repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
jeiranj/gensim | docs/notebooks/deepir.ipynb | gpl-3.0 | import re
contractions = re.compile(r"'|-|\"")
# all non alphanumeric
symbols = re.compile(r'(\W+)', re.U)
# single character removal
singles = re.compile(r'(\s\S\s)', re.I|re.U)
# separators (any whitespace)
seps = re.compile(r'\s+')
# cleaner (order matters)
def clean(text):
text = text.lower()
text = contr... |
weikang9009/pysal | notebooks/model/spvcm/using_the_sampler.ipynb | bsd-3-clause | from pysal.model import spvcm as spvcm #package API
spvcm.both_levels.Generic # abstract customizable class, ignores rho/lambda, equivalent to MVCM
spvcm.both_levels.MVCM # no spatial effect
spvcm.both_levels.SESE # both spatial error (SE)
spvcm.both_levels.SESMA # response-level SE, region-level spatial moving averag... |
batfish/pybatfish | docs/source/notebooks/forwarding.ipynb | apache-2.0 | bf.set_network('generate_questions')
bf.set_snapshot('generate_questions')
"""
Explanation: Packet Forwarding
This category of questions allows you to query how different types of
traffic is forwarded by the network and if endpoints are able to
communicate. You can analyze these aspects in a few different ways.
Trac... |
rishuatgithub/MLPy | nlp/UPDATED_NLP_COURSE/01-NLP-Python-Basics/02-Stemming.ipynb | apache-2.0 | # Import the toolkit and the full Porter Stemmer library
import nltk
from nltk.stem.porter import *
p_stemmer = PorterStemmer()
words = ['run','runner','running','ran','runs','easily','fairly']
for word in words:
print(word+' --> '+p_stemmer.stem(word))
"""
Explanation: <a href='http://www.pieriandata.com'> <i... |
AkshanshChahal/BTP | Satellite/Try Test Learn.ipynb | mit | import numpy as np
import pandas as pd
# importing the dataset we prepared and saved using Baseline 1 Notebook
ricep = pd.read_csv("/Users/macbook/Documents/BTP/Notebook/BTP/ricep.csv")
ricep.head()
ricep = ricep.drop(["Unnamed: 0"],axis=1)
ricep["phosphorus"] = ricep["phosphorus"]*10
ricep["value"] = ricep["Producti... |
mdeff/ntds_2016 | toolkit/03_ex_hpc.ipynb | mit | def accuracy_python(y_pred, y_true):
"""Plain Python implementation."""
num_correct = 0
for y_pred_i, y_true_i in zip(y_pred, y_true):
if y_pred_i == y_true_i:
num_correct += 1
return num_correct / len(y_true)
"""
Explanation: A Python Tour of Data Science: High Performance Computin... |
jameslao/Algorithmic-Pearls | 0-1-Knapsack.ipynb | mit | def knapsack(v, w, limit, n):
F = [[0] * (limit + 1) for x in range(n + 1)]
for i in range(0, n): # F[-1] is all 0.
for j in range(limit + 1):
if j >= w[i]:
F[i][j] = max(F[i - 1][j], F[i - 1][j - w[i]] + v[i])
else:
F[i][j] = F[i -... |
dborgesr/Euplotid | pipelines/fq2HiCInts.ipynb | gpl-3.0 | annotation="/input_dir/mm9"
tmp="/input_dir/"
input_dir="/input_dir/"
output_dir="/output_dir/"
input_fq_1="HiC_mesc_1_1M.fq.gz"
input_fq_2="HiC_mesc_2_1M.fq.gz"
sample_name="test"
bin_size="10000"
"""
Explanation: Call DNA-DNA interactions using raw HiC data
Install instructions for HiCPro required after first image ... |
tensorflow/docs-l10n | site/en-snapshot/tensorboard/text_summaries.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... |
pycrystem/pycrystem | doc/demos/08 Pair Distribution Function Analysis.ipynb | gpl-3.0 | %matplotlib inline
import hyperspy.api as hs
import pyxem as pxm
import numpy as np
"""
Explanation: PDF Analysis Tutorial
Introduction
This tutorial demonstrates how to acquire a multidimensional pair distribution function (PDF) from both a flat field electron diffraction pattern and a scanning electron diffraction d... |
datosgobar/pydatajson | samples/caso-uso-2-pydatajson-xlsx-justicia-no-valido.ipynb | mit | import arrow
import os, sys
sys.path.insert(0, os.path.abspath(".."))
