repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
sbu-python-summer/python-tutorial | day-5/ngram_models.ipynb | bsd-3-clause | def bigramize(filename):
pass
"""
Explanation: $n$-gram extraction and text generation
1. Bigram extraction
Write a function that extracts possible word combinations of the length $2$ from the same file, shore_leave.txt. Note that the last word of one sentence, and the first word of the next one are not a good com... |
ayejay/reading-habits | notebook/Pocket Reading Habits.ipynb | mit | import json
import glob
import pandas as pd
import datetime
import requests
import matplotlib.pyplot as plt
import numpy as np
from wordcloud import WordCloud
from urllib.parse import urlparse
"""
Explanation: # Introduction
This notebook includes a pattern about my reading habits. <a href="https://getpocket.com">Pock... |
quantumlib/Cirq | docs/tutorials/google/floquet_calibration_example.ipynb | apache-2.0 | try:
import cirq
except ImportError:
print("installing cirq...")
!pip install --quiet cirq --pre
print("installed cirq.")
from typing import Iterable, List, Optional, Sequence
import matplotlib.pyplot as plt
import numpy as np
import os
import cirq
import cirq_google as cg # Contains the Floquet ca... |
tornadozou/tensorflow | tensorflow/tools/docker/notebooks/3_mnist_from_scratch.ipynb | apache-2.0 | from __future__ import print_function
from IPython.display import Image
import base64
Image(data=base64.decodestring("iVBORw0KGgoAAAANSUhEUgAAAMYAAABFCAYAAAARv5krAAAYl0lEQVR4Ae3dV4wc1bYG4D3YYJucc8455yCSSIYrBAi4EjriAZHECyAk3rAID1gCIXGRgIvASIQr8UTmgDA5imByPpicTcYGY+yrbx+tOUWpu2e6u7qnZ7qXVFPVVbv2Xutfce+q7hlasmTJktSAXrnn8... |
uber/pyro | tutorial/source/predictive_deterministic.ipynb | apache-2.0 | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.datasets import make_regression
import pyro.distributions as dist
from pyro.infer import MCMC, NUTS, Predictive
from pyro.infer.mcmc.util import summary
from pyro.distributions import constraints
import pyro
import torch
pyro.set_rng_s... |
tomspur/blog | posts/0001-publication-ready-figures-with-matplotlib-and-ipython-notebook/matplotlib_plots.ipynb | mit | %matplotlib inline
import seaborn as snb
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Publication ready figures with matplotlib and Jupyter notebook
A very convenient workflow to analyze data and create figures that can be used in various ways for publication is to use the IPython Notebook or Ju... |
tuanavu/coursera-university-of-washington | machine_learning/2_regression/assignment/week3/week-3-polynomial-regression-assignment-exercise.ipynb | mit | import sys
sys.path.append('C:\Anaconda2\envs\dato-env\Lib\site-packages')
import graphlab
"""
Explanation: Regression Week 3: Assessing Fit (polynomial regression)
In this notebook you will compare different regression models in order to assess which model fits best. We will be using polynomial regression as a means ... |
ellisztamas/faps | docs/tutorials/.ipynb_checkpoints/02_genotype_data-checkpoint.ipynb | mit | import numpy as np
import faps as fp
print("Created using FAPS version {}.".format(fp.__version__))
"""
Explanation: Genotype data in FAPS
End of explanation
"""
allele_freqs = np.random.uniform(0.3,0.5,10)
mypop = fp.make_parents(5, allele_freqs, family_name='my_population')
"""
Explanation: Tom Ellis, March 2017
... |
mromanello/SunoikisisDC_NER | participants_notebooks/Sunoikisis - Named Entity Extraction 1a_PG.ipynb | gpl-3.0 | 2 + 3
"""
Explanation: Plan of the lecture
Introduction: Information Extraction and Named Entity Recognition (NER)
NER: definitions and tasks (extraction, classification, disambiguation)
basic programming concepts in Python
Doing NER with existing libraries:
NER from Latin texts with CLTK
NER from journal articles wi... |
Xero-Hige/Notebooks | Algoritmos I/2018-1C/clase-23-03-2018.ipynb | gpl-3.0 | def imprimir_fichas_domino():
''' Imprime las fichas del dominó. '''
for i in range(7):
for j in range(i,7):
print(i,"/",j,end=" | ")
print()
def main():
imprimir_fichas_domino()
main()
"""
Explanation: Práctica Alan - Clase del 23/03/2018
Ejercicio 2.7
Es... |
simpeg/simpegdc | notebooks/DC_schumberger_FWD.ipynb | mit | cs = 25.
npad = 11
hx = [(cs,npad, -1.3),(cs,41),(cs,npad, 1.3)]
hy = [(cs,npad, -1.3),(cs,17),(cs,npad, 1.3)]
hz = [(cs,npad, -1.3),(cs,20)]
mesh = Mesh.TensorMesh([hx, hy, hz], 'CCN')
mesh.plotGrid()
"""
Explanation: DC Forward Modeling of Schlumber array
Here we test the accuracy of DC forward modeling using anal... |
jstac/quantecon_nyu_2016 | lecture14/james_graham_DOLO.ipynb | bsd-3-clause | from dolo import *
import numpy as np
import matplotlib.pyplot as plt
filename = ('https://raw.githubusercontent.com/EconForge/dolo/master/examples/models/rbc.yaml')
pcat(filename) # Print the model file
"""
Explanation: Introducing DOLO
What is DOLO?
