repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
Atomahawk/flagging-suspicious-blockchain-transactions | lab_notebooks/spark-play.ipynb | mit | # from pyspark import SparkContext
from pyspark.mllib.clustering import KMeans, KMeansModel
# http://spark.apache.org/docs/2.0.0/api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayesModel
from pyspark.mllib.classification import NaiveBayesModel
# http://spark.apache.org/docs/2.0.0/api/python/pyspark.m... |
karlstroetmann/Artificial-Intelligence | Python/1 Search/A-Star-Search-Slim.ipynb | gpl-2.0 | import heapq
"""
Explanation: Improved A$^*$ First Search
The module heapq provides
priority queues
that are implemented as heaps.
Technically, these heaps are just lists. In order to use them as priority queues, the entries of these lists will be pairs of the form $(p, o)$, where $p$ is the priority of the object ... |
mohanprasath/Course-Work | coursera/python_for_data_science/2.1_Tuples.ipynb | gpl-3.0 | tuple1=("disco",10,1.2 )
tuple1
"""
Explanation: <a href="http://cocl.us/topNotebooksPython101Coursera"><img src = "https://ibm.box.com/shared/static/yfe6h4az47ktg2mm9h05wby2n7e8kei3.png" width = 750, align = "center"></a>
<a href="https://www.bigdatauniversity.com"><img src = "https://ibm.box.com/shared/static/ugcqz6... |
NYUDataBootcamp/Projects | UG_S17/Booth_Praveen_Final_Project.ipynb | mit | import pandas as pd # data package
import matplotlib.pyplot as plt # graphics
import datetime as dt # date tools, used to note current date
import requests
from bs4 import BeautifulSoup
import urllib.request
from matplotlib.offsetbox import OffsetImage
%matplotlib inline
#per game statistics... |
thehackerwithin/berkeley | code_examples/python_matplotlib/Matplotlib_THW_tutorial.ipynb | bsd-3-clause | import matplotlib as mpl
mpl
# I normally prototype my code in an editor + ipy terminal.
# In those cases I import pyplot and numpy via
import matplotlib.pyplot as plt
import numpy as np
# In Jupy notebooks we've got magic functions and pylab gives you pyplot as plt and numpy as np
# %pylab
# Additionally, inline ... |
amueller/nyu_ml_lectures | First Steps.ipynb | bsd-2-clause | from sklearn.datasets import load_digits
digits = load_digits()
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(digits.data,
digits.target)
X_train.shape
"""
Explanation: Get some data to play with
End of ex... |
sysid/nbs | LP/Introduction-to-linear-programming/LaTeX_formatted_ipynb_files/Introduction to Linear Programming with Python - Part 4.ipynb | mit | import pulp
# Instantiate our problem class
model = pulp.LpProblem("Cost minimising blending problem", pulp.LpMinimize)
"""
Explanation: Introduction to Linear Programming with Python - Part 4
Real world examples - Blending Problem
We're going to make some sausages!
We have the following ingredients available to us:
... |
quantumlib/Cirq | docs/gatezoo.ipynb | apache-2.0 | try:
import cirq
except ImportError:
print("installing cirq...")
!pip install --quiet --pre cirq
print("installed cirq.")
import IPython.display as ipd
import cirq
import inspect
def display_gates(*gates):
for gate_name in gates:
ipd.display(ipd.Markdown("---"))
gate = getattr(... |
miykael/nipype_tutorial | notebooks/basic_mapnodes.ipynb | bsd-3-clause | from nipype import Function
def square_func(x):
return x ** 2
square = Function(["x"], ["f_x"], square_func)
"""
Explanation: MapNode
If you want to iterate over a list of inputs, but need to feed all iterated outputs afterward as one input (an array) to the next node, you need to use a MapNode. A MapNode is quite... |
giacomov/3ML | docs/notebooks/Quickstart.ipynb | bsd-3-clause | from threeML import *
# Let's generate some data with y = Powerlaw(x)
gen_function = Powerlaw()
# Generate a dataset using the power law, and a
# constant 30% error
x = np.logspace(0, 2, 50)
xyl_generator = XYLike.from_function("sim_data", function = gen_function,
x = x,
... |
jakevdp/sklearn_tutorial | notebooks/04.2-Clustering-KMeans.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
plt.style.use('seaborn')
"""
Explanation: <small><i>This notebook was put together by Jake Vanderplas. Source and license info is on GitHub.</i></small>
Clustering: K-Means In-Depth
Here we'll explore K Means Clustering, whi... |
GSimas/EEL7045 | .ipynb_checkpoints/Aula 10 - Circuitos RL-checkpoint.ipynb | mit | print("Exemplo 7.3")
import numpy as np
from sympy import *
I0 = 10
L = 0.5
R1 = 2
R2 = 4
t = symbols('t')
#Determinar Req = Rth
#Io hipotético = 1 A
#Analise de Malhas