from pydatajson import DataJson #lib y clase
from pydatajson.readers import read_catalog # lib, modulo ... metodo Lle el catalogo -json o xlsx o (local o url) dicc- y lo transforma en un diccionario de python
from pydatajson.writers import write_json... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_sensor_regression.ipynb | bsd-3-clause | # Authors: Tal Linzen <linzen@nyu.edu>
# Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.stats.regression import linear_regression
print(__doc__)
data_path = sample.data_path()
"""
Explanation: Sensor space lea... |
shradhaN/python_git_sessiom | Session 4.ipynb | mit | import sqlite3
#import the driver
##psycopg2 for protsgeSQL
# pymysql for MySQL
conn = sqlite3.connect('example.sqlite3')
#connecting to sqlite 3 and makes a new database file if file not already present
cur = conn.cursor()
#makes a file cursor we can make multiple cursors as well
cur.execute('CREATE TABLE countries... |
mattilyra/gensim | docs/notebooks/word2vec.ipynb | lgpl-2.1 | # import modules & set up logging
import gensim, logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
sentences = [['first', 'sentence'], ['second', 'sentence']]
# train word2vec on the two sentences
model = gensim.models.Word2Vec(sentences, min_count=1)
"""
Explanation... |
Murali-group/2017-ICSB-graphspace-tutorial | session2.ipynb | gpl-3.0 | !pip install graphspace_python==0.8.3
"""
Explanation: Session 2: Integrating GraphSpace into network analysis projects
Presenters: Aditya Bharadwaj, Jeffrey N. Law and T. M. Murali
Introduction
Required files for today
Clone or download this repository: http://bit.ly/2017icsb
IPython/Jupyter notebooks
Datasets in t... |
mlamoureux/PIMS_YRC | Using_Python.ipynb | mit | 2+2
2/3
(1+2j)*(2+3j)
"""
Explanation: Using Python in Jupyter
This is a typical notebook in Jupyter.
It is organized as a series of cells. Each cell could contain text, code, or some raw format (Raw NBConvert).
You can select which type of code you want to run. For this notebook, we are using Python 3.
You could ... |
DJCordhose/ai | notebooks/tf2/rnn-add-example.ipynb | mit | !pip install -q tf-nightly-gpu-2.0-preview
import tensorflow as tf
print(tf.__version__)
"""
Explanation: <a href="https://colab.research.google.com/github/DJCordhose/ai/blob/master/notebooks/tf2/rnn-add-example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In C... |
mne-tools/mne-tools.github.io | 0.18/_downloads/0cd97a6d68ec19255d6658b4ecac3774/plot_artifacts_correction_ssp.ipynb | bsd-3-clause | import numpy as np
import mne
from mne.datasets import sample
from mne.preprocessing import compute_proj_ecg, compute_proj_eog
# getting some data ready
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = mne.io.read_raw_fif(raw_fname, preload=True)
"""
Explana... |
jhillairet/scikit-rf | doc/source/examples/vectorfitting/vectorfitting_ex2_190ghz_active.ipynb | bsd-3-clause | import skrf
import numpy as np
import matplotlib.pyplot as mplt
"""
Explanation: Ex2: Measured 190 GHz Active 2-Port
The Vector Fitting feature is demonstrated using a 2-port S-matrix of an active circuit measured from 140 GHz to 220 GHz. Additional explanations and background information can be found in the Vector Fi... |
AllenDowney/ModSim | python/soln/examples/wall_soln.ipynb | gpl-2.0 | # install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve... |
adityaka/misc_scripts | python-scripts/data_analytics_learn/ipython_notebook_tutorial.ipynb | bsd-3-clause | # Hit shift + enter or use the run button to run this cell and see the results
print 'hello world'
# The last line of every code cell will be displayed by default,
# even if you don't print it. Run this cell to see how this works.
2 + 2 # The result of this line will not be displayed
3 + 3 # The result of this line... |
liganega/Gongsu-DataSci | previous/notes2017/old/NB-15-Recursion.ipynb | gpl-3.0 | def factorial(n):
return n * factorial(n-1)
"""
Explanation: 재귀함수
이번에 공부할 주제는 재귀(recursion)이다. 재귀는 한자용어로 "본래 있던 곳으로 다시 돌아온다"의 의미를 갖는다.
재귀를 이용하여 구현한 함수를 _재귀함수(recursive function)_라 부른다.
재귀함수 용법
재귀함수를 사용하면 복잡한 코드를 매우 간단하게 구현할 수 있다는 장점이 있다.
하지만 재귀함수를 호출하면 메모리 내부에서 어떤 변화가 어떻게 발생하는가를 이해하는 일이 경우에 따라 간단하지 않다.
또한 시간 및 공... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/end_to_end_ml/solutions/sample_babyweight.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install --user google-cloud-bigquery==1.25.0
"""
Explanation: Creating a Sampled Dataset
Learning Objectives
Setup up the environment
Sample the natality dataset to create train, eval, test sets
Preprocess the data in Pandas dataframe
Introduct... |
coolharsh55/advent-of-code | 2016/python3/Day17.ipynb | mit | with open('../inputs/day17.txt', 'r') as f:
path_string = f.readline().strip()
TEST_DATA = (
'ihgpwlah',
'kglvqrro',
'ulqzkmiv'
)
"""
Explanation: Day 17: Two Steps Forward
author: Harshvardhan Pandit
license: MIT
link to problem statement
You're trying to access a secure vault protected by a 4x4 grid... |
AshivDhondea/SORADSIM | example_notebooks/notebook_005_orbitpropa_sgp4_local_topo_visualization.ipynb | mit | from IPython.display import Image
Image(filename='ashivorbit2017.png')
