repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
keras-team/keras-io | examples/vision/ipynb/deit.ipynb | apache-2.0 | from typing import List
import tensorflow as tf
import tensorflow_addons as tfa
import tensorflow_datasets as tfds
import tensorflow_hub as hub
from tensorflow import keras
from tensorflow.keras import layers
tfds.disable_progress_bar()
tf.keras.utils.set_random_seed(42)
"""
Explanation: Distilling Vision Transforme... |
YaleDHLab/lab-workshops | beautifulsoup/intro-to-html-parsing.ipynb | mit | !pip install requests
"""
Explanation: Introduction to HTML Parsing with Python
The web has vast troves of data, but to use that data in a machine learning application, it must first be collected and parsed. This workshop aims to show you how to accomplish both of these feats. By the end of this notebook, you will hav... |
tiagoft/inteligencia_computacional | classificador_regras.ipynb | mit | %matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
"""
Explanation: Classificação por Regras Pré-Definidas
O problema com o qual vamos lidar é o de classificar automaticamente elementos de um conjunto através de suas características mensuráveis. Trata-se, assim, do problema de observar element... |
tensorflow/workshops | extras/amld/notebooks/exercises/1_data.ipynb | apache-2.0 | data_path = '/content/gdrive/My Drive/amld_data'
# Alternatively, you can also store the data in a local directory. This method
# will also work when running the notebook in Jupyter instead of Colab.
# data_path = './amld_data
if data_path.startswith('/content/gdrive/'):
from google.colab import drive
assert data_... |
VictorQuintana91/Thesis | notebooks/001_data_normalisation.ipynb | mit | def parse(path):
g = gzip.open(path, 'rb')
for l in g:
yield eval(l)
def getDF(path):
i = 0
df = {}
for d in parse(path):
df[i] = d
i += 1
return pd.DataFrame.from_dict(df, orient='index')
df = getDF('/Users/falehalrashidi/Downloads/reviews_Books_5.json.gz')
df.head()
df1 = df[['reviewerID',... |
google/nitroml | examples/nitroml_kubeflow.ipynb | apache-2.0 | import sys
# install kfp (https://kubeflow-pipelines.readthedocs.io/en/latest/source/kfp.html)
!{sys.executable} -m pip install --user --upgrade -q kfp==1.0.0
!{sys.executable} -m pip install --user --upgrade -q kfp-server-api==1.0.0
# Download skaffold and set it executable.
# !curl -Lo skaffold https://storage.goog... |
koverholt/notebooks | fire-incidents/fire-incidents.ipynb | bsd-3-clause | import pandas as pd
%matplotlib inline
pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_columns', 1000)
df = pd.read_csv('fire-incidents.csv')
df.head(3)
df.shape
"""
Explanation: Data from http://catalog.data.gov/dataset/baton-rouge-fire-incidents
End of explanation
"""
df.columns
df['DISPAT... |
johanfrisk/Python_at_web | notebooks/networked_programs.ipynb | mit | # Python built in support for TCP sockets
import socket
# this just opens a 'porthole' out from my computer
mysock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# this connects me to the other computer
mysock.connect(('www.py4inf.com', 80))
"""
Explanation: These are my notes on networked programs
End of expla... |
ngast/rmf_tool | examples/Example_2choice.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
"""
Explanation: This document demonstrate how to use the library to define a "density dependent population process" and to compute its mean-... |
tensorflow/docs-l10n | site/ja/federated/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... |
mmckerns/tuthpc | kocham.ipynb | bsd-3-clause | $ unzip kocham.zip
$ cd kocham
$ python setup.py install
"""
a toy password cracker
"""
import time
import itertools
from multiprocess.dummy import Pool
import kocham.imap as imap
import kocham.corpus as corpus
stopwords = corpus.stopwords
ipassword = corpus.ipassword
compare = imap.login
# turn on verbosity
corpus.V... |
mne-tools/mne-tools.github.io | 0.21/_downloads/142c866d928b3d3a3a76c80e0ef4ea81/plot_rereference_eeg.ipynb | bsd-3-clause | # Authors: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
from matplotlib import pyplot as plt
print(__doc__)
# Setup for reading the raw data
data_path = sample.data_path()
raw_fname = data_... |
macks22/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:... |
tpin3694/tpin3694.github.io | machine-learning/random_forest_classifier.ipynb | mit | # Load libraries
from sklearn.ensemble import RandomForestClassifier
from sklearn import datasets
"""
Explanation: Title: Random Forest Classifier
Slug: random_forest_classifier
Summary: Training a random forest classifier in scikit-learn.
