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# Data Management

## Importing and exporting data

`mlpy.``data_fromfile`(file, ytype=<type 'int'>)

  Read data file in the form:

      x11 [TAB] x12 [TAB] ... x1n [TAB] y1
      x21 [TAB] x22 [TAB] ... x2n [TAB] y2
       .         .        .    .        .
       .         .         .   .        .
       .         .          .  .        .
      xm1 [TAB] xm2 [TAB] ... xmn [TAB] ym

  where xij are float and yi are of type ‘ytype’ (numpy.int or numpy.float).

  Input

  > - file - data file name
  > - ytype - numpy datatype for labels (numpy.int or numpy.float)

  Output

  > - x - data [2D numpy array float]
  > - y - classes [1D numpy array int or float]

  Example:

      >>> from numpy import *
      >>> from mlpy import *
      >>> x, y = data_fromfile('data_example.dat')
      >>> x
      array([[ 1.1,  2. ,  5.3,  3.1],
      ...    [ 3.7,  1.4,  2.3,  4.5],
      ...    [ 1.4,  5.4,  3.1,  1.4]])
      >>> y
      array([ 1, -1,  1])

`mlpy.``data_fromfile_wl`(file)

  Read data file in the form:

      x11 [TAB] x12 [TAB] ... x1n [TAB]
      x21 [TAB] x22 [TAB] ... x2n [TAB]
       .         .        .    .
       .         .         .   .
       .         .          .  .
      xm1 [TAB] xm2 [TAB] ... xmn [TAB]

  where xij are float.

  Input

  > - file - data file name

  Output

  > - x - data [2D numpy array float]

  Example:

      >>> from numpy import *
      >>> from mlpy import *
      >>> x, y = data_fromfile('data_example.dat')
      >>> x
      array([[ 1.1,  2. ,  5.3,  3.1],
      ...    [ 3.7,  1.4,  2.3,  4.5],
      ...    [ 1.4,  5.4,  3.1,  1.4]])

`mlpy.``data_tofile`(file, x, y, sep='\t')

  Write data file in the form:

      x11 [sep] x12 [sep] ... x1n [sep] y1
      x21 [sep] x22 [sep] ... x2n [sep] y2
       .         .        .    .        .
       .         .         .   .        .
       .         .          .  .        .
      xm1 [sep] xm2 [sep] ... xmn [sep] ym

  where xij are float and yi are integer.

  Input

  > - file - data file name
  > - x - data [2D numpy array float]
  > - y - classes [1D numpy array integer]
  > - sep - separator

`mlpy.``data_tofile_wl`(file, x, sep='\t')

  Write data file in the form:

      x11 [sep] x12 [sep] ... x1n [sep]
      x21 [sep] x22 [sep] ... x2n [sep]
       .         .        .    .
       .         .         .   .
       .         .          .  .
      xm1 [sep] xm2 [sep] ... xmn [sep]

  where xij are float.

  Input

  > - file - data file name
  > - x - data [2D numpy array float]
  > - sep - separator

## Normalization

`mlpy.``data_normalize`(x)

  Normalize numpy array (2D) x.

  Input

  > - x - data [2D numpy array float]

  Output

  > - normalized data

  Example:

      >>> from numpy import *
      >>> from mlpy import *
      >>> x = array([[ 1.1,  2. ,  5.3,  3.1],
      ...            [ 3.7,  1.4,  2.3,  4.5],
      ...            [ 1.4,  5.4,  3.1,  1.4]])
      >>> data_normalize(x)
      array([[-0.9797065 , -0.48295391,  1.33847226,  0.12418815],
      ...    [ 0.52197912, -1.13395464, -0.48598056,  1.09795608],
      ...    [-0.75217354,  1.35919078,  0.1451563 , -0.75217354]])

  Warning

  Deprecated in version 2.3

`mlpy.``data_standardize`(x, p=None)

  Standardize numpy array (2D) x and optionally standardize p using mean and std of x.

  Input

  > - x - data [2D numpy array float]
  > - p - optional data [2D numpy array float]

  Output

  > - standardized data

  Example:

      >>> from numpy import *
      >>> from mlpy import *
      >>> x = array([[ 1.1,  2. ,  5.3,  3.1],
      ...            [ 3.7,  1.4,  2.3,  4.5],
      ...            [ 1.4,  5.4,  3.1,  1.4]])
      >>> data_standardize(x)
      array([[-0.67958381, -0.43266792,  1.1157668 ,  0.06441566],
      ...    [ 1.1482623 , -0.71081158, -0.81536804,  0.96623494],
      ...    [-0.46867849,  1.1434795 , -0.30039875, -1.0306506 ]])

  Warning

  Deprecated in version 2.3. Use mlpy.standardize and mlpy.standardize_from instead

`mlpy.``standardize`(x)

  Standardize x.

  x is standardized to have mean 0 and unit length by columns. Return standardized x, the mean and the standard deviation.

`mlpy.``center`(y)

  Center y to have mean 0.

  Return centered y.

`mlpy.``standardize_from`(x, mean, std)

  Standardize x using external mean and standard deviation.

  Return standardized x.

`mlpy.``center_from`(y, mean)

  Center y using external mean.

  Return centered y.

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