#4i2 + 2(i2 - i0) = -3i0
#6i2 = 5
#i2 = 5/6
#ix' = i2 - i1 = 5/6 - 1 = -1/6
#Vr1 = ix' * R1 = -1/6 * 2 = -1/3
#Rth =... |
armandosrz/UdacityNanoMachine | titanic-survival-exploration/titanic_survival_exploration/Titanic_Survival_Exploration.ipynb | apache-2.0 | import numpy as np
import pandas as pd
# RMS Titanic data visualization code
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# Load the dataset
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)
# Print the first few entries of the RMS Titanic data... |
scotthuang1989/Python-3-Module-of-the-Week | BeautifulSoup/Improving Performance by Parsing Only Part of the Document.ipynb | apache-2.0 | from bs4 import BeautifulSoup,SoupStrainer
import re
doc = '''Bob reports <a href="http://www.bob.com/">success</a>
with his plasma breeding <a
href="http://www.bob.com/plasma">experiments</a>. <i>Don't get any on
us, Bob!</i>
<br><br>Ever hear of annular fusion? The folks at <a
href="http://www.boogabooga.net/">Boog... |
citxx/sis-python | crash-course/slices.ipynb | mit | lst = [1, 2, 3, 4, 5, 6, 7, 8]
print(lst[::])
"""
Explanation: <h1>Содержание<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Получение-среза" data-toc-modified-id="Получение-среза-1">Получение среза</a></span><ul class="toc-item"><li><span><a href="#Без-параметров" data-toc... |
phoebe-project/phoebe2-docs | 2.3/examples/legacy_spots.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
"""
Explanation: Comparing Spots in PHOEBE 2 vs PHOEBE Legacy
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # un... |
shareactorIO/pipeline | source.ml/jupyterhub.ml/notebooks/zz_old/TensorFlow/HvassLabsTutorials/05_Ensemble_Learning.ipynb | apache-2.0 | from IPython.display import Image
Image('images/02_network_flowchart.png')
"""
Explanation: TensorFlow Tutorial #05
Ensemble Learning
by Magnus Erik Hvass Pedersen
/ GitHub / Videos on YouTube
Introduction
This tutorial shows how to use a so-called ensemble of convolutional neural networks. Instead of using a single n... |
agentzh2m/muic_class_freq | class_freq.ipynb | mit | df = pd.read_csv('t2_2016.csv')
df = df[df['Type'] == 'master']
df.head()
#format [Day, start_time, end_time]
def time_extract(s):
s = str(s).strip().split(" "*16)
def helper(s):
try:
temp = s.strip().split(" ")[1:]
comb = temp[:2] + temp[3:]
comb[0] = comb[0][1:]
... |
faneshion/MatchZoo | tutorials/data_handling.ipynb | apache-2.0 | data_pack = mz.datasets.toy.load_data()
data_pack.left.head()
data_pack.right.head()
data_pack.relation.head()
"""
Explanation: DataPack
Structure
matchzoo.DataPack is a MatchZoo native data structure that most MatchZoo data handling processes build upon. A matchzoo.DataPack consists of three parts: left, right and... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/launching_into_ml/labs/intro_logistic_regression.ipynb | apache-2.0 | import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: Introduction to Logistic Regression
Learning Objectives
Create Seaborn plots for Exploratory Data Analysis
Train a Logistic Regression Model using Scikit-Learn
Introduction
This... |
sindrerb/VecDiSCS | notebooks/Generate.ipynb | gpl-3.0 | lattice = np.array([[ 3.2871687359128612, 0.0000000000000000, 0.0000000000000000],
[-1.6435843679564306, 2.8467716318265182, 0.0000000000000000],
[ 0.0000000000000000, 0.0000000000000000, 5.3045771064003047]])
positions = [[0.3333333333333357, 0.6666666666666643, 0.99968143309... |
fcollonval/coursera_data_visualization | Analysis_Variance.ipynb | mit | # Magic command to insert the graph directly in the notebook
%matplotlib inline
# Load a useful Python libraries for handling data
import pandas as pd
import numpy as np
import statsmodels.formula.api as smf
import seaborn as sns
import matplotlib.pyplot as plt
from IPython.display import Markdown, display
# Read the ... |
ManyBodyPhysics/NuclearForces | doc/exercises/Variable_Phase_Approach.ipynb | cc0-1.0 | # I know you're not supposed to do this to avoid namespace issues, but whatever
from numpy import *
from matplotlib.pyplot import *
# Global variables for this notebook
mu=1.
R=1.
hbar=1.
"""
Explanation: Preliminaries
In this notebook, we compute the phase shifts of the potential with $V = -V0$ for $r < R$ and $V=0$... |
GoogleCloudPlatform/practical-ml-vision-book | 07_training/07c_export.ipynb | apache-2.0 | import tensorflow as tf
print('TensorFlow version' + tf.version.VERSION)
print('Built with GPU support? ' + ('Yes!' if tf.test.is_built_with_cuda() else 'Noooo!'))
