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import random
import sys

import noise
import numpy as np
import matplotlib.pyplot

if "--seed" in sys.argv:
    try:
        seed = int(sys.argv[sys.argv.index("--seed")+1])

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import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from multiprocessing import Pool
from scipy.stats import t
from tqdm import tqdm

class FracND:
    class Worker:
        def __call__(self, window):

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# how to run this thingy
# create a file on your mac called setup.sh
# run it from terminal with: sh setup.sh

# heavily inspired by https://twitter.com/damcclean
# https://github.com/damcclean/dotfiles/blob/master/install.sh

# faster dock hiding/showing (run in terminal)
# defaults write com.apple.dock autohide-delay -float 0; defaults write com.apple.dock autohide-time-modifier -int 0;killall Dock

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'''
A workflow for classifying Landsat remote sensing imagery 
involves utilizing soft voting with SVM and Gradient Boosting classifiers. 
The Isolation Forest algorithm is applied to identify outliers in the 
training data. Feature selection is performed through a Recursive Feature 
Elimination approach. The outcome consists of classification and classification 
probability raster images saved in TIF format.

Author: Fabian Löw
Date: June 2023

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This page is a curated collection of Jupyter/IPython notebooks that are notable for some reason. Feel free to add new content here, but please try to only include links to notebooks that include interesting visual or technical content; this should not simply be a dump of a Google search on every ipynb file out there.

Important contribution instructions: If you add new content, please ensure that for any notebook you link to, the link is to the rendered version using nbviewer, rather than the raw file. Simply paste the notebook URL in the nbviewer box and copy the resulting URL of the rendered version. This will make it much easier for visitors to be able to immediately access the new content.

Note that Matt Davis has conveniently written a set of bookmarklets and extensions to make it a one-click affair to load a Notebook URL into your browser of choice, directly opening into nbviewer.

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'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats