Rochachan RochaC

## Q-Table Learning-Clean.ipynb

      
              1 file
            
          
              0 forks
            
          
                0 comments
              
            
              0 stars
            
          
                RochaC
                / Q-Table Learning-Clean.ipynb
            
            
              Created
              August 27, 2017 11:18
                — forked from awjuliani/Q-Table Learning-Clean.ipynb
            
              
                Q-Table learning in OpenAI grid world.
              
          
      Loading

      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## gpu-add.sh
#!/bin/bash

# install CUDA Toolkit v8.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))
CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-get update
sudo apt-get -y install cuda

## featureHash.py
from sklearn.feature_extraction import FeatureHasher
from scipy.sparse import csr_matrix,hstack
from sklearn.base import BaseEstimator, TransformerMixin

# # hasher class
class Hasher(BaseEstimator, TransformerMixin):
    def __init__(self,hashMap=None):
        self.hashMap = hashMap

    def fit(self,X):

## 0_reuse_code.js
// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console
	#!/bin/bash

	# install CUDA Toolkit v8.0
	# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))
	CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
	wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
	sudo dpkg -i ${CUDA_REPO_PKG}
	sudo apt-get update
	sudo apt-get -y install cuda
	from sklearn.feature_extraction import FeatureHasher
	from scipy.sparse import csr_matrix,hstack
	from sklearn.base import BaseEstimator, TransformerMixin

	# # hasher class
	class Hasher(BaseEstimator, TransformerMixin):
	def __init__(self,hashMap=None):
	self.hashMap = hashMap

	def fit(self,X):
	// Use Gists to store code you would like to remember later on
	console.log(window); // log the "window" object to the console