Date: 2017-09-21 12:00
Category: Machine Learning
Tags: Trees And Forests
Autho... |
nicjhan/MOM6-examples | ocean_only/flow_downslope/Understanding native output data from MOM6.ipynb | gpl-3.0 | %pylab inline
import scipy.io.netcdf
"""
Explanation: This "flow downslope" example involves four sub-directories, layer, rho, sigma and z, in which the model is running in one of four coordinate configurations. To use this notebook it is assumed you have run each of those experiments in place and have kept the outpu... |
scotthuang1989/Python-3-Module-of-the-Week | text/re.ipynb | apache-2.0 | import re
pattern = 'text'
text = 'Does this text match the pattern?'
match = re.search(pattern, text)
s = match.start()
e = match.end()
print('Found "{}"\n in "{}"\n from {} to {} ("{}")'.format(
match.re.pattern, match.string, s, e, text[s:e]))
"""
Explanation: Regular expressions are text matching patterns ... |
xdze2/thermique_appart | testweek_get_data.ipynb | mit | coords_grenoble = (45.1973288, 5.7139923)
startday = pd.to_datetime('12/07/2017', format='%d/%m/%Y').tz_localize('Europe/Paris')
lastday = pd.to_datetime('24/07/2017', format='%d/%m/%Y').tz_localize('Europe/Paris')
"""
Explanation: Téléchargement des données et premier traitement
End of explanation
"""
# routine po... |
mmathioudakis/web_browsing | browsing_history.ipynb | gpl-2.0 | %%bash
cp ~/Library/Safari/History.db ~/Workspace/web_browsing/hs.db
"""
Explanation: Part 1: Retrieving our Safari Browsing History
To access our browsing history, we go to ~/Library/Safari and look for the database History.db. We make a copy of it in a folder in our workspace, e.g. to ~/Workspace/web_browsing/hs.db.... |
bcantarel/bcantarel.github.io | bicf_nanocourses/courses/ML_1/exercises/PGM.ipynb | gpl-3.0 | from scipy.stats import beta
α = 1
colors = sns.palettes.color_palette("Blues",15)[5:]
for i in range(10):
β = 0.5*(i+1)
x = np.linspace(1e-2, 1-1e-2, 1e4)
_ = sns.plt.plot(x, beta.pdf(x, α, β), color=colors[i],
lw=2, alpha=0.6, label='')
"""
Explanation: Probability Primer
Distributi... |
Kevogich/Mercedes-Benz-Test-Bench-Kaggle- | BenzDatasetAnalysis.ipynb | mit | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import preprocessing
import xgboost as xgb
color = sns.color_palette()
%matplotlib inline
pd.options.mode.chained_assignment = None # default='... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/end_to_end_ml/solutions/preproc.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install --user google-cloud-bigquery==1.25.0
"""
Explanation: <h1> Preprocessing using Dataflow </h1>
This notebook illustrates:
<ol>
<li> Creating datasets for Machine Learning using Dataflow
</ol>
<p>
While Pandas is fine for experimenting, fo... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_compute_raw_data_spectrum.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io, read_proj, read_selection
from mne... |
kingmolnar/DataScienceProgramming | 03-NumPy-and-Linear-Algebra/Introduction_class.ipynb | cc0-1.0 | %matplotlib inline
import math
import numpy as np
import matplotlib.pyplot as plt
##import seaborn as sbn
##from scipy import *
"""
Explanation: Introduction to NumPy
Topics
Basic Synatx
creating vectors matrices
special: ones, zeros, identity eye
add, product, inverse
Mechanics: indexing, slicing, concatenating, r... |
neildhir/DCBO | notebooks/ind_scm.ipynb | mit | %load_ext autoreload
%autoreload 2
import sys
sys.path.append("../src/")
sys.path.append("..")