print('There are {} GPUs'.format(len(tf.config.experimental.list_physical_devices("GPU"))))
device_name = tf.test.gpu_device_name()
if device_name != '/devi... |
mbeyeler/opencv-machine-learning | notebooks/06.01-Implementing-Your-First-Support-Vector-Machine.ipynb | mit | from sklearn import datasets
X, y = datasets.make_classification(n_samples=100, n_features=2,
n_redundant=0, n_classes=2,
random_state=7816)
"""
Explanation: <!--BOOK_INFORMATION-->
<a href="https://www.packtpub.com/big-data-and-business-intellige... |
vicente-gonzalez-ruiz/YAPT | scientific_computation/about_accuracy.ipynb | cc0-1.0 | x = 7**273
print(x)
print(type(x))
"""
Explanation: About arithmetic accuracy in Python
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Integers" data-toc-modified-id="Integers-1"><span class="toc-item-num">1 </span><a href="https://docs.pyth... |
dashee87/blogScripts | Jupyter/2017-12-19-charting-the-rise-of-song-collaborations-with-scrapy-and-pandas.ipynb | mit | import scrapy
import re # for text parsing
import logging
class ChartSpider(scrapy.Spider):
name = 'ukChartSpider'
# page to scrape
start_urls = ['http://www.officialcharts.com/charts/']
# if you want to impose a delay between sucessive scrapes
# download_delay = 0.5
def parse(self, response):
... |
mne-tools/mne-tools.github.io | dev/_downloads/d418deb5d74ab4363c42409de6a8e6df/label_source_activations.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inve... |
smorton2/think-stats | code/chap03soln.ipynb | gpl-3.0 | from __future__ import print_function, division
%matplotlib inline
import numpy as np
import nsfg
import first
import thinkstats2
import thinkplot
"""
Explanation: Examples and Exercises from Think Stats, 2nd Edition
http://thinkstats2.com
Copyright 2016 Allen B. Downey
MIT License: https://opensource.org/licenses/... |
spacy-io/thinc | examples/04_parallel_training_ray.ipynb | mit | # To let ray install its own version in Colab
!pip uninstall -y pyarrow
# You might need to restart the Colab runtime
!pip install --upgrade "thinc>=8.0.0a0" "ml_datasets>=0.2.0a0" ray psutil setproctitle
"""
Explanation: Parallel training with Thinc and Ray
This notebook is based off one of Ray's tutorials and shows... |
bramacchino/numberSense | Plotly-Mesh3d.ipynb.ipynb | mit | from IPython.display import HTML
HTML('<iframe src=https://plot.ly/~empet/13475/ width=850 height=350></iframe>')
"""
Explanation: Generating and Visualizing Alpha Shapes with Python Plotly
Notebook available at https://plot.ly/~notebook_demo/125/generating-and-visualizing-alpha-shapes/
Starting with a finite set of... |
tensorflow/tfx-addons | tfx_addons/schema_curation/example/taxi_example_colab.ipynb | apache-2.0 | !pip install -U tfx
x = !pwd
if 'schemacomponent' not in str(x):
!git clone https://github.com/rcrowe-google/schemacomponent
%cd schemacomponent/example
"""
Explanation: <a href="https://colab.research.google.com/github/rcrowe-google/schemacomponent/blob/Nirzari%2Ffeature%2Fexample/example/taxi_example_colab.ipy... |
sassoftware/sas-viya-programming | communities/Getting a CASTable Object from an Existing CAS Table.ipynb | apache-2.0 | import swat
conn = swat.CAS(host, port, username, password)
"""
Explanation: Getting a Python CASTable Object from an Existing CAS Table
Many of the examples in the Python series of articles here use a CASTable object to invoke actions or apply DataFrame-like syntax to CAS tables. In those examples, the CASTable obj... |
csiu/100daysofcode | misc/day85.ipynb | mit | url = "http://finance.yahoo.com/webservice/v1/symbols/allcurrencies/quote?format=json"
"""
Explanation: I want to look into stock data.
Yahoo Finance API
According to stackoverflow ("alternative to google finance api"), financial information can be obtained through the Yahoo Finance API.
For instance, you can generate... |
AtmaMani/pyChakras | python_crash_course/seaborn_cheat_sheet_2.ipynb | mit | import seaborn as sns
%matplotlib inline
tips = sns.load_dataset('tips')
tips.head()
"""
Explanation: Seaborn - categorical plotting
End of explanation
"""
sns.barplot(x='sex', y='total_bill', data=tips)
"""
Explanation: ToC
- Barplot
- Countplot
- Boxplot
- Violin plot
- Strip plot
- Swarm plot
Barplot
Barpl... |
deepcharles/ruptures | docs/examples/text-segmentation.ipynb | bsd-2-clause | from pathlib import Path
import nltk
import numpy as np
import ruptures as rpt # our package
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.tokenize import regexp_tokenize
from ruptures.base import BaseCost
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metri... |
jorisvandenbossche/DS-python-data-analysis | _solved/00-jupyter_introduction.ipynb | bsd-3-clause | from IPython.display import Image
Image(url='http://python.org/images/python-logo.gif')
"""
Explanation: <p><font size="6"><b>Jupyter notebook INTRODUCTION </b></font></p>
© 2021, Joris Van den Bossche and Stijn Van Hoey (jorisvandenbossc&... |
t-davidson/hate-speech-and-offensive-language | src/Automated Hate Speech Detection and the Problem of Offensive Language Python 3.6.ipynb | mit | import pandas as pd
import numpy as np
import pickle
import sys
from sklearn.feature_extraction.text import TfidfVectorizer
import nltk
from nltk.stem.porter import *
import string
import re
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer as VS
from textstat.textstat import *
from sklearn.linear_mo... |
