
lines = [line.split() for line in textFile]

Matrix = [[0 for x in xrange(len(lines[0]))] for x in xrange(len(lines))] 

for y in range(0, len(lines)):
    for x in range(0, len(lines[0])):
        Matrix[y][x] = lines[y][x]

使用上面的代码，我将我的数据读入int的2d数组。我如何使用这个数组来执行svm训练和评估？

param = svm_parameter('-t 0 -c 4 -b 1')
m = svm_train(Matrix, param)

文字档案：

1   0   9   0   0   0   0   5   2   5   15   2   3   50   0   4   6   27   0   16   34   0   11   30   12   23   41   1   0   2   0   10   67   34 ...
-1   0   10   0   0   0   0   1   0   2   5   1   8   14   0   12   11   4   2   4   22   0   6   40   8   20   47   2   1   0   0   2   1   21   0   1   11   1  ...
...

Matrix = []
with open('dataset3.txt') as f:
row = []
for line in f:
    data = line.split()
    target = float(data[0]) # target value
    str1 = str(target) 
    for i,j in enumerate(data):
        if i==0:
            continue
        else:   
            str1 = str1 + " " + str(i) +":"+ str(j) +" " 
row.append(str1)

Answer 1

试试此代码

with open('dataset3.txt') as f:
    Matrix = [map(float, line.split()) for line in f]

for line in f读取每一行。
line.split()分为每个value
map(float, line.split())将value转换为float

<强>更新

OP评论了不同的输入格式。

Matrix = []
with open('dataset3.txt') as f:
    for line in f:
        data = line.split()
        target = float(data[0]) # target value
        row = []

        for i, (idx, value) in enumerate([item.split(':') for item in data[1:]]):
            n = int(idx) - (i + 1) # num missing
            for _ in range(n):
                row.append(0) # for missing
            row.append(float(value))
        Matrix.append(row)

如何在libsvm python中加载数据集

1 个答案: