Mercurial > ift6266
annotate datasets/ftfile.py @ 599:587674030c5a
added authors and emails list
author | boulanni <nicolas_boulanger@hotmail.com> |
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date | Fri, 15 Oct 2010 14:14:06 -0400 |
parents | a92ec9939e4f |
children | 5e481b224117 |
rev | line source |
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1 from pylearn.io.filetensor import _read_header, _prod |
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2 import numpy, theano |
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3 from dataset import DataSet |
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4 from dsetiter import DataIterator |
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5 from itertools import izip, imap |
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6 |
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7 class FTFile(object): |
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8 def __init__(self, fname, scale=1, dtype=None): |
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9 r""" |
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10 Tests: |
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11 >>> f = FTFile('/data/lisa/data/nist/by_class/digits/digits_test_labels.ft') |
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12 """ |
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13 self.file = open(fname, 'rb') |
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14 self.magic_t, self.elsize, _, self.dim, _ = _read_header(self.file, False) |
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15 self.size = self.dim[0] |
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16 self.scale = scale |
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17 self.dtype = dtype |
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18 |
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19 def skip(self, num): |
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20 r""" |
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21 Skips `num` items in the file. |
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22 |
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23 If `num` is negative, skips size-num. |
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24 |
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25 Tests: |
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26 >>> f = FTFile('/data/lisa/data/nist/by_class/digits/digits_test_labels.ft') |
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27 >>> f.size |
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28 58646 |
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29 >>> f.elsize |
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30 4 |
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31 >>> f.file.tell() |
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32 20 |
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33 >>> f.skip(1000) |
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34 >>> f.file.tell() |
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35 4020 |
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36 >>> f.size |
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37 57646 |
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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.file.tell() |
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42 20 |
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43 >>> f.skip(-1000) |
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44 >>> f.file.tell() |
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45 230604 |
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46 >>> f.size |
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47 1000 |
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48 """ |
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49 if num < 0: |
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50 num += self.size |
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51 if num < 0: |
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52 raise ValueError('Skipping past the start of the file') |
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53 if num >= self.size: |
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54 self.size = 0 |
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55 else: |
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56 self.size -= num |
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57 f_start = self.file.tell() |
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58 self.file.seek(f_start + (self.elsize * _prod(self.dim[1:]) * num)) |
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59 |
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60 def read(self, num): |
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61 r""" |
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62 Reads `num` elements from the file and return the result as a |
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63 numpy matrix. Last read is truncated. |
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64 |
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65 Tests: |
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66 >>> f = FTFile('/data/lisa/data/nist/by_class/digits/digits_test_labels.ft') |
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67 >>> f.read(1) |
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68 array([6], dtype=int32) |
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69 >>> f.read(10) |
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70 array([7, 4, 7, 5, 6, 4, 8, 0, 9, 6], dtype=int32) |
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71 >>> f.skip(58630) |
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72 >>> f.read(10) |
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73 array([9, 2, 4, 2, 8], dtype=int32) |
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74 >>> f.read(10) |
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75 array([], dtype=int32) |
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76 >>> f = FTFile('/data/lisa/data/nist/by_class/digits/digits_test_data.ft') |
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77 >>> f.read(1) |
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78 array([[0, 0, 0, ..., 0, 0, 0]], dtype=uint8) |
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79 """ |
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80 if num > self.size: |
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81 num = self.size |
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82 self.dim[0] = num |
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83 self.size -= num |
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84 res = numpy.fromfile(self.file, dtype=self.magic_t, count=_prod(self.dim)).reshape(self.dim) |
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85 if self.dtype is not None: |
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86 res = res.astype(self.dtype) |
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87 if self.scale != 1: |
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88 res /= self.scale |
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89 return res |
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90 |
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91 class FTSource(object): |
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92 def __init__(self, file, skip=0, size=None, maxsize=None, |
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93 dtype=None, scale=1): |
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94 r""" |
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95 Create a data source from a possible subset of a .ft file. |
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96 |
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97 Parameters: |
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98 `file` -- (string) the filename |
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99 `skip` -- (int, optional) amount of examples to skip from |
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100 the start of the file. If negative, skips |
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101 filesize - skip. |
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102 `size` -- (int, optional) truncates number of examples |
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103 read (after skipping). If negative truncates to |
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104 filesize - size (also after skipping). |
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105 `maxsize` -- (int, optional) the maximum size of the file |
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106 `dtype` -- (dtype, optional) convert the data to this |
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107 dtype after reading. |
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108 `scale` -- (number, optional) scale (that is divide) the |
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109 data by this number (after dtype conversion, if |
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110 any). |
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111 |
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112 Tests: |
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113 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft') |
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114 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', size=1000) |
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115 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', skip=10) |
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116 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', skip=100, size=120) |
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117 """ |
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118 self.file = file |
