mfurquimdev

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

mfurquimdev

This post examines the features of [R Markdown](http://www.rstudio.org/docs/authoring/using_markdown) 
using [knitr](http://yihui.name/knitr/) in Rstudio 0.96.
This combination of tools provides an exciting improvement in usability for 
[reproducible analysis](http://stats.stackexchange.com/a/15006/183).
Specifically, this post 
(1) discusses getting started with R Markdown and `knitr` in  Rstudio 0.96;
(2) provides a basic example of producing console output and plots using R Markdown;
(3) highlights several code chunk options such as caching and controlling how input and output is displayed;
(4) demonstrates use of standard Markdown notation as well as the extended features of formulas and tables; and 
(5) discusses the implications of R Markdown.

mfurquimdev

# This file defines a decorator '@log_to()' that logs every call to a
# function, along with the arguments that function was called with. It
# takes a logging function, which is any function that accepts a
# string and does something with it. A good choice is the debug
# function from the logging module. A second decorator '@logdebug' is
# provided that uses 'logging.debug' as the logger.

from __future__ import print_function
from functools import wraps
from inspect import getcallargs, getargspec

mfurquimdev

"""
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)

mfurquimdev

from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
from sklearn.datasets import make_classification
from sklearn.decomposition import PCA
from sklearn.manifold import LocallyLinearEmbedding
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from gooey import Gooey