PyBrain:用numpy.loadtxt加载数据?

2024-10-02 18:18:12 发布

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我有一些工作代码可以正确地将csv文件中的数据加载到PyBrain数据集中:

def old_get_dataset():

    reader = csv.reader(open('test.csv', 'rb'))

    header = reader.next()
    fields = dict(zip(header, range(len(header))))
    print header

    # assume last field in csv is single target variable
    # and all other fields are input variables
    dataset = SupervisedDataSet(len(fields) - 1, 1)

    for row in reader:
        #print row[:-1]
        #print row[-1]
        dataset.addSample(row[:-1], row[-1])

    return dataset

现在我试图重写这段代码,以使用numpy的loadtxt函数。我相信addSample可以使用numpy数组,而不必一次添加一行数据。

假设我加载的numpy数组是m x n维,那么如何将第一个m x(n-1)数据集作为第一个参数传递,将最后一列数据作为第二个参数传递?这就是我想要的:

def get_dataset():

    array = numpy.loadtxt('test.csv', delimiter=',', skiprows=1)

    # assume last field in csv is single target variable
    # and all other fields are input variables
    number_of_columns = array.shape[1]
    dataset = SupervisedDataSet(number_of_columns - 1, 1)

    #print array[0]
    #print array[:,:-1]
    #print array[:,-1]
    dataset.addSample(array[:,:-1], array[:,-1])

    return dataset

但我得到了以下错误:

Traceback (most recent call last):
  File "C:\test.py", line 109, in <module>
    (d, n, t) = main()
  File "C:\test.py", line 87, in main
    ds = get_dataset()
  File "C:\test.py", line 45, in get_dataset
    dataset.addSample(array[:,:-1], array[:,-1])
  File "C:\Python27\lib\site-packages\pybrain\datasets\supervised.py",
       line 45, in addSample self.appendLinked(inp, target)
  File "C:\Python27\lib\site-packages\pybrain\datasets\dataset.py",
       line 215, in appendLinked self._appendUnlinked(l, args[i])
  File "C:\Python27\lib\site-packages\pybrain\datasets\dataset.py",
       line 197, in _appendUnlinked self.data[label][self.endmarker[label], :] = row
ValueError: output operand requires a reduction, but reduction is not enabled

我该怎么解决?


Tags: csv数据inpytestgetlinearray
2条回答

经过大量的实验和重新读取dataset documentation后,以下命令运行无误:

def get_dataset():

    array = numpy.loadtxt('test.csv', delimiter=',', skiprows=1)

    # assume last field in csv is single target variable
    # and all other fields are input variables
    number_of_columns = array.shape[1]
    dataset = SupervisedDataSet(number_of_columns - 1, 1)

    print array[0]
    #print array[:,:-1]
    #print array[:,-1]
    #dataset.addSample(array[:,:-1], array[:,-1])
    #dataset.addSample(array[:,:-1], array[:,-2:-1])
    dataset.setField('input', array[:,:-1])
    dataset.setField('target', array[:,-1:])

    return dataset

我得再检查一下它做的对不对。

我写了一个函数来做这个

def load_csv(filename, cols, sep = ',', skip = 0):
    from numpy import loadtxt
    data = loadtxt(filename, delimiter = sep, usecols = cols, skiprows = skip)
    return data

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