from src.examples.example_setups import setup_ind_scm
from src.utils.sem_utils.toy_sems import StationaryIndependentSEM as IndSEM
from src.utils.sem_utils.sem_estimate import build_sem_hat
from src.experimental.experiments ... |
matthewljones/computingincontext | CinC_lecture_02_vectorizing.ipynb | gpl-2.0 | %matplotlib inline
import pandas as pd
def document_vector(wordstring):
"""put yer documentation here friend"""
wordlist = wordstring.split()
set_of_words=set(wordlist)
distinct_words=list(set_of_words)
wordfreq = [wordlist.count(w) for w in distinct_words]
return distinct_words, wordfreq
x,... |
PythonFreeCourse/Notebooks | week05/2_Functions_Part_2.ipynb | mit | def my_range(end, start):
numbers = []
i = start
while i < end:
numbers.append(i)
i += 1
return numbers
my_range(5, 0)
"""
Explanation: <img src="images/logo.jpg" style="display: block; margin-left: auto; margin-right: auto;" alt="לוגו של מיזם לימוד הפייתון. נחש מצויר בצבעי צהוב וכחול... |
kit-cel/wt | mloc/ch4_Autoencoders/Autoencoder_Compression_Binarizer_Sweep.ipynb | gpl-2.0 | import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import numpy as np
from matplotlib import pyplot as plt
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print("We are using the following device for learning:",device)
"""
Explanation: Image Compression using Autoencoders with B... |
wheeler-microfluidics/mr-box-peripheral-board.py | mr_box_peripheral_board/notebooks/Peripherals GTK UI.ipynb | mit | import gtk
import gobject
import threading
import datetime as dt
import matplotlib as mpl
import matplotlib.style
import numpy as np
import pandas as pd
from streaming_plot import StreamingPlot
def _generate_data(stop_event, data_ready, data):
'''
Generate random data to emulate, e.g., reading data from ADC... |
great-expectations/great_expectations | tests/test_fixtures/rule_based_profiler/example_notebooks/DataAssistants_Instantiation_And_Running.ipynb | apache-2.0 | import great_expectations as ge
from great_expectations.core.yaml_handler import YAMLHandler
from great_expectations.core.batch import BatchRequest
from great_expectations.core import ExpectationSuite
from great_expectations.core.expectation_configuration import ExpectationConfiguration
from great_expectations.validato... |
myedibleenso/this-before-that | notebooks/keras-lstm.ipynb | apache-2.0 | import pandas as pd
data = pd.read_json("../annotations.json")
# how many annotations exist with the positive labels of interest?
print("annotations for E1 precedes E2: {}".format((pd.read_json("../annotations.json").relation == "E1 precedes E2").sum()))
print("annotations for E2 precedes E1: {}".format((pd.read_json... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/.ipynb_checkpoints/n00_datasets_generation-checkpoint.ipynb | mit | # Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
%matplotlib inline
%pylab inline
pylab.rcParams['figure.figsize'] = (20.0, 10... |
ageron/ml-notebooks | math_linear_algebra.ipynb | apache-2.0 | from __future__ import division, print_function, unicode_literals
"""
Explanation: Math - Linear Algebra
Linear Algebra is the branch of mathematics that studies vector spaces and linear transformations between vector spaces, such as rotating a shape, scaling it up or down, translating it (ie. moving it), etc.