zenoss/pywbem | docs/notebooks/subscriptionmanager.ipynb | lgpl-2.1 | from __future__ import print_function
import pywbem
# The WBEM server that should emit the indications
server = 'http://myserver'
username = 'user'
password = 'password'
# The URL of the WBEM listener
listener_url = 'http://mylistener'
conn = pywbem.WBEMConnection(server, (username, password),
... |
sallai/mbuild | docs/tutorials/tutorial_simple_LJ.ipynb | mit | import mbuild as mb
class MonoLJ(mb.Compound):
def __init__(self):
super(MonoLJ, self).__init__()
lj_particle1 = mb.Particle(name='LJ', pos=[0, 0, 0])
self.add(lj_particle1)
lj_particle2 = mb.Particle(name='LJ', pos=[1, 0, 0])
self.add(lj_particle2)
lj_particle3 = ... |
Alex-Ian-Hamilton/solarbextrapolation | docs/auto_examples/plot_define_and_run_trivial_analytical_model.ipynb | mit | # Module imports
from solarbextrapolation.map3dclasses import Map3D
from solarbextrapolation.analyticalmodels import AnalyticalModel
from solarbextrapolation.visualisation_functions import visualise
"""
Explanation: Defining and Run a Custom Analytical Model
Here you will be creating trivial analytical model following... |
giacomov/astromodels | examples/Point_source_tutorial.ipynb | bsd-3-clause | from astromodels import *
# Using J2000 R.A. and Dec (ICRS), which is the default coordinate system:
simple_source_icrs = PointSource('simple_source', ra=123.2, dec=-13.2, spectral_shape=powerlaw())
"""
Explanation: Point sources
In astromodels a point source is described by its position in the sky and its spectral ... |
astroumd/GradMap | notebooks/Lectures2018/Lecture4/Lecture4-2BodyProblem-Student.ipynb | gpl-3.0 | #Physical Constants (SI units)
G=6.67e-11
AU=1.5e11 #meters. Distance between sun and earth.
daysec=24.0*60*60 #seconds in a day
"""
Explanation: Welcome to your first numerical simulation! The 2 Body Problem
Many problems in statistical physics and astrophysics requiring solving problems consisting of many particles ... |
jupyter-widgets/ipywidgets | docs/source/examples/Widget Styling.ipynb | bsd-3-clause | from ipywidgets import Button, Layout
b = Button(description='(50% width, 80px height) button',
layout=Layout(width='50%', height='80px'))
b
"""
Explanation: Layout and Styling of Jupyter widgets
This notebook presents how to layout and style Jupyter interactive widgets to build rich and reactive widget-ba... |
lwahedi/CurrentPresentation | talks/MDI2/Scraping+Lecture.ipynb | mit | import pandas as pd
import numpy as np
import pickle
import statsmodels.api as sm
from sklearn import cluster
import matplotlib.pyplot as plt
%matplotlib inline
from bs4 import BeautifulSoup as bs
import requests
import time
# from ggplot import *
"""
Explanation: Collecting and Using Data in Python
Laila A. Wahedi
Ma... |
vikashvverma/machine-learning | mlfoundation/istat/L1_Starter_Code.ipynb | mit | import unicodecsv
## Longer version of code (replaced with shorter, equivalent version below)
# enrollments = []
# f = open('enrollments.csv', 'rb')
# reader = unicodecsv.DictReader(f)
# for row in reader:
# enrollments.append(row)
# f.close()
def read_csv(filename):
with open(filename, 'rb') as f:
r... |
GoogleCloudPlatform/training-data-analyst | quests/sparktobq/02_gcs.ipynb | apache-2.0 | # Catch up cell. Run if you did not do previous notebooks of this sequence
!wget http://kdd.ics.uci.edu/databases/kddcup99/kddcup.data_10_percent.gz
"""
Explanation: Migrating from Spark to BigQuery via Dataproc -- Part 2
Part 1: The original Spark code, now running on Dataproc (lift-and-shift).
Part 2: Replace HDFS ... |
dougkelly/SmartMeterResearch | SmartMeterResearch_Phase2.ipynb | apache-2.0 | s3 = boto3.client('s3')
s3.list_buckets()
def create_s3_bucket(bucketname):
"""Quick method to create bucket with exception handling"""
s3 = boto3.resource('s3')
exists = True
bucket = s3.Bucket(bucketname)
try:
s3.meta.client.head_bucket(Bucket=bucketname)
except botocore.exceptions.C... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session07/Day0/TooBriefVisualization.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Introduction to Visualization:
Density Estimation and Data Exploration
Version 0.1
There are many flavors of data analysis that fall under the "visualization" umbrella in astronomy. Today, by way of example, we will focus on 2 basic... |
antoniomezzacapo/qiskit-tutorial | community/terra/qis_adv/topological_quantum_walk.ipynb | apache-2.0 | #initialization
import sys
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
# importing QISKit
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
from qiskit import Aer, IBMQ, execute
from qiskit.wrapper.jupyter import *
from qiskit.backends.ibmq import least_busy
from qiskit.... |
sertansenturk/tomato | demos/score_analysis_demo.ipynb | agpl-3.0 | score_data = scoreAnalyzer.analyze(txt_filename, mu2_filename, symbtr_name=symbtr_name)
# pretty print the metadata
pprint(score_data['metadata'])
"""
Explanation: You can use the single line call "analyze," which does all the available analysis simultaneously
End of explanation
"""
from tomato.metadata.symbtr im... |
rastala/mmlspark | notebooks/samples/102 - Regression Example with Flight Delay Dataset.ipynb | mit | import numpy as np
import pandas as pd
import mmlspark
"""
Explanation: 102 - Training Regression Algorithms with the L-BFGS Solver
In this example, we run a linear regression on the Flight Delay dataset to predict the delay times.