# Note that this image belongs to me. I have created it myself.
# Load the libraries required
# These two are mine
import AstroFunctions as AstFn
import UnbiasedConvertedMeasurements as UCM
import math
import numpy as np
# Libraries needed for ti... |
mathLab/RBniCS | tutorials/17_navier_stokes/tutorial_navier_stokes_1_deim.ipynb | lgpl-3.0 | from ufl import transpose
from dolfin import *
from rbnics import *
"""
Explanation: Tutorial 17 - Navier Stokes equations
Keywords: DEIM, supremizer operator
1. Introduction
In this tutorial, we will study the Navier-Stokes equations over the two-dimensional backward-facing step domain $\Omega$ shown below:
<img src=... |
iutzeler/Introduction-to-Python-for-Data-Sciences | 3-2_Dataframes.ipynb | mit | import numpy as np
import pandas as pd
"""
Explanation: <table>
<tr>
<td width=15%><img src="./img/UGA.png"></img></td>
<td><center><h1>Introduction to Python for Data Sciences</h1></center></td>
<td width=15%><a href="http://www.iutzeler.org" style="font-size: 16px; font-weight: bold">Franck Iutzeler</a> </td>
</tr>
... |
chengsoonong/crowdastro | notebooks/35_classifier_analysis.ipynb | mit | import csv
import sys
import astropy.wcs
import h5py
import matplotlib.pyplot as plot
import numpy
import sklearn.metrics
sys.path.insert(1, '..')
import crowdastro.train
CROWDASTRO_H5_PATH = '../data/crowdastro.h5'
CROWDASTRO_CSV_PATH = '../crowdastro.csv'
TRAINING_H5_PATH = '../data/training.h5'
ARCMIN = 1 / 60
%... |
remenska/iSDM | notebooks/Demo-Climate-DEM.ipynb | apache-2.0 | import logging
root = logging.getLogger()
root.addHandler(logging.StreamHandler())
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Reading and manipulating Climate data layers
just some logging/plotting magic to output in this notebook, nothing to care about.
End of explanation
"""
from iSDM.envi... |
bryanwweber/thermostate | docs/Plot-Tutorial.ipynb | bsd-3-clause | from thermostate import State, Q_, units
from thermostate.plotting import IdealGas, VaporDome
"""
Explanation: Ploting Tutorial
This tutorial acts as a guide to the plotting classes in ThermoState. It is designed to ease the creation of simple plots of thermodynamic states and processes for a variety of common substan... |
anhaidgroup/py_entitymatching | notebooks/guides/step_wise_em_guides/Performing Blocking Using Built-In Blockers (Sorted Neighborhood Blocker).ipynb | bsd-3-clause | # Import py_entitymatching package
import py_entitymatching as em
import os
import pandas as pd
"""
Explanation: Contents
Introduction
Block Using the Sorted Neighborhood Blocker
Block Tables to Produce a Candidate Set of Tuple Pairs
Handling Missing Values
Window Size
Stable Sort Order
Sorted Neighborhood Blocker Li... |
Vvkmnn/books | ThinkBayes/07_Prediction.ipynb | gpl-3.0 | def EvalPoissonPmf(k, lam):
return (lam)**k * math.exp(-lam) / math.factorial(k)
"""
Explanation: Prediction
The Boston Bruins problem
In the 2010-11 National Hockey League (NHL) Finals, my beloved Boston
Bruins played a best-of-seven championship series against the despised
Vancouver Canucks. Boston lost the firs... |
IBMDecisionOptimization/docplex-examples | examples/cp/jupyter/sudoku.ipynb | apache-2.0 | import sys
try:
import docplex.cp
except:
if hasattr(sys, 'real_prefix'):
#we are in a virtual env.