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119 self.skip = skip |
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120 self.size = size |
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121 self.dtype = dtype |
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122 self.scale = scale |
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123 self.maxsize = maxsize |
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124 |
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125 def open(self): |
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126 r""" |
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127 Returns an FTFile that corresponds to this dataset. |
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128 |
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129 Tests: |
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130 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft') |
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131 >>> f = s.open() |
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132 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', size=1) |
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133 >>> len(s.open().read(2)) |
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134 1 |
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135 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', skip=57646) |
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136 >>> s.open().size |
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137 1000 |
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138 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', skip=57646, size=1) |
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139 >>> s.open().size |
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140 1 |
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141 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', size=-10) |
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142 >>> s.open().size |
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143 58636 |
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144 """ |
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145 f = FTFile(self.file, scale=self.scale, dtype=self.dtype) |
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146 if self.skip != 0: |
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147 f.skip(self.skip) |
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148 if self.size is not None and self.size < f.size: |
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149 if self.size < 0: |
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150 f.size += self.size |
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151 if f.size < 0: |
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152 f.size = 0 |
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153 else: |
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154 f.size = self.size |
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155 if self.maxsize is not None and f.size > self.maxsize: |
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156 f.size = self.maxsize |
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157 return f |
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158 |
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159 class FTData(object): |
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160 r""" |
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161 This is a list of FTSources. |
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162 """ |
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163 def __init__(self, datafiles, labelfiles, skip=0, size=None, maxsize=None, |
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164 inscale=1, indtype=None, outscale=1, outdtype=None): |
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165 if maxsize is not None: |
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166 maxsize /= len(datafiles) |
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167 self.inputs = [FTSource(f, skip, size, maxsize, scale=inscale, dtype=indtype) |
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168 for f in datafiles] |
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169 self.outputs = [FTSource(f, skip, size, maxsize, scale=outscale, dtype=outdtype) |
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170 for f in labelfiles] |
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171 |
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172 def open_inputs(self): |
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173 return [f.open() for f in self.inputs] |
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174 |
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175 def open_outputs(self): |
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176 return [f.open() for f in self.outputs] |
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177 |
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178 |
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179 class FTDataSet(DataSet): |
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180 def __init__(self, train_data, train_lbl, test_data, test_lbl, |
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181 valid_data=None, valid_lbl=None, indtype=None, outdtype=None, |
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182 inscale=1, outscale=1, maxsize=None): |
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183 r""" |
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184 Defines a DataSet from a bunch of files. |
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185 |
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186 Parameters: |
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187 `train_data` -- list of train data files |
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188 `train_label` -- list of train label files (same length as `train_data`) |
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189 `test_data`, `test_labels` -- same thing as train, but for |
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190 test. The number of files |
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191 can differ from train. |
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192 `valid_data`, `valid_labels` -- same thing again for validation. |
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193 (optional) |
180
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194 `indtype`, `outdtype`, -- see FTSource.__init__() |
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195 `inscale`, `outscale` (optional) |
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196 `maxsize` -- maximum size of the set returned |
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197 |
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198 |
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199 If `valid_data` and `valid_labels` are not supplied then a sample |
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200 approximately equal in size to the test set is taken from the train |
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201 set. |
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202 """ |
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203 if valid_data is None: |
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204 total_valid_size = sum(FTFile(td).size for td in test_data) |
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205 if maxsize is not None: |
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206 total_valid_size = min(total_valid_size, maxsize) |
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207 valid_size = total_valid_size/len(train_data) |
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208 self._train = FTData(train_data, train_lbl, size=-valid_size, |
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209 inscale=inscale, outscale=outscale, |
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210 indtype=indtype, outdtype=outdtype, |
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211 maxsize=maxsize) |
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212 self._valid = FTData(train_data, train_lbl, skip=-valid_size, |
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213 inscale=inscale, outscale=outscale, |
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214 indtype=indtype, outdtype=outdtype, |
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215 maxsize=maxsize) |
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216 else: |
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217 self._train = FTData(train_data, train_lbl, maxsize=maxsize, |
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218 inscale=inscale, outscale=outscale, |
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219 indtype=indtype, outdtype=outdtype) |
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220 self._valid = FTData(valid_data, valid_lbl, maxsize=maxsize, |
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221 inscale=inscale, outscale=outscale, |
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222 indtype=indtype, outdtype=outdtype) |
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223 self._test = FTData(test_data, test_lbl, maxsize=maxsize, |
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224 inscale=inscale, outscale=outscale, |
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225 indtype=indtype, outdtype=outdtype) |
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226 |
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227 def _return_it(self, batchsize, bufsize, ftdata): |
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228 return izip(DataIterator(ftdata.open_inputs(), batchsize, bufsize), |
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229 DataIterator(ftdata.open_outputs(), batchsize, bufsize)) |