We demonstrate how to use the TrainRegressor and the ComputePerInstanceStatistics APIs.... |
4dsolutions/Python5 | STEM Mathematics.ipynb | mit | from itertools import accumulate, islice
def cubocta():
"""
Classic Generator: Cuboctahedral / Icosahedral #s
https://oeis.org/A005901
"""
yield 1 # nuclear ball
f = 1
while True:
elem = 10 * f * f + 2 # f for frequency
yield elem # <--- pause / resume here
f +... |
castanan/w2v | ml-scripts/Word2Vec with Tweets.ipynb | mit | t0 = time.time()
datapath = '/Users/jorgecastanon/Documents/github/w2v/data/tweets.gz'
tweets = sqlContext.read.json(datapath)
tweets.registerTempTable("tweets")
twr = tweets.count()
print "Number of tweets read: ", twr
# this line add ~7 seconds (from ~24.5 seconds to ~31.5 seconds)
# Number of tweets read: 239082
p... |
kit-cel/wt | sigNT/signals_transforms/rect_sinc.ipynb | gpl-2.0 | import numpy as np
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(18, 6) )
"""
Explanation: Content and Objective
Show different aspects when dealing with FFT
Using ... |
farr/emcee | docs/_static/notebooks/autocorr.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1234)
# Build the celerite model:
import celerite
from celerite import terms
kernel = terms.RealTerm(log_a=0.0, log_c=-6.0)
kernel += terms.RealTerm(log_a=0.0, log_c=-2.0)
# The true autocorrelation time can be calculated analytically:
true_tau = sum(... |
pagutierrez/tutorial-sklearn | notebooks-spanish/20-clustering_jerarquico_y_basado_densidades.ipynb | cc0-1.0 | from sklearn import datasets
iris = datasets.load_iris()
X = iris.data[:, [2, 3]]
y = iris.target
n_samples, n_features = X.shape
plt.scatter(X[:, 0], X[:, 1], c=y);
"""
Explanation: Aprendizaje no supervisado: algoritmos de clustering jerárquicos y basados en densidades
En el cuaderno número 8, introdujimos uno de ... |
mavillan/SciProg | 01_intro/01_intro.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
"""
Explanation: <h1 align="center">Scientific Programming in Python</h1>
<h2 align="center">Topic 1: Introduction and basic tools </h2>
Notebook created by Martín Villanueva - martin.villanueva@usm.cl - DI UTFSM - April 2017.
En... |
cochoa0x1/integer-programming-with-python | 04-packing-and-allocation/knapsack_problem.ipynb | mit | from pulp import *
import numpy as np
"""
Explanation: Knapsack Problem
Bin packing tried to minimize the number of bins needed for a fixed number of items, if we instead fix the number of bins and assign some way to value objects, then the knapsack problem tells us which objects to take to maximize our total item val... |
GoogleCloudPlatform/training-data-analyst | courses/unstructured/ML-Tests-Solution.ipynb | apache-2.0 | from googleapiclient.discovery import build
import subprocess
images = subprocess.check_output(["gsutil", "ls", "gs://{}/unstructured/photos".format(BUCKET)])
images = list(filter(None,images.split('\n')))
print(images)
"""
Explanation: <h2> Finding specific text in a corpus of scanned documents </h2>
End of explanat... |
ocefpaf/secoora | notebooks/timeSeries/ssh/01-skill_score.ipynb | mit | import os
try:
import cPickle as pickle
except ImportError:
import pickle
run_name = '2014-07-07'
fname = os.path.join(run_name, 'config.pkl')
with open(fname, 'rb') as f:
config = pickle.load(f)
import numpy as np
from pandas import DataFrame, read_csv
from utilities import (load_secoora_ncs, to_html,
... |
ES-DOC/esdoc-jupyterhub | notebooks/hammoz-consortium/cmip6/models/sandbox-2/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'hammoz-consortium', 'sandbox-2', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: HAMMOZ-CONSORTIUM
Source ID: SANDBOX-2
Topic: Aerosol
Sub-Topics: T... |
HrantDavtyan/Data_Scraping | Week 5/Working_with_XML_docs.ipynb | apache-2.0 | data = '''
<xml_data>
<person>
<id>01</id>
<name>
<first>Hrant</first>
<last>Davtyan</last>
</name>
<status organization="AUA">Instructor</status>
</person>
<person>
<id>02</id>
<name>
<first>Jack</first>
<last>N... |
Startupsci/data-science-notebooks | .ipynb_checkpoints/titanic-kaggle-old-checkpoint.ipynb | mit | df['Gender'] = df['Sex'].map( {'female': 0, 'male': 1} ).astype(int)
df.head()
"""
Explanation: Convert Sex feature to numeric
End of explanation
"""
df['Age'].dropna().hist(bins=16, range=(0,80), alpha = .5)
P.show()
median_ages = np.zeros((2,3))
median_ages
for i in range(0, 2):
for j in range(0, 3):
... |
satishkt/ML-Foundations-Coursera | Week3-Classification/.ipynb_checkpoints/Analyzing product sentiment-checkpoint.ipynb | bsd-2-clause | import graphlab
"""
Explanation: Predicting sentiment from product reviews
Fire up GraphLab Create
End of explanation
"""
products = graphlab.SFrame('amazon_baby.gl/')
"""
Explanation: Read some product review data
Loading reviews for a set of baby products.