!pip install docplex
else:
!pip install --user docplex
"""
Explanation: Sudoku
This tutorial includes everything you need to set up decision optimization engines, build constraint pro... |
JasonNK/udacity-dlnd | dcgan-svhn/DCGAN.ipynb | mit | %matplotlib inline
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
!mkdir data
"""
Explanation: Deep Convolutional GANs
In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a De... |
adityaka/misc_scripts | python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/05_03/Final/Multiple.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
"""
Explanation: <h1>Multiples Lines, Single Plot</h1>
End of explanation
"""
data_set_size = 15
low_mu, low_sigma = 50, 4.3
low_data_set = low_mu + low_sigma * np.random.randn(data_set_size)
high_mu, high_sigma = 57, 5.2
... |
mne-tools/mne-tools.github.io | 0.22/_downloads/81308ca6ca6807326a79661c989cfcba/plot_make_report.ipynb | bsd-3-clause | # Authors: Teon Brooks <teon.brooks@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
from mne.report import Report
from mne.datasets import sample
from mne import read_evokeds
from matplotlib import pyplot as plt
data_path = sample.data_path()
meg_path = data_path + '/MEG/sampl... |
obestwalter/pet | ipynb/containers.ipynb | mit | aString = "123456"
aList = [1, 2.0, 1j, 'hello', [], {}, (1, 2)]
aSet = {1, 2.0, 1j, 'hello', (1, 2)}
aTuple = (1, 2.0, 1j, 'hello', [], {}, (1, 2))
aDict = {
1: 1,
2.0: 2.0,
1j: 1j,
(1, 2): (1, 2),
'hello': 'hello',
'list': [],
'dict': {},
}
iterables = [
aString,
aList,
aSet,
... |
mattmcd/PyBayes | scripts/dc_manipulating_time_series.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import yfinance as yf
%matplotlib inline
def ddir(name=None):
data_dir = 'dc_manipulating_time_series/stock_data/'
if name is None:
print(os.listdir(data_dir))
else:
return os.path.join(d... |
telecom-research/crtc-scraper | _code/notebooks/CRTC-Hearing-TextAnalysis.ipynb | mit | # importing code modules
import json
import ijson
from ijson import items
import pprint
from tabulate import tabulate
import matplotlib.pyplot as plt
import re
import csv
import sys
import codecs
import nltk
import nltk.collocations
import collections
import statistics
from nltk.metrics.spearman import *
from nltk.... |
thaophung/Udacity_deep_learning | sentiment-network/.ipynb_checkpoints/Sentiment_Classification_Projects-checkpoint.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
skaae/Recipes | examples/Using a Caffe Pretrained Network - CIFAR10.ipynb | mit | !wget https://www.dropbox.com/s/blrajqirr1p31v0/cifar10_nin.caffemodel
!wget https://gist.githubusercontent.com/ebenolson/91e2cfa51fdb58782c26/raw/b015b7403d87b21c6d2e00b7ec4c0880bbeb1f7e/model.prototxt
"""
Explanation: Introduction
This example demonstrates how to convert a network from Caffe's Model Zoo for use wit... |
EmuKit/emukit | notebooks/Emukit-tutorial-sensitivity-montecarlo.ipynb | apache-2.0 | # General imports
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
from matplotlib import cm
## Figures config
colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
LEGEND_SIZE = 15
TITLE_SIZE = 25
AXIS_SIZE = 15
"""
Explanation: Introduction to global... |
zerothi/sisl | docs/visualization/viz_module/showcase/GridPlot.ipynb | mpl-2.0 | import sisl
import sisl.viz
import numpy as np
# This is just for convenience to retreive files
siesta_files = sisl._environ.get_environ_variable("SISL_FILES_TESTS") / "sisl" / "io" / "siesta"
"""
Explanation: GridPlot
GridPlot class will help you very easily display any Grid.
<div class="alert alert-info">
Note
De... |
arogozhnikov/einops | docs/1-einops-basics.ipynb | mit | # Examples are given for numpy. This code also setups ipython/jupyter
# so that numpy arrays in the output are displayed as images
import numpy
from utils import display_np_arrays_as_images
display_np_arrays_as_images()
"""
Explanation: Einops tutorial, part 1: basics
<!-- <img src='http://arogozhnikov.github.io/image... |
sdpython/pyquickhelper | _unittests/ut_helpgen/data_gallery/notebooks/notebook_eleves/2014_2015/2015_page_rank.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: PageRank avec PIG
auteurs : M. Amestoy M., A. Auffret
L'algorithme PageRank propose une mesure de la pertinence d'un site. Il fut inventé par les fondateurs de google. L'implémentation proposée ici s'est appuyée sur celle proposée dans Da... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/kubeflow_pipelines/walkthrough/labs/kfp_walkthrough_vertex.ipynb | apache-2.0 | import os
import time
import pandas as pd
from google.cloud import aiplatform, bigquery
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import SGDClassifier
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
"""
Explanation: Using custom conta... |
ecervera/ga-nb | First Example.ipynb | mit | from pyevolve import G1DList
"""
Explanation: First Example
This notebook is adapted from a tutorial from the Pyevolve website
To make the API easy to use, there are default parameters for almost every parameter in Pyevolve, for example, when you will use the <tt>G1DList.G1DList</tt> genome without specifying the Muta... |
ellamil/bubblepopper | bubblepopper_3articleclusters.ipynb | mit | from sklearn import cluster
import pandas as pd
import numpy as np
import pickle
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
num_topics = 20
doc_data = pickle.load(open('pub_probabs_topic'+str(num_topics)+'.pkl','rb'))
lda_topics = ['topic'+str(i) for i in range(0,num_topics)]
cluster_dim... |
musketeer191/job_analytics | extract_feat.ipynb | gpl-3.0 | HOME_DIR = 'd:/larc_projects/job_analytics/'; DATA_DIR = HOME_DIR + 'data/clean/'
RES_DIR = HOME_DIR + 'results/'
skill_df = pd.read_csv(DATA_DIR + 'skill_index.csv')
"""
Explanation: Load data
End of explanation
"""
doc_skill = buildDocSkillMat(jd_docs, skill_df, folder=DATA_DIR)
with(open(DATA_DIR + 'doc_skill.m... |
mdiaz236/DeepLearningFoundations | intro-to-tflearn/TFLearn_Digit_Recognition.ipynb | mit | # Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
"""
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This... |
cosmolejo/Fisica-Experimental-3 | Fourier/Tarea_Fourier/FT-2D.ipynb | gpl-3.0 | import numpy as np
import matplotlib
import pylab as plt
import scipy.misc as pim
from scipy import stats
% matplotlib inline
"""
Explanation: Análisis de Fourier 2D
Última actualización: Edgar Rueda, marzo de 2016.