A Python-based language to write and solve a variety of econo... |
thom056/ada-parliament-ML | 02-NLP_Sentiment/02-MLOnVotation.ipynb | gpl-2.0 | import pandas as pd
import glob
import os
import numpy as np
from time import time
import logging
import gensim
import bz2
import re
from stop_words import get_stop_words
"""
Explanation: 02. Machine Learning on the Votations
What we aim to perform now is predict the topics that are treated in a Vote, given the short ... |
rahulremanan/python_tutorial | Hacker_Rank/03-Strings/11_Find_a_string.ipynb | mit | s = 'ABCD'
for i in range(0, len(s)):
print (s[i])
string = 'ABCABDEABCF'
sub_string = 'ABC'
string[5:7]
def output_substring(string, sub_string):
for i in range(0, len(string)-len(sub_string)+1):
n = i
print (string[n:(n+len(sub_string))])
output_substring(string, sub_string)
"""
Explanat... |
RajeshThevar/Image-Classification | 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... |
joekasp/ionic_liquids | ionic_liquids/examples/Example_Workflow.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from rdkit import Chem
from rdkit.Chem import AllChem, Descriptors
from rdkit.ML.Descriptors.MoleculeDescriptors import MolecularDescriptorCalculator as Calculator
import pandas as pd
import numpy... |
ananswam/bioscrape | inference examples/Gaussian prior example.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = "retina"
from matplotlib import rcParams
rcParams["savefig.dpi"] = 100
rcParams["figure.dpi"] = 100
rcParams["font.size"] = 20
"""
Explanation: Parameter identification example
Here is a simple toy model that we use to demonstrate the working of the inference p... |
manparvesh/manparvesh.github.io | oldsitejekyll/markdown_generator/publications.ipynb | mit | !cat publications.tsv
"""
Explanation: Publications markdown generator for academicpages
Takes a TSV of publications with metadata and converts them for use with academicpages.github.io. This is an interactive Jupyter notebook (see more info here). The core python code is also in publications.py. Run either from the m... |
NREL/bifacial_radiance | docs/tutorials/19 - Example Simulation - East West Sheds.ipynb | bsd-3-clause | import os
import numpy as np
import pandas as pd
from pathlib import Path
import bifacial_radiance
bifacial_radiance.__version__
testfolder = testfolder = str(Path().resolve().parent.parent / 'bifacial_radiance' / 'Tutorial_01')
if not os.path.exists(testfolder):
os.makedirs(testfolder)
demo = bifacial_radiance.... |
zhouqifanbdh/liupengyuan.github.io | 201621198175.ipynb | mit | name = input("请输入您的姓名：")
date = float(input("请输入您出生的月份.日期："))
if 3.21 <= date <= 4.19:
print(name,"，你是非常有性格的白羊座！")
elif 4.20 <= date <= 5.20:
print(name,"，你是非常有性格的金牛座！")
elif 5.21 <= date <= 6.21:
print(name,"，你是非常有性格的双子座！")
elif 6.22 <= date <= 7.22:
print(name,"，你是非常有性格的巨蟹座！")
elif 7.23 <= date <= 8.... |
intel-analytics/analytics-zoo | docs/docs/colab-notebook/orca/quickstart/autoestimator_pytorch_lenet_mnist.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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed un... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_ml/td2a_timeseries_correction.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
"""
Explanation: 2A.ml - Séries temporelles - correction
Prédictions sur des séries temporelles.
End of explanation
"""
import pandas
data = pandas.read_csv("xavierdupre_sessions.csv", sep="\t")
data.set_index("Date", inplace=True)
d... |
evanmiltenburg/python-for-text-analysis | Chapters-colab/Chapter_11_Functions_and_scope.ipynb | apache-2.0 | %%capture
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Data.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/images.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Extra_Material.zip
!unzip Data.zip -d ../
!unzip images.zip -d ./
!unzip Ext... |
JohnGriffiths/ConWhAt | docs/examples/exploring_conwhat_atlases.ipynb | bsd-3-clause | # ConWhAt stuff
from conwhat import VolConnAtlas,StreamConnAtlas,VolTractAtlas,StreamTractAtlas
from conwhat.viz.volume import plot_vol_scatter,plot_vol_and_rois_nilearn
# Neuroimaging stuff
import nibabel as nib
from nilearn.plotting import plot_stat_map,plot_surf_roi
# Viz stuff
%matplotlib inline
from matplotlib i... |
birdsarah/bokeh-miscellany | 0.12.14 bugs - test in 0.12.5.ipynb | gpl-2.0 | N = 10000
x = np.random.normal(0, np.pi, N)
y = np.sin(x) + np.random.normal(0, 0.2, N)
p = figure(webgl=True)
p.scatter(x, y, alpha=0.1)
show(p)
"""
Explanation: WebGL problems
Drag around canvas is shifted down, cut off at top spilling over bottom.
