Manuel mlagunas

## hdrihaven_download_hdris.py
# FIRST do: pip install beautifulsoup4 tqdm requests fake-useragent urllib3
#

import requests
import os
from sys import argv
from bs4 import BeautifulSoup
from urllib.parse import urlparse
from urllib.request import urlretrieve
from urllib.request import URLopener

## compile_mistuba.sh
# go to the dir where you will download the source files
cd ...

# download the source files
git clone https://github.com/mitsuba-renderer/mitsuba

# go into the mitsuba folder
cd mitsuba

# copy the config file to build

## lz4.py
import lz4.block
import numpy as np

__header_sample = {
    'desc': b'LANT_IMG_CODING\x00',
    'width': np.array(1024, dtype=np.uint32),  # uint32_t
    'height': np.array(1024, dtype=np.uint32),  # uint32_t
    'row_stride_in_byte': np.array(4096, dtype=np.uint32),  # uint32_t
    'pixel_format': np.array(2, dtype=np.uint32),  # pixel_format
    'color_space': np.array(0, dtype=np.uint32),  # color_space

## ejercicio_corto.ipynb

      
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                mlagunas
                / ejercicio_corto.ipynb
            
            
              Created Feb 18, 2018
            
          
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## SGD_comparison.ipynb

      
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                mlagunas
                / SGD_comparison.ipynb
            
            
              Last active Jul 25, 2017
            
              
                Comparison between SGD using L2 regularization, standard momentum, Nesterov momentum and ADAM 
              
          
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	# FIRST do: pip install beautifulsoup4 tqdm requests fake-useragent urllib3
	#

	import requests
	import os
	from sys import argv
	from bs4 import BeautifulSoup
	from urllib.parse import urlparse
	from urllib.request import urlretrieve
	from urllib.request import URLopener
	# go to the dir where you will download the source files
	cd ...

	# download the source files
	git clone https://github.com/mitsuba-renderer/mitsuba

	# go into the mitsuba folder
	cd mitsuba

	# copy the config file to build
	import lz4.block
	import numpy as np

	__header_sample = {
	'desc': b'LANT_IMG_CODING\x00',
	'width': np.array(1024, dtype=np.uint32), # uint32_t
	'height': np.array(1024, dtype=np.uint32), # uint32_t
	'row_stride_in_byte': np.array(4096, dtype=np.uint32), # uint32_t
	'pixel_format': np.array(2, dtype=np.uint32), # pixel_format
	'color_space': np.array(0, dtype=np.uint32), # color_space