End of explanation
"""
tam = 256 # tamaño matriz
dx = 0.01 # resolución (m/pixel)
x = np.arange(-dx*... |
kwinkunks/axb | NumPy_reflectivity.ipynb | apache-2.0 | import numpy as np
import numpy.linalg as la
import matplotlib.pyplot as plt
from utils import plot_all
%matplotlib inline
from scipy import linalg as spla
def convmtx(h, n):
"""
Equivalent of MATLAB's convmtx function, http://www.mathworks.com/help/signal/ref/convmtx.html.
Makes the convolution matr... |
fifabsas/talleresfifabsas | python/Extras/Labo3/Adquisicion_programada.ipynb | mit | import time
import numpy as np
import visa
rm = visa.ResourceManager() # Creamos al Resource Manager
rm.list_resources() # Esto les permitirá ver qué es lo que pyvisa reconoce conectado a la PC
resource_name = 'USB0::0x0699::0x0346::C033250::INSTR' # Este es un nombre ejemplo con el cual Pyvisa reconoce al instrume... |
robertclf/FAFT | FAFT_64-points_R2C/nbFAFT128_offset_xyz_3D.ipynb | bsd-3-clause | import numpy as np
import ctypes
from ctypes import *
import pycuda.gpuarray as gpuarray
import pycuda.driver as cuda
import pycuda.autoinit
from pycuda.compiler import SourceModule
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import math
import time
%matplotlib inline
"""
Explanation: 3D Fas... |
stevetjoa/stanford-mir | mfcc.ipynb | mit | url = 'http://audio.musicinformationretrieval.com/simple_loop.wav'
urllib.urlretrieve(url, filename='simple_loop.wav')
"""
Explanation: ← Back to Index
Mel Frequency Cepstral Coefficients (MFCCs)
The mel frequency cepstral coefficients (MFCCs) of a signal are a small set of features (usually about 10-20) which co... |
karlnapf/shogun | doc/ipython-notebooks/metric/LMNN.ipynb | bsd-3-clause | import numpy
import os
import shogun as sg
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
x = numpy.array([[0,0],[-1,0.1],[0.3,-0.05],[0.7,0.3],[-0.2,-0.6],[-0.15,-0.63],[-0.25,0.55],[-0.28,0.67]])
y = numpy.array([0,0,0,0,1,1,2,2])
"""
Explanation: Metric Learning with the Shogun Machine Learning Tool... |
hetaodie/hetaodie.github.io | assets/media/uda-ml/fjd/ccjl/层次聚类/.ipynb_checkpoints/Hierarchical Clustering Lab-zh-checkpoint.ipynb | mit | from sklearn import datasets
iris = datasets.load_iris()
"""
Explanation: 层次聚类 Lab
在此 notebook 中，我们将使用 sklearn 对鸢尾花数据集执行层次聚类。该数据集包含 4 个维度/属性和 150 个样本。每个样本都标记为某种鸢尾花品种（共三种）。
在此练习中，我们将忽略标签和基于属性的聚类，并将不同层次聚类技巧的结果与实际标签进行比较，看看在这种情形下哪种技巧的效果最好。然后，我们将可视化生成的聚类层次。
1. 导入鸢尾花数据集
End of explanation
"""
iris.data[:10]
iris.target
... |
VlachosGroup/VlachosGroupAdditivity | docs/source/WorkshopJupyterNotebooks/OpenMKM_demo/batch/batch.ipynb | mit | import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 500
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
"""
Explanation: Simulating Batch Reactor
Here a batch reactor simulation is demoed with pure gas phase mechanism... |
tensorflow/text | docs/tutorials/classify_text_with_bert.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... |
stevetjoa/stanford-mir | evaluation_beat.ipynb | mit | y, sr = librosa.load('audio/prelude_cmaj.wav')
ipd.Audio(y, rate=sr)
"""
Explanation: ← Back to Index
Evaluation Example: Beat Tracking
Documentation: mir_eval.beat
Evaluation method: compute the error between the estimated beat times and some reference list of beat locations. Many metrics additionally compare t... |
CGATOxford/CGATPipelines | CGATPipelines/pipeline_docs/pipeline_peakcalling/notebooks/template_peakcalling_filtering_Report_reads_per_chr.ipynb | mit | import sqlite3
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import CGATPipelines.Pipeline as P
import os
import statistics
import collections
#load R and the R packages required
%load_ext rpy2.ipython
%R require(ggplot2)
# use these f... |
boffi/boffi.github.io | dati_2020/04/EP_Exact+Numerical.ipynb | mit | def resp_elas(m,c,k, cC,cS,w, F, x0,v0):
wn2 = k/m ; wn = sqrt(wn2) ; beta = w/wn
z = c/(2*m*wn)
wd = wn*sqrt(1-z*z)
# xi(t) = R sin(w t) + S cos(w t) + D
det = (1.-beta**2)**2+(2*beta*z)**2
R = ((1-beta**2)*cS + (2*beta*z)*cC)/det/k
S = ((1-beta**2)*cC - (2*beta*z)*cS)/det/k
D = F/k
... |
tpin3694/tpin3694.github.io | python/geocoding_and_reverse_geocoding.ipynb | mit | # Load packages
from pygeocoder import Geocoder
import pandas as pd
import numpy as np
"""
Explanation: Title: Geocoding And Reverse Geocoding
Slug: geocoding_and_reverse_geocoding
Summary: Geocoding And Reverse Geocoding
Date: 2016-05-01 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon
Geocoding (co... |
mvdbosch/AtosCodexDemo | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | gpl-3.0 | %%bash
