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)

souldeux

def determine_feature_importance(df):
	#Determines the importance of individual features within a dataframe
	#Grab header for all feature values excluding score & ids
	features_list = df.columns.values[4::]
	print "Features List: \n", features_list
	
	#set X equal to all feature values, excluding Score & ID fields
	X = df.values[:,4::]
	
	#set y equal to all Score values

balzer82

jkleint

#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

thomasjungblut

from bayes_opt import BayesianOptimization
from sklearn.cross_validation import KFold
import xgboost as xgb

def xgbCv(train, features, numRounds, eta, gamma, maxDepth, minChildWeight, subsample, colSample):   
   # prepare xgb parameters 
   params = {
            "objective": "reg:linear",
            "booster" : "gbtree",
            "eval_metric": "mae",            

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':

maximus009

from keras.layers import Lambda
from keras import backend as K
from numpy import newaxis
from keras.models import Model, Input


def outer_product(inputs):
    """
    inputs: list of two tensors (of equal dimensions, 
        for which you need to compute the outer product

ragulpr

def load_challenge_data(df,start_at,truncate_at):
    seq_len = np.max([truncate_at,df[:,1].max().astype(int)+1])    
    n_vars = df.shape[1]-2 # Drop unit_number and time
    n_series = int(df[:,0].max())

    feature_data   = np.zeros([seq_len,n_series,n_vars])
    times_to_event = np.zeros([seq_len,n_series,1])
    seq_lengths    = np.zeros([n_series])
    mask           = np.ones([seq_len,n_series,1])

nigeljyng

class AttentionWithContext(Layer):
    """
    Attention operation, with a context/query vector, for temporal data.
    Supports Masking.
    Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf]
    "Hierarchical Attention Networks for Document Classification"
    by using a context vector to assist the attention
    # Input shape
        3D tensor with shape: `(samples, steps, features)`.
    # Output shape

chretm

#author : Sujit Pal 

#Note: this is a python3 updated version of http://sujitpal.blogspot.fr/2014/12/semantic-similarity-for-short-sentences.html 
# by mathieu Chrétien ([email protected])

#contributor : Mathieu Chrétien

from __future__ import division
import nltk
from nltk.corpus import wordnet as wn