Bad in 0.12.14 and 0.12.15dev3
Good in 0.12.10
End of explanation
... |
exa-analytics/atomic | docs/source/notebooks/01_basics.ipynb | apache-2.0 | import exatomic
exatomic.__version__
"""
Explanation: Welcome to exatomic
This notebook demonstrates some basics of working with exatomic.
End of explanation
"""
exatomic.Universe?
"""
Explanation: Getting help in the Jupyter notebook is easy, just put a "?" after a class or function.
Don't forget to use tab to hel... |
mjuenema/ipython-notebooks | dnspython-resolver.ipynb | bsd-2-clause | import dns.rdataclass
dns.rdataclass.IN
"""
Explanation: dnspython Resolver
The socket module of the Python standard library provides basic functions for resolving hostnames (gethostbyname and gethostbyname_ex) as implemented by the C library. The resolver of the dnspython allows to bypass the C library and query DNS ... |
Benedicto/ML-Learning | Clustering_0_nearest-neighbors-features-and-metrics_blank.ipynb | gpl-3.0 | import graphlab
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
"""
Explanation: Nearest Neighbors
When exploring a large set of documents -- such as Wikipedia, news articles, StackOverflow, etc. -- it can be useful to get a list of related material. To find relevant documents you typically
* Dec... |
saketkc/hatex | 2015_Fall/MATH-578B/Homework3/Homework3.ipynb | mit | ### Simulation
%matplotlib inline
from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1)
import math
N=1000
s=0
def R(x,y):
return math.sqrt(x*x+y*y)
for i in range(N):
r=-100
y=0
x=0
while R(x,y)>r:
S=np.random.uniform(size=2)
x=S[0]
... |
erickpeirson/statistical-computing | Markov Chain Monte Carlo.ipynb | cc0-1.0 | %matplotlib inline
import random
import math
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from scipy.stats import norm, uniform, multivariate_normal
"""
Explanation: Chapter 1 - Markov Chain Monte Carlo
End of explanation
"""
U = [random.random() for i in xrange(10000)]
"""
Explanati... |
mne-tools/mne-tools.github.io | 0.17/_downloads/f79b821209d128d6d63d736e8cc0beb3/plot_fdr_stats_evoked.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.stats import bonferroni_correction, fdr_correction
print(__doc__)
"""
... |
ocefpaf/folium | examples/plugin-Search.ipynb | mit | import geopandas
states = geopandas.read_file(
"https://rawcdn.githack.com/PublicaMundi/MappingAPI/main/data/geojson/us-states.json",
driver="GeoJSON",
)
cities = geopandas.read_file(
"https://d2ad6b4ur7yvpq.cloudfront.net/naturalearth-3.3.0/ne_50m_populated_places_simple.geojson",
driver="GeoJSON",
... |
McIntyre-Lab/ipython-demo | hdf5.ipynb | gpl-2.0 | # Import packages
import numpy as np
import tables as pt # PyTables
import h5py as hp # h5py
import pandas as pd
import rpy2
%load_ext rpy2.ipython
# Create a New HDF5 File
h5file = pt.open_file('test.h5', mode='w', title='Test file')
"""
Explanation: HDF5
HDF5 stands for (Hierarchical Data Format 5), and it is... |
tpin3694/tpin3694.github.io | regex/match_any_of_series_of_characters.ipynb | mit | # Load regex package
import re
"""
Explanation: Title: Match Any Of A Series Of Options
Slug: match_any_of_series_of_characters
Summary: Match Any Of A Series Of Options
Date: 2016-05-01 12:00
Category: Regex
Tags: Basics
Authors: Chris Albon
Based on: Regular Expressions Cookbook
Preliminaries
End of explanation
""... |
tensorflow/docs-l10n | site/ja/tutorials/load_data/pandas_dataframe.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... |
samuxiii/notebooks | houses/House Prices.ipynb | apache-2.0 | import numpy as np
import pandas as pd
#load the files
train = pd.read_csv('input/train.csv')
test = pd.read_csv('input/test.csv')
data = pd.concat([train, test])
#size of training dataset
train_samples = train.shape[0]
#print some of them
data.head()
# remove the Id feature
data.drop(['Id'],1, inplace=True);
data... |
abhinavsingh/proxy.py | tutorial/http_parser.ipynb | bsd-3-clause | from proxy.http.methods import httpMethods
from proxy.http.parser import HttpParser, httpParserTypes, httpParserStates
from proxy.common.constants import HTTP_1_1
get_request = HttpParser(httpParserTypes.REQUEST_PARSER)
get_request.parse(memoryview(b'GET / HTTP/1.1\r\nHost: jaxl.com\r\n\r\n'))
print(get_request.build... |
jpn--/larch | book/example/017_mnl_final.ipynb | gpl-3.0 | # TEST
import larch.numba as lx
import larch
import pandas as pd
pd.set_option("display.max_columns", 999)