cat /proc/cpuinfo | grep 'processor\|model name'
%%bash
free -g
"""
Explanation: Atos Codex - Data Scientist Workbench
Explore the CBS Crime and Demographics Dataset
First check some of the environment specs and see what we have here
End of explanation
"""
from __future__ import print_function
import pandas ... |
gururajl/deep-learning | image-classification/dlnd_image_classification.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10... |
robertoalotufo/ia898 | master/Rampa_solucoes.ipynb | mit | def rr_indices( lado):
import numpy as np
r,c = np.indices( (lado, lado), dtype='uint16' )
return c
print(rr_indices(11))
"""
Explanation: Análise das soluções do programa Rampa
Esta página apresenta as principais soluções apresentadas no programa Rampa.
O objetivo é entender as discrepâncias entre el... |
souljourner/fab | EDA/temp.ipynb | mit | import matplotlib.pyplot as plt # Import matplotlib
# This line is necessary for the plot to appear in a Jupyter notebook
%matplotlib inline
# Control the default size of figures in this Jupyter notebook
%pylab inline
pylab.rcParams['figure.figsize'] = (15, 9) # Change the size of plots
import glob
from collection... |
cherryc/dynet | examples/python/tutorials/RNNs.ipynb | apache-2.0 | # we assume that we have the dynet module in your path.
# OUTDATED: we also assume that LD_LIBRARY_PATH includes a pointer to where libcnn_shared.so is.
from dynet import *
"""
Explanation: RNNs tutorial
End of explanation
"""
model = Model()
NUM_LAYERS=2
INPUT_DIM=50
HIDDEN_DIM=10
builder = LSTMBuilder(NUM_LAYERS, ... |
google/flax | examples/imagenet/imagenet.ipynb | apache-2.0 | # Install ml-collections & latest Flax version from Github.
!pip install -q clu ml-collections git+https://github.com/google/flax
example_directory = 'examples/imagenet'
editor_relpaths = ('configs/default.py', 'input_pipeline.py', 'models.py', 'train.py')
repo, branch = 'https://github.com/google/flax', 'main'
# (I... |
kitu2007/dl_class | gan_mnist/Intro_to_GANs_Exercises.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
NEONScience/NEON-Data-Skills | tutorials/Python/Hyperspectral/intro-hyperspectral/Intro_NEON_AOP_HDF5_Reflectance_Flightlines_py/Intro_NEON_AOP_HDF5_Reflectance_Flightlines_py.ipynb | agpl-3.0 | #Check that you are using the correct version of Python (should be 3.4+, otherwise gdal won't work)
import sys
sys.version
"""
Explanation: syncID: 8491e02fec01499281d05f3b92409e27
title: "NEON AOP Hyperspectral Data in HDF5 format with Python - Flightlines"
description: "Learn how to read NEON AOP hyperspectral flig... |
ngast/rmf_tool | examples/BasicExample_SIR.ipynb | mit | # To load the library
import rmftool as rmf
import importlib
importlib.reload(rmf)
# To plot the results
import numpy as np
import matplotlib.pyplot as plt
# %matplotlib inline
%matplotlib notebook
"""
Explanation: This document demonstrate how to use the library to define a "density dependent population process"... |
taku-y/bmlingam | doc/notebook/expr/20160915/20160902-eval-bml.ipynb | mit | %matplotlib inline
%autosave 0
import sys, os
sys.path.insert(0, os.path.expanduser('~/work/tmp/20160915-bmlingam/bmlingam'))
from copy import deepcopy
import hashlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import time
from bmlingam import do_mcmc_bmlingam, InferPa... |
UV-CDAT/tutorials | graphics/ParallelCoordinates.ipynb | bsd-2-clause | import vcs # For plots
import vcsaddons # module containing pcoords
import cdms2 # for data
import glob # to list files in directories
import pcmdi_metrics # for special json loader class
"""
Explanation: import necessary modules
End of explanation
"""
import tempfile
import base64
class VCSAddonsNotebook(object):
... |
timothyb0912/pylogit | examples/.ipynb_checkpoints/Main PyLogit Example-checkpoint.ipynb | bsd-3-clause | from collections import OrderedDict # For recording the model specification
import pandas as pd # For file input/output
import numpy as np # For vectorized math operations
import pylogit as pl # For MNL model estimation and
... |
ChadFulton/statsmodels | examples/notebooks/statespace_arma_0.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import print_function
import numpy as np
from scipy import stats
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.graphics.api import qqplot
"""
Explanation: Autoregressive Moving Average (ARMA): Sunspots data
This notebook replicat... |
penguinmenac3/ml-notebooks | Machine Learning Basics with Sklearn.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Machine Learning Basics with Sklearn
First some imports for the notebook and visualization.
