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#!/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
>>> from pandas import DataFrame
>>> from sklearn.feature_extraction.text import CountVectorizer
>>> docs = ["You can catch more flies with honey than you can with vinegar.",
... "You can lead a horse to water, but you can't make him drink."]
>>> vect = CountVectorizer(min_df=0., max_df=1.0)
>>> X = vect.fit_transform(docs)
>>> print(DataFrame(X.A, columns=vect.get_feature_names()).to_string())
but can catch drink flies him honey horse lead make more than to vinegar water with you
0 0 2 1 0 1 0 1 0 0 0 1 1 0 1 0 2 2
1 1 2 0 1 0 1 0 1 1 1 0 0 1 0 1 0 2
  1. General Background and Overview
#!/usr/bin/env python
import numpy as np
def medfilt (x, k):
"""Apply a length-k median filter to a 1D array x.
Boundaries are extended by repeating endpoints.
"""
assert k % 2 == 1, "Median filter length must be odd."
import numpy as np
from math import pi, log
import pylab
from scipy import fft, ifft
from scipy.optimize import curve_fit
i = 10000
x = np.linspace(0, 3.5 * pi, i)
y = (0.3*np.sin(x) + np.sin(1.3 * x) + 0.9 * np.sin(4.2 * x) + 0.06 *
np.random.randn(i))
def plot_correlogram(df,figsize=(20,20)):
''' Creat an n x n matrix of scatter plots for every
combination of numeric columns in a dataframe'''
cols = list(df.columns[df.dtypes=='float64'])
n = len(cols)
fig, ax = plt.subplots(n,n,figsize=figsize)
for i,y in enumerate(cols):
for j,x in enumerate(cols):
if i != n-1:
#!flask/bin/python
from flask import Flask, jsonify, abort, request, make_response, url_for
from flask.ext.httpauth import HTTPBasicAuth
app = Flask(__name__, static_url_path = "")
auth = HTTPBasicAuth()
@auth.get_password
def get_password(username):
if username == 'miguel':
@ahurriyetoglu
ahurriyetoglu / rank_metrics.py
Created December 10, 2015 06:04 — forked from bwhite/rank_metrics.py
Ranking Metrics
"""Information Retrieval metrics
Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np
"Ecrin"
"Eymen"
"Ceylin"
"Ebrar"
"Tuana"
"Esila"
"Esra"
"Enes"
"Talha"
"Ömer"