pd.set_option('expand_frame_repr', False)
pd.set_option('display.precision', 3)
larch._doctest_mode_ = True
"""
Explanation: 17: MTC Expanded MNL Mode Choice
End of explanation
"""
import larch.numba as lx
d = ... |
Yu-Group/scikit-learn-sandbox | jupyter/backup_deprecated_nbs/14_Check_Utils_pep8.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.datasets import load_breast_cancer
import numpy as np
from functools import reduce
# Import our custom utilities
from imp import reload
from utils import irf_jupyter_utils
from utils import irf_utils
reload(irf_jupy... |
USCDataScience/parser-indexer-py | notebooks/all-ner/MTE_NER.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
from snorkel import SnorkelSession
import os
import numpy as np
import re, string
import codecs
# Open Session
session = SnorkelSession()
"""
Explanation: NER using Data Programming
Project Mars Target Encyclopedia
This notebook does not explain much, however, th... |
SunPower/pvfactors | docs/tutorials/Account_for_AOI_losses.ipynb | bsd-3-clause | # Import external libraries
import os
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
import pandas as pd
import warnings
# Settings
%matplotlib inline
np.set_printoptions(precision=3, linewidth=300)
warnings.filterwarnings('ignore')
plt.style.use('seaborn-whitegrid')
plt.rcParams.upda... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/04_features/labs/a_features.ipynb | apache-2.0 | import math
import shutil
import numpy as np
import pandas as pd
import tensorflow as tf
print(tf.__version__)
tf.logging.set_verbosity(tf.logging.INFO)
pd.options.display.max_rows = 10
pd.options.display.float_format = '{:.1f}'.format
"""
Explanation: Trying out features
Learning Objectives:
* Improve the accuracy... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/text_classification/labs/custom_tf_hub_word_embedding.ipynb | apache-2.0 | !pip freeze | grep tensorflow-hub==0.7.0 || pip install tensorflow-hub==0.7.0
import os
import tensorflow as tf
import tensorflow_hub as hub
"""
Explanation: Custom TF-Hub Word Embedding with text2hub
Learning Objectives:
1. Learn how to deploy AI Hub Kubeflow pipeline
1. Learn how to configure the run parameter... |
ANTsX/ANTsPy | docs/other/ANTsPy Tutorial.ipynb | apache-2.0 | import ants
import matplotlib.pyplot as plt
%matplotlib inline
img = ants.image_read( ants.get_ants_data('r16'), 'float' )
plt.imshow(img.numpy(), cmap='Greys_r')
plt.show()
mask = ants.get_mask(img)
plt.imshow(mask.numpy())
plt.show()
"""
Explanation: ANTsPy Tutorial
In this tutorial, I will show of some of the co... |
jforbess/pvlib-python | docs/tutorials/tmy_and_diffuse_irrad_models.ipynb | bsd-3-clause | # built-in python modules
import os
import inspect
# scientific python add-ons
import numpy as np
import pandas as pd
# plotting stuff
# first line makes the plots appear in the notebook
%matplotlib inline
import matplotlib.pyplot as plt
# seaborn makes your plots look better
try:
import seaborn as sns
sns.s... |
YeEmrick/learning | cs231/assignment/assignment2/.ipynb_checkpoints/BatchNormalization-checkpoint.ipynb | apache-2.0 | # As usual, a bit of setup
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solver import Solver
%matplotlib inline
... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a/pandas_iterator_correction.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
from sklearn.datasets import load_iris
data = load_iris()
import pandas
df = pandas.DataFrame(data.data)
df.column = "X1 X2 X3 X4".split()
df["target"] = data.target
df.head(n=2)
"""
Explanation: 2A.data - Pandas et itérateurs - correction
pandas a tenda... |
jasonarita/Kaggle-Titanic-R-Python | 0-Basic Model-All Survive or Die.ipynb | mit | import csv as csv
import numpy as np
"""
Explanation: The Most Basic-est Model of Them All
They all survive
End of explanation
"""
test_file = open('./data/test.csv', 'rb') # Open the test data
test_file_object = csv.reader(test_file)
header = test_file_object.next()
header
"""
Explanation: The op... |
bspalding/research_public | lectures/drafts/Measures of Dispersion.ipynb | apache-2.0 | import numpy as np
import math
np.random.seed(121)
X = np.sort(np.random.randint(100, size=20))
print 'X:', X
mu = np.mean(X)
print 'Mean of X:', mu
"""
Explanation: Dispersion
By Evgenia "Jenny" Nitishinskaya and Delaney Granizo-Mackenzie
Notebook released under the Creative Commons Attribution 4.0 License.
Dispers... |
Cyb3rWard0g/ThreatHunter-Playbook | docs/notebooks/windows/05_defense_evasion/WIN-190510202010.ipynb | gpl-3.0 | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: WDigest Downgrade
Metadata
| | |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2019/05/10 |
| modification date | 2020/09/20 |
| playbook related | [] |
Hypothesis
A... |
cogitare-ai/cogitare | docs/source/quickstart.ipynb | mit | # adapted from https://github.com/pytorch/examples/blob/master/mnist/main.py
from cogitare import Model
from cogitare import utils
from cogitare.data import DataSet, AsyncDataLoader
from cogitare.plugins import EarlyStopping
from cogitare.metrics.classification import accuracy
import cogitare
import torch.nn as nn
imp... |
landlab/landlab | notebooks/tutorials/overland_flow/kinwave_implicit/kinwave_implicit_overland_flow.ipynb | mit | import copy
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from landlab import RasterModelGrid, imshow_grid
from landlab.components.overland_flow import KinwaveImplicitOverlandFlow
from landlab.io.esri_ascii import read_esri_ascii
print(KinwaveImplicitOverlandFlow.__doc__)
"""
Explanation... |
OpenSourceBrain/IzhikevichModel | numba/faster_izhikevich_model.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import collections
import quantities as pq
import izhikevich as izhi
import numpy as np
%matplotlib inline
from utils import reduced_cells, transform_input, plot_model
DELAY = 0*pq.ms
DURATION = 250 *pq.ms
"""
Explanation: This is a reproduction of the MATLAB script 2007.m
The code is i... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session07/Day2/Clustering-Astronomical-Sources.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import glob
import os
from time import time
from matplotlib.pyplot import imshow
from matplotlib.image import imread
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn import metrics
from sk... |
tpin3694/tpin3694.github.io | machine-learning/model_selection_using_grid_search.ipynb | mit | # Load libraries
import numpy as np
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
# Set random seed
np.random.seed(0)
"""
Explanation: Title: Mo... |
marisanest/content-management | Assignment01/Assignment01.ipynb | apache-2.0 | import csv
import re
import pandas as pd
from pandas import *
import numpy
from numpy import *
import math
%matplotlib inline
import matplotlib.pyplot as plt
from random import randint
"""
Explanation: Graded Assignment 01: Titanic: Machine Learning from Disaster
2017-05-17
(c) Marisa Nest 2017
Imports
End of explanat... |
tensorflow/docs | site/en/tutorials/images/classification_with_model_garden.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... |
tpin3694/tpin3694.github.io | machine-learning/select_best_number_of_components_in_lda.ipynb | mit | # Load libraries
from sklearn import datasets
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
"""
Explanation: Title: Selecting The Best Number Of Components For LDA
Slug: select_best_number_of_components_in_lda
Summary: How to select the best number of components for linear discriminant analysi... |
davofis/computational_seismology | 07_spectral_elements/se_homo_1d_solution.ipynb | gpl-3.0 | # Import all necessary libraries, this is a configuration step for the exercise.
# Please run it before the simulation code!
import numpy as np
import matplotlib.pyplot as plt
from gll import gll
from lagrange1st import lagrange1st
from ricker import ricker
# Show the plots in the Notebook.
plt.switch_backend("nbagg... |
PDBeurope/PDBe_Programming | search_interface/notebooks/search_facets.ipynb | apache-2.0 | from mysolr import Solr
PDBE_SOLR_URL = "http://wwwdev.ebi.ac.uk/pdbe/search/pdb"
solr = Solr(PDBE_SOLR_URL, version=4)
UNLIMITED_ROWS = 10000000 # necessary because default in mysolr is mere 10
import logging, sys
#reload(logging) # reload is just a hack to make logging work in the notebook, it's usually unnecessary... |
ES-DOC/esdoc-jupyterhub | notebooks/bcc/cmip6/models/bcc-esm1/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bcc', 'bcc-esm1', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: BCC
Source ID: BCC-ESM1
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radiativ... |
adukic/nd101 | autoencoder/Convolutional_Autoencoder_Solution.ipynb | mit | %matplotlib inline
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', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
"""
Explanation: C... |
dkillick/courses | course_content/notebooks/numpy_intro.ipynb | gpl-3.0 | # numpy is generally imported as 'np'
import numpy as np
print(np)
print(np.__version__)
"""
Explanation: A Workshop Introduction to NumPy
The Python language is an excellent tool for general-purpose programming, with a highly readable syntax, rich and powerful data types (strings, lists, sets, dictionaries, arbitrary... |
ClementPhil/deep-learning | first-neural-network/dlnd-your-first-neural-network.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
ESSS/notebooks | interpolation_to_a_structured_grid_from_a_cloud_of_points.ipynb | mit | # Imports
import math
import seaborn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
from sklearn.neighbors import NearestNeighbors
from sklearn.preprocessing import normalize
"""
Explanation: Interpolation to a structured grid from a cloud of points
First of all, we ha... |
AllenDowney/ThinkStats2 | code/chap12ex.ipynb | gpl-3.0 | from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve(url, filename)
print("Downloaded " + local)
download("https://github.com/AllenDowney/ThinkStats2/raw/master/code/th... |
IBMDecisionOptimization/docplex-examples | examples/cp/jupyter/truck_fleet.ipynb | apache-2.0 | from sys import stdout
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: The Truck Fleet puzzle
This tutorial includes everything you need to set up decision optimization e... |
davicsilva/dsintensive | notebooks/eda-miniprojects/human_temp/sliderule_dsi_inferential_statistics_exercise_1.ipynb | apache-2.0 | import pandas as pd
df = pd.read_csv('data/human_body_temperature.csv')
# Your work here.
# Load Matplotlib + Seaborn and SciPy libraries
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import stats
from scipy.stats import norm
from statsmodels.stats.weightstats import ztest
%matp... |
tensorflow/docs-l10n | site/ko/guide/function.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... |
josh-gree/maths-with-python | 05-classes-oop.ipynb | mit | p_normal = (12, -14, 0, 2)
"""
Explanation: Classes and Object Oriented Programming
We have looked at functions which take input and return output (or do things to the input). However, sometimes it is useful to think about objects first rather than the actions applied to them.
Think about a polynomial, such as the cub... |
SamLau95/nbinteract | docs/notebooks/recipes/recipes_layout.ipynb | bsd-3-clause | df_interact(videos)
# nbi:left
options = {
'title': 'Views for Trending Videos',
'xlabel': 'Date Trending',
'ylabel': 'Views',
'animation_duration': 500,
'aspect_ratio': 1.0,
}
def xs(channel):
return videos.loc[videos['channel_title'] == channel].index
def ys(xs):
return videos.loc[xs, '... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session11/Day3/PSFphotometrySolutions.ipynb | mit | import numpy as np
from astropy.io import fits
import matplotlib.pyplot as plt
import astropy.convolution
import pandas as pd
f = fits.open("calexp-0527247_10.fits")
image = f[1].data
"""
Explanation: PSF Photometry
Version 0.1
We're going to try to piece together the different elements of a PSF photometry pipeline ... |
satishgoda/learning | python/libs/rxpy/support/A Decision Tree of Observable Operators. Part I - Creation.ipynb | mit | reset_start_time(O.just)
stream = O.just({'answer': rand()})
disposable = subs(stream)
sleep(0.5)
disposable = subs(stream) # same answer
# all stream ops work, its a real stream:
disposable = subs(stream.map(lambda x: x.get('answer', 0) * 2))
"""
Explanation: A Decision Tree of Observable Operators
Part 1: NEW Observ... |
ComputationalModeling/spring-2017-danielak | past-semesters/spring_2016/homework_assignments/Homework_6.ipynb | agpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import math
def calc_accel(velocity, use_resistance=True):
'''
This function calculates and returns the acceleration vector
from a launched t-shirt, given an input velocity. Optionally,
you can turn on and off air resistance for c... |
metpy/MetPy | v0.10/_downloads/0fad3c70b425eaed875fe7cd5ea738b8/Advanced_Sounding.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, SkewT
from metpy.units import units
"""
Explanation: Advanced Sounding
Plot a sounding using MetPy with more advanced features.
Beyond just p... |
fdmazzone/Ecuaciones_Diferenciales | Teoria_Basica/scripts/EjerciciosGruposLie.ipynb | gpl-2.0 | from sympy import *
init_printing()
x,epsilon=symbols('x,epsilon')
y=Function('y')(x)
x1=x*(x+y)/(y+(1+epsilon)*x)
y1=(epsilon*x+y)*(x+y)/(y+(1+epsilon)*x)
exp1=y1.diff(x)/x1.diff(x)
exp2=exp1.subs(y.diff(x),(x**2*sin(x+y)+y)/x/(1-x*sin(x+y)))
x1,y1=symbols('x1,y1')
exp2=exp2.simplify()
exp3=exp2.subs({y:(-epsilon*x1+y... |
LorenzoBi/courses | UQ/assignment_3/Assignment 3.ipynb | mit | import numpy as np
from scipy.special import binom
import matplotlib.pylab as plt
from scipy.misc import factorial as fact
%matplotlib inline
def binomial(p, n, k):
return binom(n, k) * p ** k * (1 - p) ** (n-k)
"""
Explanation: Lorenzo Biasi and Michael Aichmüller
End of explanation
"""
p = 1. / 365
1 - np.su... |
Upward-Spiral-Science/team1 | code/Assignment11_Akash.ipynb | apache-2.0 | from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import urllib2
import scipy.stats as stats
np.set_printoptions(precision=3, suppress=True)
url = ('https://raw.githubusercontent.com/Upward-Spiral-Science'
'/data/master/syn-density/output.csv')
data =... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_artifacts_correction_ica.ipynb | bsd-3-clause | import numpy as np
import mne
from mne.datasets import sample
from mne.preprocessing import ICA
from mne.preprocessing import create_eog_epochs, create_ecg_epochs
# 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(... |
idekerlab/deep-cell | data-builder/tree_generator-clixo-final.ipynb | mit | # Load data sets
import pandas as pd
treeSourceUrl = './data/preds_yeastnet_no_gi_0.04_0.5.txt.propagate.small_parent_tree'
geneCountFile = './data/preds_yeastnet_no_gi_0.04_0.5.txt.propagate.term_sizes'
alignmentFile = './data/alignments_FDR_0.1_t_0.1'
geneAssignment = './data/preds_yeastnet_no_gi_0.04_0.5.txt.propag... |
ML4DS/ML4all | C1.Intro_Classification/Intro_Classification_student.ipynb | mit | # To visualize plots in the notebook
%matplotlib inline
# Import some libraries that will be necessary for working with data and displaying plots
import csv # To read csv files
import random
import matplotlib.pyplot as plt
import numpy as np
from scipy import spatial
from sklearn import neighbors, datasets
"""
E... |
dcavar/python-tutorial-for-ipython | notebooks/Python Word Sense Disambiguation.ipynb | apache-2.0 | from nltk.corpus import wordnet
"""
Explanation: Python Word Sense Disambiguation
(C) 2017-2019 by Damir Cavar
Version: 1.2, November 2019
License: Creative Commons Attribution-ShareAlike 4.0 International License (CA BY-SA 4.0)
This is a tutorial related to the discussion of a WordSense disambiguation and various mac... |
akront1104/World_Bank_Data | sliderule_dsi_json_exercise.ipynb | mit | import pandas as pd
"""
Explanation: JSON examples and exercise
get familiar with packages for dealing with JSON
study examples with JSON strings and files
work on exercise to be completed and submitted
reference: http://pandas.pydata.org/pandas-docs/stable/io.html#io-json-reader
data source: http://jsonstudio.... |
kubeflow/pipelines | samples/tutorials/gpu/gpu.ipynb | apache-2.0 | import kfp
from kfp import dsl
def gpu_smoking_check_op():
return dsl.ContainerOp(
name='check',
image='tensorflow/tensorflow:latest-gpu',
command=['sh', '-c'],
arguments=['nvidia-smi']
).set_gpu_limit(1)
@dsl.pipeline(
name='GPU smoke check',
description='smoke check a... |
kimkipyo/dss_git_kkp | Python 복습/11일차.목_Pandas/11일차_3T_ajax 로 이루어진 select, option 정보 가져오기 ( feat, 건강보험심사평가원 ).ipynb | mit | import requests
from bs4 import BeautifulSoup #requests한 것을 parsiing하기 위해서
response = requests.get("http://www.hira.or.kr/re/diag/getDiagAmtList.do")
dom = BeautifulSoup(response.text, "html.parser")
dom.select_one("#sidoCd")
select_element = dom.select_one("#sidoCd")
print(select_element)
"""
Explanation: 3T_aj... |
sdpython/pyquickhelper | _unittests/ut_helpgen/data_gallery/notebooks/2a/notebook_convert.ipynb | mit | %%javascript
var kernel = IPython.notebook.kernel;
var body = document.body,
attribs = body.attributes;
var command = "theNotebook = " + "'"+attribs['data-notebook-name'].value+"'";
kernel.execute(command);
if "theNotebook" in locals():
a=theNotebook
else:
a="pas trouvé"
a
"""
Explanation: Convert a not... |
mne-tools/mne-tools.github.io | 0.17/_downloads/af4923da095ff8767e419fa9e705bbba/plot_dipole_orientations.ipynb | bsd-3-clause | from mayavi import mlab
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
data_path = sample.data_path()
evokeds = mne.read_evokeds(data_path + '/MEG/sample/sample_audvis-ave.fif')
left_auditory = evokeds[0].apply_baseline()
fwd = mne.read_forward_solution(
... |
ctuning/ck-math | script/explore-clblast-matrix-size/clblast-distribution-tuner-sizes-analysis.ipynb | bsd-3-clause | import os
import sys
import json
import re
"""
Explanation: [PUBLIC] Analysis of CLBlast tuning
<a id="overview"></a>
Overview
This Jupyter Notebook analyses the performance that CLBlast achieves across a range of routines, sizes and configurations.
Run first clblast-tuning-benchmarking.py
<a id="data"></a>
Get the e... |
abevieiramota/data-science-cookbook | 2017/05-naive-bayes/Naive_Bayes_Tutorial_01.ipynb | mit | import csv
def loadCsv(filename):
lines = csv.reader(open(filename, "r"))
dataset = list(lines)
for i in range(len(dataset)):
dataset[i] = [float(x) for x in dataset[i]]
return dataset
"""
Explanation: Naive Bayes
Introdução
Neste tutorial iremos apresentar a implentação do algoritmo Naive Ba... |
rebeccabilbro/rebeccabilbro.github.io | _drafts/mushroom_tutorial_reboot.ipynb | mit | from yellowbrick.datasets import load_mushroom
X, y = load_mushroom()
print(X[:5]) # inspect the first five rows
"""
Explanation: Model Selection Tutorial with Yellowbrick
In this tutorial, we are going to look at scores for a variety of scikit-learn models and compare them using visual diagnostic tools from Yellowbr... |
reece/ga4gh-examples | nb/Search VariantAnnotations using SO term sets.ipynb | apache-2.0 | import itertools
import pprint
import re
from IPython.display import HTML, display
import ga4gh.client
import prettytable
import requests
print(ga4gh.__version__)
gc = ga4gh.client.HttpClient("http://localhost:8000")
region_constraints = dict(referenceName="1", start=0, end=int(1e10))
variant_set_id = 'YnJjYTE6T1I... |
Vvkmnn/books | AutomateTheBoringStuffWithPython/lesson39.ipynb | gpl-3.0 | # Test the requests module by importing it
import requests
# Store a website url in a response object that can be queried
res = requests.get('https://automatetheboringstuff.com/files/rj.txt')
"""
Explanation: Lesson 39:
Downloading from the Web with the Requests Module
The requests module lets you easily download fil... |
zach-hartwig/IPyLogbook | mgmt/IPyLogbookExtensions.ipynb | gpl-3.0 | # Enable Python variables to by inserted into Markdown cells via the "{{}}" syntax
use_python_markdown = True
# Enable cells to be 'read-only' via 'lock' click button up above-right
use_read_only = True
# Enable all input cells to be hidden via ' bars'click button above-right
use_hide_input_all = True
# Enable png/j... |
rflamary/POT | docs/source/auto_examples/plot_otda_color_images.ipynb | mit | # Authors: Remi Flamary <remi.flamary@unice.fr>
# Stanislas Chambon <stan.chambon@gmail.com>
#
# License: MIT License
import numpy as np
from scipy import ndimage
import matplotlib.pylab as pl
import ot
r = np.random.RandomState(42)
def im2mat(I):
"""Converts an image to matrix (one pixel per line)"""... |
ml-ensemble/ml-ensemble.github.io | info/_downloads/parallel.ipynb | mit | from mlens.parallel import ParallelProcessing, Job, Learner
from mlens.index import FoldIndex
from mlens.utils.dummy import OLS
import numpy as np
np.random.seed(2)
X = np.arange(20).reshape(10, 2)
y = np.random.rand(10)
indexer = FoldIndex(folds=2)
learner = Learner(estimator=OLS(),
indexer=index... |
jhaip/livedata-mqtt | notebooks/D3 MQTT 9DOF.ipynb | mit | from IPython.core.display import display, HTML
from string import Template
import pandas as pd
import json, random
%%javascript
require.config({paths: {d3: "https://d3js.org/d3.v4.min"}});
require(['d3'], function(d3) {
window.d3 = d3;
})
html_template = Template('''
<svg id="graph-div"></div>
<script> $js_text <... |
leizhipeng/ml | titanic_survival_exploration/titanic_survival_exploration.ipynb | gpl-3.0 | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the dataset
in_file... |
pagutierrez/tutorial-sklearn | notebooks-spanish/05-aprendizaje_supervisado_clasificacion.ipynb | cc0-1.0 | from sklearn.datasets import make_blobs
X, y = make_blobs(centers=2, random_state=0)
print('X ~ n_samples x n_features:', X.shape)
print('y ~ n_samples:', y.shape)
print('\n5 primeros ejemplos:\n', X[:5, :])
print('\n5 primeras etiquetas:', y[:5])
"""
Explanation: Aprendizaje supervisado parte 1 -- Clasificación
Pa... |
elastic/examples | Machine Learning/Query Optimization/notebooks/2 - Query tuning - best_fields.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
import importlib
import os
import sys
from elasticsearch import Elasticsearch
from skopt.plots import plot_objective
# project library
sys.path.insert(0, os.path.abspath('..'))
import qopt
importlib.reload(qopt)
from qopt.notebooks import evaluate_mrr100_dev, optimize_query_mrr10... |
DJCordhose/ai | notebooks/rl/berater-v8.ipynb | mit | !pip install git+https://github.com/openai/baselines >/dev/null
!pip install gym >/dev/null
"""
Explanation: <a href="https://colab.research.google.com/github/DJCordhose/ai/blob/master/notebooks/rl/berater-v8.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab... |
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