End of explanation
"""
from sklearn.datasets import load_iris
iris = load_iris()
"""
Explanation: Choosing a dataset
First of all you nee... |
WNoxchi/Kaukasos | FADL2/darknet_loss_PR.ipynb | mit | %matplotlib inline
%reload_ext autoreload
%autoreload 2
from pathlib import Path
from fastai.conv_learner import *
# from fastai.models import darknet
"""
Explanation: PR: Adding LogSoftmax layer to Darknet for Cross Entropy Loss
Wayne Nixalo - 2018/4/24
0. Proposed Change; Setup
Dataset is the fast.ai ImageNet sampl... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_eco2/td2a_eco_5d_Travailler_du_texte_les_expressions_regulieres_correction.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 2A.eco - Les expressions régulières : à quoi ça sert ? (correction)
Chercher un mot dans un texte est une tâche facile, c'est l'objectif de la méthode find attachée aux chaînes de caractères, elle suffit encore lorsqu'on cherche un mot au... |
liufuyang/deep_learning_tutorial | course-deeplearning.ai/course1-nn-and-deeplearning/Logistic+Regression+with+a+Neural+Network+mindset+v3.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset
%matplotlib inline
"""
Explanation: Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will build a ... |
b4be1/ball_catcher | src3d/help/demo.ipynb | cc0-1.0 | from casadi import *
from casadi.tools import * # for dotdraw
from matplotlib.pyplot import *
%matplotlib inline
x = SX.sym("x") # scalar symbolic primitives
y = SX.sym("y")
z = x*sin(x+y) # common mathematical operators
print z
dotdraw(z,direction="BT")
J = jacobian(z,x)
print J
dotdraw(J,direction="BT")
""... |
DJCordhose/ai | notebooks/ml/4-tf-keras-nn.ipynb | mit | import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import pandas as pd
print(pd.__version__)
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)
print(tf.__version__)
"""
Explanation: Neural Networks with TensorFlow and Keras
End of explanation
"""
df = pd.read_csv('... |
Upward-Spiral-Science/team1 | code/Spike Imaging.ipynb | apache-2.0 | # Spike images
from mpl_toolkits.mplot3d import axes3d
import numpy as np
import urllib2
import scipy.stats as stats
import matplotlib.pyplot as plt
from image_builder import get_image
np.set_printoptions(precision=3, suppress=True)
url = ('https://raw.githubusercontent.com/Upward-Spiral-Science'
'/data/master/... |
wso2/product-apim | modules/recommendation-engine/repository/resources/Word2vec_Model/.ipynb_checkpoints/Build_Word2vec_model-checkpoint.ipynb | apache-2.0 | model_1 = gensim.models.Word2Vec (dataset, size=300, window=10, min_count=5, workers=10)
model_1.train(dataset,total_examples=len(dataset),epochs=15)
"""
Explanation: The 'Dataset.txt' file consists of API descriptions of over 15,000 APIs.
Using the 'Dataset_PW.txt' file, a dataset which consists of sentences, is crea... |
moble/PostNewtonian | PNTerms/Precession.ipynb | mit | Precession_ellHat = PNCollection()
Precession_chiVec1 = PNCollection()
Precession_chiVec2 = PNCollection()
"""
Explanation: The following PNCollection objects will contain all the terms describing precession.
End of explanation
"""
Precession_ellHat.AddDerivedVariable('gamma_PN_coeff', v**2)
Precession_ellHat.AddD... |
SheffieldML/notebook | GPy/coregionalized_regression_tutorial.ipynb | bsd-3-clause | %pylab inline
import pylab as pb
pylab.ion()
import GPy
"""
Explanation: Coregionalized Regression Model (vector-valued regression)
updated: 17th June 2015
by Ricardo Andrade-Pacheco
This tutorial will focus on the use and kernel selection of the $\color{firebrick}{\textbf{coregionalized regression}}$ model in GPy.
