# -*- coding: utf-8 -*-
"""
Created on Tue Sep 12 13:05:29 2017
@author: damodara
"""
import numpy as np
def zero_mean_unitvarince(data, scaling=False):
size = data.shape
data = np.float64(data)
if len(size)==2: # for the 2D data
from sklearn.preprocessing import scale
mean_data = np.mean(data,axis=0)
std_data = np.std(data, axis=0)
data = data - np.tile(mean_data, [size[0], 1])
data = data/(np.tile(std_data, [size[0],1]))
elif len(size)>2:# for the images
mean_image = np.mean(data, axis=0)
std_image = np.std(data, axis=0)
mean_size = mean_image.shape
channel_mean = np.mean(mean_image, axis=(0,1))
channel_std = np.std(data, axis=(0,1,2))
if len(mean_size)>2:
# per channel mean subtraction
for i in range(mean_size[2]):
data[:,:,:,i] = data[:,:,:,i]-channel_mean[i]
if scaling:
data[:,:,:,i] = data[:,:,:,i]/(channel_std[i]+1e-5)
else:
data = data-channel_mean
if scaling:
data = data/(channel_std+1e-5)
data[np.isnan(data)]=0
return data
def instance_zero_mean_unitvar(data, scaling =False):
size = data.shape
data = np.float64(data)
if len(size)==2: # for the 2D data
from sklearn.preprocessing import scale
mean_data = np.mean(data,axis=0)
std_data = np.std(data, axis=0)
data = data - np.tile(mean_data, [size[0], 1])
data = data/(np.tile(std_data, [size[0],1]))
elif len(size)>2:# for the images
if len(size)==4:
for i in range(size[0]):
for j in range(size[3]):
std_d = np.std(data[i,:,:,j])
data[i,:,:,j]= data[i,:,:,j]-np.mean(data[i,:,:,j])
data[i,:,:,j]= data[i,:,:,j]/(std_d+1e-5)
else:
for i in range(size[0]):
std_d = np.std(data[i,:,:])
data[i,:,:]= data[i,:,:]-np.mean(data[i,:,:])
data[i,:,:]= data[i,:,:]/(std_d+1e-5)
data[np.isnan(data)]=0
return data
def resize_data(data, resize_size):
from scipy.misc import imresize
data_type = type(data)
if data_type==list:
s= data[0].shape
ndata= len(data)
else:
s=data[0].shape
ndata = data.shape[0]
if len(s)==2:
data1=np.zeros((ndata,resize_size,resize_size))
for i in range(ndata):
data1[i] = imresize(data[i], (resize_size,resize_size))
elif len(s)==3:
data1=np.zeros((ndata,resize_size,resize_size,3))
for i in range(ndata):
data1[i] = imresize(data[i], (resize_size,resize_size))
del data
return data1
#
# if (len(size)==4):
# for i in range(size[0]):
# for j in range(size[3]):
# data[i,:,:,j]= data[i,:,:,j]-np.mean(data[i,:,:,j])
# elif (len(size)==3):
# for i in range(size[0]):
# data[i,:,:]= data[i,:,:]-np.mean(data[i,:,:])
#
def min_max_scaling(data, lowerbound_zero=False):
from sklearn.preprocessing import minmax_scale
size = data.shape
data = data/255.0
if not lowerbound_zero:
data = (data *2.0)-1.0
data[np.isnan(data)] = 0
# if (len(size)==4):
# for i in range(size[3]):
# tmp = minmax_scale(data[:,:,:,i].reshape(-1, size[1]*size[2]),
# feature_range = (s, t), axis=1)
# data[:,:,:,i] = tmp.reshape(-1,size[1],size[2])
# elif (len(size)==3):
# data = minmax_scale(data.reshape(-1, size[1]*size[2]), axis=1)
# data = data.reshape(-1, size[1],size[2])
return data