Machine... |
rdhyee/diversity-census-calc | 03_02_Displaying_Census_URLs.ipynb | apache-2.0 | # http://api.census.gov/data/2010/sf1/geo.html
from IPython.core.display import HTML
HTML("<iframe src='http://api.census.gov/data/2010/sf1/geo.html' width='800px'/>")
%%HTML
<b>hi there</b>
try:
from urllib.parse import urlparse, urlencode, parse_qs, urlunparse
except ImportError:
from urlparse import url... |
dtamayo/MachineLearning | Day3/TransitClassification_Ensemble_part1.ipynb | gpl-3.0 | import sklearn
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split
from sklearn.utils import shuffle
from sklearn import metrics
from sklearn.metrics import roc_curve
from sklearn.metrics import classification_report
from sklearn.decomposition import PCA
from sklear... |
probml/pyprobml | notebooks/book1/12/poisson_regression_insurance.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sklearn
print(sklearn.__version__)
from sklearn.linear_model import PoissonRegressor
"""
Explanation: <a href="https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/poisson_regression_insurance.ipynb" target="_pare... |
alanmitchell/fnsb-benchmark | ddc/ddc_data_tutorial.ipynb | mit | # Import the needed libraries
import pandas as pd
import numpy as np
import ddc_readers # the module that has DDC trend file readers
# import matplotlib pyplot commands
import matplotlib.pyplot as plt
# Show Plots in the Notebook
%matplotlib inline
# Increase the size of plots and their fonts
plt.rcParams['fig... |
chseifert/tutorials | visual-perception/Color-Perception-and-Palettes.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
fig, ax1 = plt.subplots()
my_map = LinearSegmentedColormap.from_list('Map', ['yellow', 'blue'])
left, bottom, width, height = [0.57, 0.65, 0.2, 0.2]
ax2 = fig.add_axes([left, bottom, width, height])
left, bottom... |
richardotis/pycalphad-sandbox | CALPHAD2015-Demo.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from pycalphad import Database, binplot
db_alfe = Database('alfe_sei.TDB')
my_phases_alfe = ['LIQUID', 'B2_BCC', 'FCC_A1', 'HCP_A3', 'AL5FE2', 'AL2FE', 'AL13FE4', 'AL5FE4']
fig = plt.figure(figsize=(9,6))
pdens = [{'B2_BCC': 20000}, 2000]
%time binplot(db_alfe, ['AL',... |
mauriciogtec/PropedeuticoDataScience2017 | Alumnos/Karen_Esther/Tarea_2/Tarea_2 _Karen_v2.ipynb | mit | import numpy as np # funciones numéricas (arrays, matrices, etc.)
import PIL.Image # funciones para cargar y manipular imágenes
im = PIL.Image.open("/Users/Karen/image.jpg")
col,row = im.size
image = np.zeros((row*col, 5))
pixels = im.load()
print(pixels[188,266])
for i in range(col):
... |
empet/Plotly-plots | Isosurface-in-volumetric-data.ipynb | gpl-3.0 | import plotly.graph_objs as go
import numpy as np
from skimage import measure
"""
Explanation: Isosurface in volumetric data
Linear and nonlinear slices in volumetric data, as graphs of functions of two variables, were defined in this Jupyter Notebook http://nbviewer.jupyter.org/github/empet/Plotly-plots/blob/master/P... |
nguy/AWOT | examples/awot_track_kmz_save.ipynb | gpl-2.0 | import os
import matplotlib.pyplot as plt
import numpy as np
import awot
%matplotlib inline
"""
Explanation: <h2>Examples saving KMZ/KML files</h2>
End of explanation
"""
flname = os.path.join("/Users/guy/data/king_air/pecan2015", "20150716.c1.nc")
fl = awot.io.read_netcdf(fname=flname, platform='uwka')
print(fl[... |
ES-DOC/esdoc-jupyterhub | notebooks/nerc/cmip6/models/sandbox-1/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'sandbox-1', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NERC
Source ID: SANDBOX-1
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radi... |
zerkh/theano_lstm | Tutorial.ipynb | bsd-3-clause | ## Fake dataset:
class Sampler:
def __init__(self, prob_table):
total_prob = 0.0
if type(prob_table) is dict:
for key, value in prob_table.items():
total_prob += value
elif type(prob_table) is list:
prob_table_gen = {}
for key in prob_tabl... |
sempwn/salary-prediction | explore-data.ipynb | mit | data.SalaryNormalized.hist(); plt.ylabel('frequency'); plt.xlabel(u'salary (£)'); plt.yscale('log');
"""
Explanation: plot salary
Normalized salary is the target variable. Seems fairly straight on a ylog plot suggesting a simple linear regression on categories isn't going to cut it.