End of explanation
"""
products.head()
"""
Explanation... |
grokkaine/biopycourse | day1/.ipynb_checkpoints/tutorial-checkpoint.ipynb | cc0-1.0 | # This is a line comment.
"""
A multi-line
comment.
"""
a = None #Just declared an empty object
print(a)
a = 1
print(a)
a = 'abc'
print(a)
b = 3
c = [1, 2, 3]
a = [a, 2, b, 1., 1.2e-5, True] #This is a list.
print(a)
## Python is a dynamic language
a = 1
print(type(a))
print(a)
a = "spam"
print(type(a))
print(a)
a = ... |
jorisvandenbossche/DS-python-data-analysis | notebooks/pandas_03a_selecting_data.ipynb | bsd-3-clause | import pandas as pd
# redefining the example DataFrame
data = {'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],
'population': [11.3, 64.3, 81.3, 16.9, 64.9],
'area': [30510, 671308, 357050, 41526, 244820],
'capital': ['Brussels', 'Paris', 'Berlin', 'Amsterdam', 'Lo... |
slundberg/shap | notebooks/benchmark/text/Abstractive Summarization Benchmark Demo.ipynb | mit | import numpy as np
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
import nlp
import shap
import shap.benchmark as benchmark
"""
Explanation: Text Data Explanation Benchmarking: Abstractive Summarization
This notebook demonstrates how to use the benchmark utility to benchmark the performance... |
Kaggle/learntools | notebooks/machine_learning/raw/tut_automl.ipynb | apache-2.0 | #$HIDE_INPUT$
# Save CSV file with first 2 million rows only
import pandas as pd
train_df = pd.read_csv("../input/new-york-city-taxi-fare-prediction/train.csv", nrows = 2_000_000)
train_df.to_csv("train_small.csv", index=False)
PROJECT_ID = 'kaggle-playground-170215'
BUCKET_NAME = 'automl-tutorial-alexis'
DATASET_DIS... |
QuantScientist/Deep-Learning-Boot-Camp | day03/Advanced_Keras_Tutorial/1.0 Multi-Modal Networks.ipynb | mit | !pip install keras==2.0.8
from keras.datasets import mnist
from keras.layers import *
from keras.layers import Dense, Input, Flatten
from keras.models import Model
from keras.layers.merge import concatenate
from keras.utils import np_utils
img_rows, img_cols = 28, 28
if K.image_data_format() == 'channels_first':
... |
tolaoniyangi/dmc | notebooks/week-3/01-basic ann.ipynb | apache-2.0 | %matplotlib inline
import random
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set(style="ticks", color_codes=True)
from sklearn.preprocessing import OneHotEncoder
from sklearn.utils import shuffle
"""
Explanation: Lab 3 - Basic Artificial Neural Network
In this lab we will build a very... |
CosmoJG/neural-heatmap | cable-properties/cable-length-calculator.ipynb | gpl-3.0 | # Imports
import sys # Required for system access (below)
import os # Required for os access (below)
sys.path.append(os.path.join(os.path.dirname(os.getcwd()), 'dependencies'))
from neuron_readExportedGeometry import * # Required to interpret hoc files
"""
Explanation: Cable Length Calculator
This program reads a neur... |
planetlabs/notebooks | jupyter-notebooks/analytics/change_detection_heatmap.ipynb | apache-2.0 | !pip install cython
!pip install https://github.com/SciTools/cartopy/archive/v0.18.0.zip
"""
Explanation: Creating a Heatmap of Vector Results
In this notebook, you'll learn how to use Planet's Analytics API to display a heatmap of vector analytic results, specifically buildng change detections. This can be used to i... |
GSimas/EEL7045 | Aula 9.3 - Circuitos RC.ipynb | mit | print("Exemplo 7.1")
import numpy as np
from sympy import *
C = 0.1
v0 = 15
t = symbols('t')
Req1 = 8 + 12
Req2 = Req1*5/(Req1 + 5)
tau = C*Req2
vc = v0*exp(-t/tau)
vx = vc*12/(12 + 8)
ix = vx/12
print("Tensão Vc:",vc,"V")
print("Tensão Vx:",vx,"V")
print("Corrente ix:",ix,"A")
"""
Explanation: Circuitos Lineares... |
qinwf-nuan/keras-js | notebooks/layers/pooling/GlobalMaxPooling3D.ipynb | mit | data_in_shape = (6, 6, 3, 4)
L = GlobalMaxPooling3D(data_format='channels_last')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(270)
data_in = 2 * np.random.random(data_in_shape) - 1
result ... |
njtwomey/ADS | 03_data_transformation_and_integration/01_wrangling_casas.ipynb | mit | ## from __future__ import print_function # uncomment if using python 2
from os.path import join
import pandas as pd
import numpy as np
from datetime import datetime
%matplotlib inline
"""
Explanation: Applied Data Science
Data Wrangling
Niall Twomey
To contact, please email <firstname>.<lastname>@brist... |
snth/ctdeep | MNIST Tutorial.ipynb | mit | from __future__ import absolute_import
from __future__ import print_function
