mblondel

"""
(C) Mathieu Blondel - 2010
License: BSD 3 clause

Implementation of the collapsed Gibbs sampler for
Latent Dirichlet Allocation, as described in

Finding scientifc topics (Griffiths and Steyvers)
"""

mlent

    var parsetree = function(selector, options) {
        this.el = document.querySelector(selector);
        this.options = options || {};
 
        if (this.el == null)
            console.log("Could not find DOM object");
 
        return this;
    };
 

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

stephenturner

Installing gcc 4.8 and Linuxbrew on CentOS 6

The GCC distributed with CentOS 6 is 4.4.7, which is pretty outdated. I'd like to use gcc 4.8+. Also, when trying to install Linuxbrew you run into a dependency loop where Homebrew's gcc depends on zlib, which depends on gcc. Here's how I solved the problem.

Note: Requires sudo privileges.

Resources:

• http://superuser.com/a/676337/88393: Forum response on using CERN's open Scientific Linux distribution of RHEL's developer toolset.
• http://linux.web.cern.ch/linux/devtoolset/: CERN's developer toolset installation instructions.

dirko

# Keras==1.0.6
import numpy as np
from keras.models import Sequential
from keras.layers.recurrent import LSTM
from keras.layers.core import TimeDistributedDense, Activation
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from sklearn.cross_validation import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, precision_recall_fscore_support

dirko

# Keras==1.0.6
from keras.models import Sequential
import numpy as np
from keras.layers.recurrent import LSTM
from keras.layers.core import TimeDistributedDense, Activation
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from sklearn.cross_validation import train_test_split
from keras.layers import Merge
from keras.backend import tf

cbaziotis

def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':