rasbt

" Sebastian Raschka
" 09/11/2013
"

syntax on
set nonumber
set ruler

set tabstop=4
set shiftwidth=4 " controls the depth of autoindentation

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import nltk

def eng_ratio(text):
    ''' Returns the ratio of non-English to English words from a text '''

    english_vocab = set(w.lower() for w in nltk.corpus.words.words()) 
    text_vocab = set(w.lower() for w in text.split() if w.lower().isalpha()) 
    unusual = text_vocab.difference(english_vocab)
    diff = len(unusual)/len(text_vocab)
    return diff

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from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn.linear_model import LogisticRegression

def plot_decision_regions(X, y, classifier, resolution=0.1):

    # setup marker generator and color map
    markers = ('s', 'x', 'o', '^', 'v')

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#!/usr/bin/env python
"""
simple example script for running and testing notebooks.

Usage: `ipnbdoctest.py foo.ipynb [bar.ipynb [...]]`

Each cell is submitted to the kernel, and the outputs are compared with those stored in the notebook.
"""
# License: Public Domain, but credit is nice (Min RK).

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import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader


if torch.cuda.is_available():

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name: base
channels:
  - conda-forge
dependencies:
  - absl-py=1.0.0=pyhd8ed1ab_0
  - aiohttp=3.8.1=py39h5161555_0
  - aiosignal=1.2.0=pyhd8ed1ab_0
  - anyio=3.5.0=py39h2804cbe_0
  - appnope=0.1.2=py39h2804cbe_2
  - argh=0.26.2=pyh9f0ad1d_1002

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### EYECANDY ###
autoload -U colors && colors
PS1="%{$fg[red]%}%n%{$reset_color%}@%{$fg[blue]%}%m %{$fg[yellow]%}%~ %{$reset_color%}%% "
export CLICOLOR=1
#export LSCOLORS=ExFxBxDxCxegedabagacad
#alias ls='ls -GFh'
### END

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import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.calibration import CalibratedClassifierCV


X, y = make_classification(
    n_samples=1000, n_features=5, n_redundant=2, 
    n_clusters_per_class=1, random_state=123)

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# Sebastian Raschka 09/24/2022
# Create a new conda environment and packages
#   conda create -n whisper python=3.9
#   conda activate whisper
#   conda install mlxtend -c conda-forge

# Install ffmpeg
# macOS & homebrew
#   brew install ffmpeg
# Ubuntu