End of explanation
"""
cachedStop... |
vitojph/kschool-nlp | notebooks-py3/vsm.ipynb | gpl-3.0 | # corpus ficticio con tres documentos de la misma longitud
# y sin repeticiones de términos dentro del mismo documento
# cada doc es una lista de palabras
d1 = 'los angeles times'.split()
d2 = 'new york times'.split()
d3 = 'new york post'.split()
# nuestro corpus D es una lista de documentos
D = [d1, d2, d3]
print(D... |
UDST/activitysim | activitysim/examples/example_estimation/notebooks/14_joint_tour_scheduling.ipynb | bsd-3-clause | import os
import larch # !conda install larch -c conda-forge # for estimation
import pandas as pd
"""
Explanation: Estimating Joint Tour Scheduling
This notebook illustrates how to re-estimate the joint tour scheduling component for ActivitySim. This process
includes running ActivitySim in estimation mode to read h... |
spencerchan/ctabus | notebooks/Visualizing Bus Bunching.ipynb | gpl-3.0 | patterns = tools.load_patterns(73, waypoints=True)
patterns = patterns[patterns.pid == 2170]
patterns.head()
"""
Explanation: A Common City Scene
If you've ever ridden the bus, you've probably had the following experience. You're standing at the bus stop waiting for the bus to come. You've been waiting over ten minute... |
iiasa/xarray_tutorial | xarray-tutorial-egu2017-answers.ipynb | bsd-3-clause | # standard imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import xarray as xr
import warnings
%matplotlib inline
np.set_printoptions(precision=3, linewidth=80, edgeitems=1) # make numpy less verbose
xr.set_options(display_width=70)
warnings.simplefilter('ignore') # filter some warnin... |
gaufung/PythonStandardLibrary | mathematic/decimal.ipynb | mit | import decimal
fmt = '{0:<25}{1:<25}'
print(fmt.format('Input', 'Output'))
print(fmt.format('-'*25, '-'*25))
#Integer
print(fmt.format(5, decimal.Decimal(5)))
#String
print(fmt.format('3.14', decimal.Decimal('3.14')))
#Float
f = 0.1
print(fmt.format(repr(f), decimal.Decimal(str(f))))
print('{:0.23g}{:<25}'.format(f, st... |
sbg/Mitty | docs/filter-based-analysis-tutorial/filter-based-analysis-tutorial.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
import time
import matplotlib.pyplot as plt
import cytoolz.curried as cyt
from bokeh.plotting import figure, show, output_file
import mitty.analysis.bamtoolz as bamtoolz
import mitty.analysis.bamfilters as mab
import mitty.analysis.plots as mapl
# import logging
# FORMAT = "[%(file... |
Rotvig/cs231n | Deep Learning/Exercise 2/Q1.ipynb | mit | # As usual, a bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient
from cs231n.layers import *
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolati... |
Ganeshgajakosh/ml_lab_ecsc_306 | labwork/lab7/sci-learn/plot_pca_3d.ipynb | apache-2.0 | print(__doc__)
# Authors: Gael Varoquaux
# Jaques Grobler
# Kevin Hughes
# License: BSD 3 clause
from sklearn.decomposition import PCA
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
"""
Explanation: ===============================... |
kmunve/APS | aps/notebooks/ml_varsom/linear_regression.ipynb | mit | import pandas as pd
import numpy as np
import json
import graphviz
import matplotlib.pyplot as plt
from sklearn import linear_model
pd.set_option("display.max_rows",6)
%matplotlib inline
df_data = pd.read_csv('varsom_ml_preproc.csv', index_col=0)
X = df_data.filter(['mountain_weather_wind_speed_num', 'mountain_weat... |
nirmorgo/BTC_trade_strategy_utils | BTC trade strategy utilities demo.ipynb | gpl-3.0 | import pandas as pd
pd.options.mode.chained_assignment = None # default='warn'
