def zero_one_normal(tar=None, out=None, ref=None):
'''
tar: the target file for zero one normalization
out: the output file after zero one normalization
ref: the reference file. If reference file is 'None',
then zero one normalization will be done based on
target file itself.
'''
if ref=='None':
tar_data=PCLfile(tar, skip_col = 0)
tar_data.zero_one_normalization()
tar_data.write_pcl(out)
else:
ref_data = PCLfile(ref, skip_col=0)
tar_data = PCLfile(tar, skip_col=0)
for i in xrange(ref_data.data_matrix.shape[0]):
row_minimum = ref_data.data_matrix[i,:].min()
row_maximum = ref_data.data_matrix[i,:].max()
row_range = row_maximum - row_minimum
tar_data.data_matrix[i,:] = (tar_data.data_matrix[i,:] - row_minimum)/row_range
#bound the values to be between 0 and 1
tar_data.data_matrix[i,:] = [0 if x< 0 else x for x in tar_data.data_matrix[i,:]]
tar_data.data_matrix[i,:] = [1 if x> 1 else x for x in tar_data.data_matrix[i,:]]
tar_data.write_pcl(out)
def zero_one_normal(tar=None, out=None, ref=None):
'''
tar: the target file for zero one normalization
out: the output file after zero one normalization
ref: the reference file. If reference file is 'None',
then zero one normalization will be done based on
target file itself.
'''
if ref == 'None':
tar_data = PCLfile(tar, skip_col=0)
tar_data.zero_one_normalization()
tar_data.write_pcl(out)
else:
ref_data = PCLfile(ref, skip_col=0)
tar_data = PCLfile(tar, skip_col=0)
for i in xrange(ref_data.data_matrix.shape[0]):
row_minimum = ref_data.data_matrix[i, :].min()
row_maximum = ref_data.data_matrix[i, :].max()
row_range = row_maximum - row_minimum
tar_data.data_matrix[i, :] =\
(tar_data.data_matrix[i, :] - row_minimum)/row_range
# bound the values to be between 0 and 1
tar_data.data_matrix[i, :] =\
[0 if x < 0 else x for x in tar_data.data_matrix[i, :]]
tar_data.data_matrix[i, :] =\
[1 if x > 1 else x for x in tar_data.data_matrix[i, :]]
tar_data.write_pcl(out)
