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amueller / colormap_extraction.py
Last active Apr 27, 2016
extract colormap from an image
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from colorspacious import cspace_convert
from scipy.sparse.csgraph import minimum_spanning_tree
from sklearn.metrics import euclidean_distances
import scipy.sparse as sp
from colorspacious import cspace_convert
from scipy.sparse.csgraph import minimum_spanning_tree
from sklearn.metrics import euclidean_distances
import scipy.sparse as sp
@amueller
amueller / knn_imputation_speed.ipynb
Created Aug 25, 2015
np.multiply test for knn imputation
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amueller / magic_constructor_estimator.ipynb
Created Apr 14, 2015
No more double underscores in sklearn.
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amueller / elkan_bench.py
Last active Aug 29, 2015
benching elkan k-means implementation
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from sklearn.cluster import KMeans
from time import time
from sklearn.datasets import load_digits, fetch_mldata, load_iris, fetch_20newsgroups_vectorized
def bench_kmeans(data, n_clusters=5, init='random', n_init=1):
start = time()
km1 = KMeans(algorithm='lloyd', n_clusters=n_clusters, random_state=0, init=init, n_init=n_init).fit(X)
print("lloyd time: %f inertia: %f" % (time() - start, km1.inertia_))
start = time()
km2 = KMeans(algorithm='elkan', n_clusters=n_clusters, random_state=0, init=init, n_init=n_init).fit(X)
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amueller / sklearn_tutorial_draft.rst
Last active Aug 29, 2015
scipy scikit-learn tutorial draft
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Tutorial Topic

This tutorial aims to provide an introduction to machine learning and scikit-learn "from the ground up". We will start with basic concepts of machine learning and implementing these using scikit-learn. Going in detail through the characteristics of several methods, we will discuss how to pick an algorithm for your application, how to set its parameters, and how to evaluate performance.

Please provide a more detailed abstract of your tutorial (again, see last years tutorials).

Machine learning is the task of extracting knowledge from data, often with the goal to generalize to new, unseen data. Applications of machine learning now touch nearly every aspect of everyday life, from the face detection in our

View scipy_interpolation_weirdness.ipnb
{
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"nbformat_minor": 0,
"worksheets": [
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View scipy_interpolation_weirdness.ipnb
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
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amueller / shuffle_once.ipynb
Created Dec 11, 2014
Shuffle once benchmarks
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amueller / reassignment_strageties.ipynb
Created Dec 24, 2013
Benchmarking differente minibatch reassignment stategies.
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