Roll your own iPython Notebook server with Amazon Web Services (EC2) using their Free Tier.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
from sklearn.base import BaseEstimator, TransformerMixin | |
from sklearn.metrics.pairwise import rbf_kernel | |
class KMeansTransformer(BaseEstimator, TransformerMixin): | |
def __init__(self, centroids): | |
self.centroids = centroids | |
def fit(self, X, y=None): | |
return self |
I've been interested in computer vision for a long time, but I haven't had any free time to make any progress until this holiday season. Over Christmas and the New Years I experimented with various methodologies in OpenCV to detect road signs and other objects of interest to OpenStreetMap. After some failed experiments with thresholding and feature detection, the excellent /r/computervision suggested using the dlib C++ module because it has more consistently-good documentation and the pre-built tools are faster.
After a day or two figuring out how to compile the examples, I finally made some progress:
- Clone
dlib
from Github to your local machine:
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# clear the workspace | |
rm(list = ls()) | |
# load the relevant libraries | |
# install.packages(rCUR) | |
library(rCUR) # for CUR decomposition | |
# install.packages(irlba) | |
library(irlba) # for fast svd |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# Mathieu Blondel, September 2010 | |
# License: BSD 3 clause | |
import numpy as np | |
from numpy import linalg | |
import cvxopt | |
import cvxopt.solvers | |
def linear_kernel(x1, x2): | |
return np.dot(x1, x2) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import numpy as np | |
import matplotlib.pyplot as plt | |
from sklearn.datasets import fetch_mldata | |
from sklearn.decomposition import FastICA, PCA | |
from sklearn.cluster import KMeans | |
# fetch natural image patches | |
image_patches = fetch_mldata("natural scenes data") | |
X = image_patches.data |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
#!/usr/bin/python | |
# | |
# K-means clustering using Lloyd's algorithm in pure Python. | |
# Written by Lars Buitinck. This code is in the public domain. | |
# | |
# The main program runs the clustering algorithm on a bunch of text documents | |
# specified as command-line arguments. These documents are first converted to | |
# sparse vectors, represented as lists of (index, value) pairs. | |
from collections import defaultdict |
NewerOlder