harahu

import gym, random

LEARNING_RATE = 0.1
DISCOUNT = 0.99

class qTable:
    """
    Implements a table tracking the estimated values
    for state action pairs in an MDP.
    """

artsobolev

Note: I'm updating this gist as I encounter new reviews, so make sure you're reading the latest revision!

Just as the previous year I collected (and keep doing so) links to various summaries and takeaways from this year's NIPS.

• NIPS 2016 Symposium on People and machines: Public views on machine learning, and what this means for machine learning researchers. (Notes and panel discussion) by /u/gcr
• NIPS 2016 summary, wrap up, and links to slides by /u/beamsearch
• Post NIPS Reflections by Neil Lawrence
• [Some general take aways from #NIPS2016](https://medium.com/@IgorCarron/some-general-take-aways-from-ni

sbos

module HMM

using Distributions

import Distributions.rand
import Distributions.fit

immutable HiddenMarkovModel{TP, K}
    theta::Vector{TP}
    A::Matrix{Float64}

superMDguy

train.visit_date = pd.to_datetime(train.visit_date)
test.visit_date = pd.to_datetime(test.visit_date)

def add_weather(dataset):                                                                                                                     
    print('Adding weather...')                                                                                                                
    air_nearest = pd.read_csv(                                                                                                                
        '../../data/raw/weather/air_store_info_with_nearest_active_station.csv')                                                              
    unique_air_store_ids = list(dataset.air_store_id.unique())                                                                                
                                                                                                                                              
    weather_dir = '../../data/raw/weather/1-1-16_5-31-17_Weathe

ceshine

#!/usr/bin/env python
# sample: Output lines from stdin to stdout with a given probability,
# for a given duration, and with a given delay between lines.
#
# Example usage: seq 100 | sample -r 20% -d 1000
#
# Dependency: Python 2.5
#
# Original Author: http://jeroenjanssens.com
# Original Script: https://github.com/jeroenjanssens/data-science-at-the-command-line/blob/master/tools/sample

shgidi

#https://gist.github.com/stared/dfb4dfaf6d9a8501cd1cc8b8cb806d2e
class PlotLosses(keras.callbacks.Callback):
    
    def __init__(self,imgs):
        super(PlotLosses, self).__init__()
        self.imgs=imgs
    
    def on_train_begin(self, logs={}):
        self.i = 0
        self.x = []

szilard

data.table vs SparkR

group-by aggregate on 100M records (1M groups)


data.table 6.5 sec (without key) / 1.3 sec (with key) - all 1 core

SparkR cached 200 sec (8 cores)
30x / 150x   ( 240x / 1200x per core)

ceshine

How To Develop Python Programs in Visual Studio Code

Prerequisites

You have to already have these in your system:

• Python. Version 3.6+ is highly recommended.
  • Official CPython from python.org
  • Anaconda

• (Recommended) Miniconda

ceshine

ceshine