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# Philipp GayretSkPhilipp

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Created Mar 27, 2019
docker-clean.sh
View docker-clean.sh
 #!/bin/bash docker ps -a -q | xargs docker kill docker ps -a -q | xargs docker rm docker network prune -f docker system prune -f docker volume prune -f
Created Apr 29, 2017
polynomial-regression-predict.py
View polynomial-regression-predict.py
 # returns array([[ 154.98253014], # [ 249.58103463]]) model.predict([[28], [30]])
Last active Nov 13, 2017
polynomial-regression.py
View polynomial-regression.py
 from sklearn.linear_model import Ridge from sklearn.preprocessing import PolynomialFeatures from sklearn.pipeline import make_pipeline for n in [2, 5]: # Create a model which includes a polynomial to the N-th degree model = make_pipeline(PolynomialFeatures(n), Ridge()) # Train the model using the rolls and prices defined before model.fit(rolls[:, np.newaxis], prices) # Plot out what the model predicts
Created Apr 29, 2017
linear-regression-predict.py
View linear-regression-predict.py
 # returns array([[ 175.61018994], # [ 195.60932458]]) model.predict([[28], [30]])
Last active Nov 13, 2017
View linear-regression-plot.py
 from matplotlib import pyplot import numpy as np from sklearn import linear_model # Taste of Hate rolls, from highest roll to lowest. First 99 entries. rolls = np.array([30,30,30,30,30,30,30,29,29,29,29,29,29,29,29,29,29,28,28,28,28,28,28,28,28,28,28,28,27,27,27,27,27,27,26,26,26,26,26,26,25,25,25,25,25,24,24,24,24,24,24,24,24,24,23,23,23,23,23,23,23,23,23,23,23,23,23,23,22,22,22,22,22,22,21,21,21,20,20,20]) prices = np.array([233,186,200,372,233,233,372,150,150,214,165,170,150,200,145,155,200,186,165,186,186,186,145,140,140,232.5,110,135,140,139,139,139.5,139.5,140,150,115,186,120,110,140,110,120,125,130,115,279,116,110,110,120,140,120,115,110,120,186,114,104,186,110,110,105,93,114,115,130,145,120,130,186,99,112,110,110,135,186,118,110,110,115]) # Plot out all the rolls, prices and our prediction pyplot.scatter(rolls, prices)
Last active Nov 13, 2017
View linear-regression.py
 from sklearn import linear_model # Create train a linear regression model model = linear_model.LinearRegression() model.fit(rolls[:, np.newaxis], prices) # Plot out all the rolls, prices pyplot.scatter(rolls, prices) pyplot.xlabel('roll') pyplot.ylabel('price')
Created Apr 23, 2017
View linear-regression-dependencies.sh
 pip3 install numpy scipy scikit-learn matplotlib
Last active Nov 13, 2017
My configuration for a hosted Jupyter Notebook
View install.sh
 apt -y install python-pip pip install jupyterlab useradd -m jupyterhost su - jupyterhost mkdir notebooks cat > .jupyter/jupyter_notebook_config.py << EOF c.NotebookApp.open_browser = False c.NotebookApp.token = '' # Format "::", with salt appeneded to password. The example represents password "memes". c.NotebookApp.password = u'sha1:faeeb4164638:80b264dcc5d723961c5f77a5b7efa20544116c0b'
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