Mercurial > ift6266

annotate datasets/ftfile.py @ 624:49933073590c

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added jmlr_review1.txt and jmlr_review2.txt
author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Sun, 13 Mar 2011 18:25:25 -0400
parents 337253b82409
children
rev   line source
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1 from itertools import izip
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2 import os
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3
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4 import numpy
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5 from pylearn.io.filetensor import _read_header, _prod
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6
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7 from dataset import DataSet
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8 from dsetiter import DataIterator
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9
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10
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11 class FTFile(object):
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12 def __init__(self, fname, scale=1, dtype=None):
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13 r"""
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14 Tests:
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15 >>> f = FTFile('/data/lisa/data/nist/by_class/digits/digits_test_labels.ft')
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16 """
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17 if os.path.exists(fname):
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18 self.file = open(fname, 'rb')
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19 self.magic_t, self.elsize, _, self.dim, _ = _read_header(self.file, False)
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20 self.gz=False
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21 else:
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22 import gzip
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23 self.file = gzip.open(fname+'.gz','rb')
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24 self.magic_t, self.elsize, _, self.dim, _ = _read_header(self.file, False, True)
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25 self.gz=True
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26
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27 self.size = self.dim[0]
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28 self.scale = scale
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29 self.dtype = dtype
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30
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31 def skip(self, num):
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32 r"""
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33 Skips `num` items in the file.
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34
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35 If `num` is negative, skips size-num.
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36
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37 Tests:
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38 >>> f = FTFile('/data/lisa/data/nist/by_class/digits/digits_test_labels.ft')
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39 >>> f.size
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40 58646
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41 >>> f.elsize
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42 4
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43 >>> f.file.tell()
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44 20
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45 >>> f.skip(1000)
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46 >>> f.file.tell()
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47 4020
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48 >>> f.size
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49 57646
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50 >>> f = FTFile('/data/lisa/data/nist/by_class/digits/digits_test_labels.ft')
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51 >>> f.size
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52 58646
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53 >>> f.file.tell()
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54 20
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55 >>> f.skip(-1000)
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56 >>> f.file.tell()
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57 230604
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58 >>> f.size
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59 1000
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60 """
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61 if num < 0:
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62 num += self.size
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63 if num < 0:
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64 raise ValueError('Skipping past the start of the file')
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65 if num >= self.size:
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66 self.size = 0
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67 else:
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68 self.size -= num
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69 f_start = self.file.tell()
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70 self.file.seek(f_start + (self.elsize * _prod(self.dim[1:]) * num))
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71
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72 def read(self, num):
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73 r"""
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74 Reads `num` elements from the file and return the result as a
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75 numpy matrix. Last read is truncated.
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76
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77 Tests:
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78 >>> f = FTFile('/data/lisa/data/nist/by_class/digits/digits_test_labels.ft')
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79 >>> f.read(1)
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80 array([6], dtype=int32)
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81 >>> f.read(10)
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82 array([7, 4, 7, 5, 6, 4, 8, 0, 9, 6], dtype=int32)
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83 >>> f.skip(58630)
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84 >>> f.read(10)
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85 array([9, 2, 4, 2, 8], dtype=int32)
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86 >>> f.read(10)
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87 array([], dtype=int32)
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88 >>> f = FTFile('/data/lisa/data/nist/by_class/digits/digits_test_data.ft')
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89 >>> f.read(1)
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90 array([[0, 0, 0, ..., 0, 0, 0]], dtype=uint8)
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91 """
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92 if num > self.size:
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93 num = self.size
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94 self.dim[0] = num
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95 self.size -= num
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96 if self.gz:
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97 d = self.file.read(_prod(self.dim)*self.elsize)
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98 res = numpy.fromstring(d, dtype=self.magic_t, count=_prod(self.dim)).reshape(self.dim)
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99 else:
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100 res = numpy.fromfile(self.file, dtype=self.magic_t, count=_prod(self.dim)).reshape(self.dim)
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101 if self.dtype is not None:
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102 res = res.astype(self.dtype)
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103 if self.scale != 1:
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104 res /= self.scale
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105 return res
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106
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107 class FTSource(object):
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108 def __init__(self, file, skip=0, size=None, maxsize=None,
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109 dtype=None, scale=1):
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110 r"""
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111 Create a data source from a possible subset of a .ft file.
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112
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113 Parameters:
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114 `file` -- (string) the filename
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115 `skip` -- (int, optional) amount of examples to skip from
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116 the start of the file. If negative, skips
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117 filesize - skip.
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118 `size` -- (int, optional) truncates number of examples
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119 read (after skipping). If negative truncates to
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120 filesize - size (also after skipping).
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121 `maxsize` -- (int, optional) the maximum size of the file
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122 `dtype` -- (dtype, optional) convert the data to this
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123 dtype after reading.
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124 `scale` -- (number, optional) scale (that is divide) the
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125 data by this number (after dtype conversion, if
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126 any).
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127
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128 Tests:
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129 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft')
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130 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', size=1000)
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131 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', skip=10)
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132 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', skip=100, size=120)
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133 """
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134 self.file = file
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135 self.skip = skip
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136 self.size = size
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137 self.dtype = dtype
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138 self.scale = scale
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139 self.maxsize = maxsize
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140
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141 def open(self):
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142 r"""
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143 Returns an FTFile that corresponds to this dataset.
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144
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145 Tests:
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146 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft')
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147 >>> f = s.open()
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148 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', size=1)
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149 >>> len(s.open().read(2))
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150 1
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151 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', skip=57646)
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152 >>> s.open().size
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153 1000
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154 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', skip=57646, size=1)
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155 >>> s.open().size
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156 1
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157 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', size=-10)
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158 >>> s.open().size
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159 58636
163
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160 """
180
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161 f = FTFile(self.file, scale=self.scale, dtype=self.dtype)
163
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162 if self.skip != 0:
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163 f.skip(self.skip)
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164 if self.size is not None and self.size < f.size:
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165 if self.size < 0:
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166 f.size += self.size
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167 if f.size < 0:
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168 f.size = 0
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169 else:
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170 f.size = self.size
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171 if self.maxsize is not None and f.size > self.maxsize:
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172 f.size = self.maxsize
163
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173 return f
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174
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175 class FTData(object):
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176 r"""
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177 This is a list of FTSources.
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178 """
257
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179 def __init__(self, datafiles, labelfiles, skip=0, size=None, maxsize=None,
180
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180 inscale=1, indtype=None, outscale=1, outdtype=None):
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181 if maxsize is not None:
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182 maxsize /= len(datafiles)
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183 self.inputs = [FTSource(f, skip, size, maxsize, scale=inscale, dtype=indtype)
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184 for f in datafiles]
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185 self.outputs = [FTSource(f, skip, size, maxsize, scale=outscale, dtype=outdtype)
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186 for f in labelfiles]
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187
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188 def open_inputs(self):
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189 return [f.open() for f in self.inputs]
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190
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191 def open_outputs(self):
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192 return [f.open() for f in self.outputs]
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193
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194
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195 class FTDataSet(DataSet):
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196 def __init__(self, train_data, train_lbl, test_data, test_lbl,
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197 valid_data=None, valid_lbl=None, indtype=None, outdtype=None,
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198 inscale=1, outscale=1, maxsize=None):
163
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199 r"""
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200 Defines a DataSet from a bunch of files.
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201
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202 Parameters:
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203 `train_data` -- list of train data files
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204 `train_label` -- list of train label files (same length as `train_data`)
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205 `test_data`, `test_labels` -- same thing as train, but for
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206 test. The number of files
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207 can differ from train.
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208 `valid_data`, `valid_labels` -- same thing again for validation.
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209 (optional)
180
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210 `indtype`, `outdtype`, -- see FTSource.__init__()
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211 `inscale`, `outscale` (optional)
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212 `maxsize` -- maximum size of the set returned
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213
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214
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215 If `valid_data` and `valid_labels` are not supplied then a sample
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216 approximately equal in size to the test set is taken from the train
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217 set.
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218 """
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219 if valid_data is None:
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220 total_valid_size = sum(FTFile(td).size for td in test_data)
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221 if maxsize is not None:
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222 total_valid_size = min(total_valid_size, maxsize)
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223 valid_size = total_valid_size/len(train_data)
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224 self._train = FTData(train_data, train_lbl, size=-valid_size,
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225 inscale=inscale, outscale=outscale,
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226 indtype=indtype, outdtype=outdtype,
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227 maxsize=maxsize)
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228 self._valid = FTData(train_data, train_lbl, skip=-valid_size,
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229 inscale=inscale, outscale=outscale,
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230 indtype=indtype, outdtype=outdtype,
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231 maxsize=maxsize)
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232 else:
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233 self._train = FTData(train_data, train_lbl, maxsize=maxsize,
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234 inscale=inscale, outscale=outscale,
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235 indtype=indtype, outdtype=outdtype)
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236 self._valid = FTData(valid_data, valid_lbl, maxsize=maxsize,
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237 inscale=inscale, outscale=outscale,
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238 indtype=indtype, outdtype=outdtype)
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239 self._test = FTData(test_data, test_lbl, maxsize=maxsize,
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240 inscale=inscale, outscale=outscale,
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241 indtype=indtype, outdtype=outdtype)
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242
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243 def _return_it(self, batchsize, bufsize, ftdata):
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244 return izip(DataIterator(ftdata.open_inputs(), batchsize, bufsize),
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245 DataIterator(ftdata.open_outputs(), batchsize, bufsize))