from ipywidgets import interact, interactive, widgets
import numpy as np
np.random.seed(1337) # for reproducibility
"""
Explanation: Deep Neural Networks
Theano
Python library that provides efficient (low-level) tools for working with Neura... |
hankcs/HanLP | plugins/hanlp_demo/hanlp_demo/zh/ner_stl.ipynb | apache-2.0 | !pip install hanlp -U
"""
Explanation: <h2 align="center">点击下列图标在线运行HanLP</h2>
<div align="center">
<a href="https://colab.research.google.com/github/hankcs/HanLP/blob/doc-zh/plugins/hanlp_demo/hanlp_demo/zh/ner_mtl.ipynb" target="_blank"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Ope... |
Chipe1/aima-python | games.ipynb | mit | from games import *
from notebook import psource, pseudocode
"""
Explanation: GAMES OR ADVERSARIAL SEARCH
This notebook serves as supporting material for topics covered in Chapter 5 - Adversarial Search in the book Artificial Intelligence: A Modern Approach. This notebook uses implementations from games.py module. Let... |
mrmeswani/Robotics | RoboND-Rover-Project/src/.ipynb_checkpoints/Rover_Project_Test_Notebook-checkpoint.ipynb | gpl-3.0 | %%HTML
<style> code {background-color : orange !important;} </style>
%matplotlib inline
#%matplotlib qt # Choose %matplotlib qt to plot to an interactive window (note it may show up behind your browser)
# Make some of the relevant imports
import cv2 # OpenCV for perspective transform
import numpy as np
import matplotl... |
Diyago/Machine-Learning-scripts | DEEP LEARNING/fastai kaggle rossman - tabular regression.ipynb | apache-2.0 | %matplotlib inline
%reload_ext autoreload
%autoreload 2
from fastai.structured import *
from fastai.column_data import *
np.set_printoptions(threshold=50, edgeitems=20)
PATH='data/rossmann/'
"""
Explanation: Structured and time series data
This notebook contains an implementation of the third place result in the Ros... |
tensorflow/federated | docs/tutorials/custom_federated_algorithms_2.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
ThyrixYang/LearningNotes | MOOC/stanford_cnn_cs231n/assignment2/ConvolutionalNetworks.ipynb | gpl-3.0 | # As usual, a bit of setup
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.cnn import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient
from cs231n.layers import *
fro... |
wisner23/serenata-de-amor | develop/2016-08-08-irio-translate-dataset.ipynb | mit | import pandas as pd
data = pd.read_csv('../data/2016-08-08-AnoAtual.csv')
data.shape
data.head()
data.iloc[0]
"""
Explanation: Translate dataset
The main language of the project is English: works well mixed in programming languages like Python and provides a low barrier for non-Brazilian contributors. Today, the da... |
adit-chandra/tensorflow | tensorflow/lite/examples/experimental_new_converter/keras_lstm.ipynb | apache-2.0 | !pip install tf-nightly --upgrade
"""
Explanation: Overview
This CodeLab demonstrates how to build a LSTM model for MNIST recognition using Keras, and how to convert it to TensorFlow Lite.
The CodeLab is very similar to the tf.lite.experimental.nn.TFLiteLSTMCell
CodeLab. However, with the control flow support in the e... |
lukasmerten/CRPropa3 | doc/pages/example_notebooks/secondaries/photons.v4.ipynb | gpl-3.0 | from crpropa import *
obs = Observer()
obs.add(ObserverPoint())
obs.add(ObserverInactiveVeto())
t = TextOutput("photon_electron_output.txt", Output.Event1D)
obs.onDetection(t)
sim = ModuleList()
sim.add(SimplePropagation())
sim.add(Redshift())
sim.add(EMPairProduction(CMB(),True))
sim.add(EMPairProduction(IRB_Gilmore... |
anachlas/w210_vendor_recommendor | Collaborative Filtering on Spark.ipynb | gpl-3.0 | import os
import sys
spark_home = os.environ['SPARK_HOME'] = '/Users/ozimmer/GoogleDrive/berkeley/w261/spark-2.0.0-bin-hadoop2.6'
if not spark_home:
raise ValueError('SPARK_HOME enviroment variable is not set')
sys.path.insert(0,os.path.join(spark_home,'python'))
sys.path.insert(0,os.path.join(spark_home,'python/li... |
francisc0garcia/autonomous_bicycle | docs/python_notebooks/EKF_Design.ipynb | apache-2.0 | # Import dependencies
from __future__ import division, print_function
%matplotlib inline
import scipy
from BicycleTrajectory2D import *
from BicycleUtils import *
from FormatUtils import *
from PlotUtils import *
"""
Explanation: Extended Kalman Filter design for bicycle's kinematic motion model
End of explanation
"... |
cgnorthcutt/rankpruning | tutorial_and_testing/tutorial.ipynb | mit | # Choose mislabeling noise rates.
