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Mateus Furquim mfurquimdev

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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
@mfurquimdev
mfurquimdev / min-char-rnn.py
Created November 19, 2021 01:08 — forked from karpathy/min-char-rnn.py
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
"""
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
mfurquimdev / argprint.py
Created September 10, 2021 13:07 — forked from DarwinAwardWinner/argprint.py
Print a Python function's arguments every time it is called
# 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
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
mfurquimdev / 0_reuse_code.js
Created September 26, 2013 17:12
Here are some things you can do with Gists in GistBox.
// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console