Se... |
neildhir/DCBO | notebooks/nonstat_scm.ipynb | mit | %load_ext autoreload
%autoreload 2
import sys
sys.path.append("../src/")
sys.path.append("..")
from src.examples.example_setups import setup_nonstat_scm
from src.utils.sem_utils.toy_sems import NonStationaryDependentSEM as NonStatSEM
from src.utils.sem_utils.sem_estimate import build_sem_hat
from src.experimental.exp... |
preigemufc/1.2016.1.notebooks | Integração Numérica.ipynb | gpl-3.0 | # Antes de tudo, importamos o pacote matemático numpy
# que nos permite manipular matrizes e vetores.
import numpy as np
# Declaramos uma função onde colocaremos todo o código
# para integração de Euler, que poderemos invocar facilmente
# sempre que quisermos integrar numericamente uma equação
# diferencial.
def solv... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_objects_from_arrays.ipynb | bsd-3-clause | # Author: Jaakko Leppakangas <jaeilepp@student.jyu.fi>
#
# License: BSD (3-clause)
import numpy as np
import neo
import mne
print(__doc__)
"""
Explanation: Creating MNE objects from data arrays
In this simple example, the creation of MNE objects from
numpy arrays is demonstrated. In the last example case, a
NEO fil... |
martinjrobins/hobo | examples/optimisation/transformed-parameters.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import pints
import pints.toy as toy
# Set some random seed so this notebook can be reproduced
np.random.seed(10)
# Load a logistic forward model
model = toy.LogisticModel()
"""
Explanation: Optimisation in a transformed parameter space
This example shows you how to... |
uliang/First-steps-with-the-Python-language | Day 1 - Unit 1.1.ipynb | mit | # change this cell into a Markdown cell. Then type something here and execute it (Shift-Enter)
"""
Explanation: 1. Your first steps with Python
1.1 Introduction
Python is a general purpose programming language. It is used extensively for scientific computing, data analytics and visualization, web development and soft... |
tensorflow/docs-l10n | site/zh-cn/tutorials/load_data/text.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... |
AnyBody-Research-Group/AnyPyTools | docs/Tutorial/02_Generating_macros.ipynb | mit | from anypytools.macro_commands import (MacroCommand, Load, SetValue, SetValue_random, Dump, SaveDesign,
LoadDesign, SaveValues, LoadValues, UpdateValues, OperationRun)
"""
Explanation: Creating AnyScript Macros
AnyPyTools can create AnyScript macros automatically. Doing so simpl... |
WomensCodingCircle/CodingCirclePython | Lesson02_Conditionals/.ipynb_checkpoints/Conditional Execution-checkpoint.ipynb | mit | cleaned_room = True
took_out_trash = False
print(cleaned_room)
print(type(took_out_trash))
"""
Explanation: Conditional Execution
Boolean Expressions
We introduce a new type, the boolean. A boolean can have one of two values: True or False
End of explanation
"""
print(5 == 6)
print("Hello" != "Goodbye")
# You can... |
icoxfog417/enigma_abroad | pola/machine/topic_model_evaluation.ipynb | mit | # enable showing matplotlib image inline
%matplotlib inline
# autoreload module
%load_ext autoreload
%autoreload 2
PROJECT_ROOT = "/"
def load_local_package():
import os
import sys
root = os.path.join(os.getcwd(), "../../")
sys.path.append(root) # load project root
return root
PROJECT_ROOT = lo... |
WillenZh/deep-learning-project | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: 语言翻译
在此项目中，你将了解神经网络机器翻译这一领域。你将用由英语和法语语句组成的数据集，训练一个... |
GregDMeyer/dynamite | examples/2-Subspaces.ipynb | mit | from dynamite.operators import sigmax, sigmaz, index_sum, op_sum
# the None default argument will be important later
def build_hamiltonian(L):
interaction = op_sum(index_sum(sigmax(0)*sigmax(i), size=L) for i in range(1,L))
uniform_field = 0.5*index_sum(sigmaz(), size=L)
return interaction + uniform_field
... |
kimmintae/MNIST | MNIST Competiton_9980/mnist_competition_9980_Final.ipynb | mit | mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# test data
test_images = mnist.test.images.reshape(10000, 28, 28, 1)
test_labels = mnist.test.labels[:]
augmentation_size = 440000
images = np.concatenate((mnist.train.images.reshape(55000, 28, 28, 1), mnist.validation.images.reshape(5000, 28, 28, 1)), ax... |
cdawei/flickr-photo | src/traj_Melb.ipynb | gpl-2.0 | %matplotlib inline
import os, sys, time
import pandas as pd
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
def print_progress(cnt, total):
"""Display a progress bar"""
assert(cnt > 0 and total > 0 and cnt <= total)
length = 80
ratio = cnt / total
n = int(length * ... |
ankoorb/scipy2015_tutorial | notebooks/3. Fitting Regression Models.ipynb | cc0-1.0 | from io import StringIO
data_string = """
Drugs Score
0 1.17 78.93
1 2.97 58.20
2 3.26 67.47
3 4.69 37.47
4 5.83 45.65
5 6.00 32.92
6 6.41 29.97
"""
lsd_and_math = pd.read_table(StringIO(data_string), sep='\t', index_col=0)
lsd_and_math
"""
Explanation: Regression modeling
A general, primary goal of many statistical... |
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