frac_pos2neg = 0.8 # rh1, P(s=0|y=1) in literature
frac_neg2pos = 0.15 # rh0, P(s=1|y=0) in literature
# Combine data into training examples and labels
data = neg.append(pos)
X = data[["x1","x2"]].values
y = data["label"].values
# Noisy P̃Ñ learning: instead of target y, we have s co... |
xebia-france/luigi-airflow | Luigi_airflow_003.ipynb | apache-2.0 | raw_dataset = pd.read_csv(source_path + "Speed_Dating_Data.csv")
"""
Explanation: Import data
End of explanation
"""
raw_dataset.head(3)
raw_dataset_copy = raw_dataset
check1 = raw_dataset_copy[raw_dataset_copy["iid"] == 1]
check1_sel = check1[["iid", "pid", "match","gender","date","go_out","sports","tvsports","ex... |
jgoppert/pymola | test/notebooks/XML.ipynb | bsd-3-clause | m1_xml = os.path.join(
'..', 'models', 'bouncing-ball.xml')
m1_ca = parse_xml.parse_file(m1_xml)
m1_ca
m1_ode = m1_ca.to_ode()
m1_ode
m1_ode.prop['x']['start'] = 1
data1 = sim_scipy.sim(m1_ode, {'dt': 0.01, 'tf': 3.5, 'integrator': 'dopri5'})
plt.figure(figsize=(15, 10))
analysis.plot(data1, marker='.', linewidth... |
ptpro3/ptpro3.github.io | Projects/Challenges/Challenge09/challenge_set_9ii_prashant.ipynb | mit | from sqlalchemy import create_engine
import pandas as pd
cnx = create_engine('postgresql://prashant:ptpro3@52.14.144.23:5432/prashant')
#port ~ 5432
pd.read_sql_query('''SELECT * FROM allstarfull LIMIT 5''',cnx)
pd.read_sql_query('''SELECT * FROM schools LIMIT 5''',cnx)
pd.read_sql_query('''SELECT * FROM salaries LI... |
skkandrach/foundations-homework | Homework_5_Soma_graded.ipynb | mit | # !pip3 install requests
import requests
response = requests.get('https://api.spotify.com/v1/search?query=Lil+&offset=0&limit=50&type=artist&market=US')
data = response.json()
data.keys()
artist_data = data['artists']['items']
for artist in artist_data:
print(artist['name'], artist['popularity'], artist['genres']) ... |
Boussau/Notebooks | Notebooks/computeConsensusChronogram.ipynb | gpl-2.0 | import sys
from ete3 import Tree, TreeStyle, NodeStyle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import math
fileT = "100.trees"
try:
f=open(fileT, 'r')
except IOError:
print ("Unknown file: " + fileT)
sys.exit()
allTrees = list()
for l in f:
allTrees.append( Tree( l.stri... |
jdnz/qml-rg | Meeting 6/aps_with_classifiers.ipynb | gpl-3.0 | import numpy as np
import os
from skimage.transform import resize
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
import image_loader as im
from matplotlib import pyplot as plt
%matplotlib inline
path=os.getcwd()+'/' # finds the path of the folder in which the notebook is
path_train=path+'i... |
csadorf/signac | doc/signac_202_Integration_with_pandas.ipynb | bsd-3-clause | import signac
import pandas as pd
project = signac.get_project(root='projects/tutorial')
"""
Explanation: 2.2 Integration with pandas data frames
As was shown earlier, we can use indexes to search for specific data points.
One way to operate on the data is using pandas data frames.
Please note: The following steps re... |
wasit7/tutorials | flask/tu/notebook/.ipynb_checkpoints/Somkiats-Basic-Python-checkpoint.ipynb | mit | x=1
print x
type(x)
x.conjugate()
type(1+2j)
z=1+2j
print z
(1,2)
t=(1,2,"text")
t
t
def foo():
return (1,2)
x,y=foo()
print x
print y
def swap(x,y):
return (y,x)
x=1;y=2
print "{0:d} {1:d}".format(x,y)
x,y=swap(x,y)
print "{:f} {:f}".format(x,y)
dir(1)
x=[]
x.append("text")
x
x.append(1)
x.p... |
SaTa999/pyPanair | examples/tutorial2/tutorial2.ipynb | mit | %matplotlib notebook
import matplotlib.pyplot as plt
from pyPanair.preprocess import wgs_creator
for eta in ("0000", "0126", "0400", "0700", "1000"):
af = wgs_creator.read_airfoil("eta{}.csv".format(eta))
plt.plot(af[:,0], af[:,2], "k-", lw=1.)
plt.plot((0.5049,), (0,), "ro", label="Center of rotation")
plt.le... |
austinjalexander/sandbox | python/py/nanodegree/intro_ds/final_project/IntroDS-ProjectOne-Section2.ipynb | mit | import numpy as np
import pandas as pd
import scipy as sp
import scipy.stats as st
import statsmodels.api as sm
import scipy.optimize as op
from sklearn.cross_validation import train_test_split
import matplotlib.pyplot as plt
%matplotlib inline
filename = '/Users/excalibur/py/nanodegree/intro_ds/final_project/improve... |
ML4DS/ML4all | R_lab1_ML_Bay_Regresion/old/Pract_regression_student.ipynb | mit | # Import some libraries that will be necessary for working with data and displaying plots
# To visualize plots in the notebook
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import scipy.io # To read matlab files
from scipy import spatial
imp... |
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