# Need to disable the annoying Pandas warnings that were added in 0.20...
import matplotlib
%matplotlib inline
"""
Explanation: This is a short demo that demonstrates the use of the functions in this repo
End of explanation
"""
from da... |
anguszxd/segment | 你好，Colaboratory.ipynb | gpl-3.0 | import tensorflow as tf
input1 = tf.ones((2, 3))
input2 = tf.reshape(tf.range(1, 7, dtype=tf.float32), (2, 3))
output = input1 + input2
with tf.Session():
result = output.eval()
result
"""
Explanation: <a href="https://colab.research.google.com/github/anguszxd/segment/blob/master/%E4%BD%A0%E5%A5%BD%EF%BC%8CColab... |
Spandan-Madan/DeepLearningProject | docs/Deep_Learning_Project-Pytorch.ipynb | mit | import warnings
warnings.filterwarnings('ignore')
import torchvision
import urllib2
import requests
import json
import imdb
import time
import itertools
import wget
import os
import tmdbsimple as tmdb
import numpy as np
import random
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as... |
tensorflow/tensorflow | tensorflow/lite/g3doc/models/modify/model_maker/question_answer.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... |
trangel/Data-Science | reinforcement_learning/qlearning.ipynb | gpl-3.0 | #XVFB will be launched if you run on a server
import os
if type(os.environ.get("DISPLAY")) is not str or len(os.environ.get("DISPLAY")) == 0:
!bash ../xvfb start
os.environ['DISPLAY'] = ':1'
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
%load_ext autoreload
%autoreload 2
%%writ... |
rsheftel/raccoon | examples/usage_dropin.ipynb | mit | # remove comment to use latest development version
import sys; sys.path.insert(0, '../')
# import libraries
import raccoon as rc
"""
Explanation: Example Usage for Drop-in List Replacements
End of explanation
"""
from blist import blist
# Construct with blist
df_blist = rc.DataFrame({'a': [1, 2, 3]}, index=[5, 6, ... |
mne-tools/mne-tools.github.io | 0.19/_downloads/075ba1175413b0aa0dc66e721f312729/plot_mixed_norm_inverse.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.inverse_sparse import mixed_norm, make_stc_from_dipoles
from mne.minimum_norm import make_inverse_... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/hadgem3-gc31-lm/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-lm', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: MOHC
Source ID: HADGEM3-GC31-LM
Sub-Topics: Radiative Forcings.
Propert... |
vitojph/kschool-nlp | notebooks-py3/nltk-pos.ipynb | gpl-3.0 | import nltk
"""
Explanation: Resumen NLTK: Etiquetado morfológico (part-of-speech tagging)
Este resumen se corresponde con el capítulo 5 del NLTK Book Categorizing and Tagging Words. La lectura del capítulo es muy recomendable.
Etiquetado morfológico con NLTK
NLTK propociona varias herramientas para poder crear fácilm... |
wei-Z/Python-Machine-Learning | code/bonus/reading_mnist.ipynb | mit | %load_ext watermark
%watermark -a 'Sebastian Raschka' -v -d
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
"""
Explanation: Sebastian Raschka, 2015
https://github.com/rasbt/python-machine-learning-book
Note that the optiona... |
abitofalchemy/hrg_nets | peer_into_thrg.ipynb | gpl-3.0 | # imports
import networkx as nx
%matplotlib inline
import matplotlib.pyplot as plt
params = {'legend.fontsize':'small',
'figure.figsize': (7,7),
'axes.labelsize': 'small',
'axes.titlesize': 'small',
'xtick.labelsize':'small',
'ytick.labelsize':'small'}
plt.rcParams.upda... |
ES-DOC/esdoc-jupyterhub | notebooks/ipsl/cmip6/models/sandbox-2/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'sandbox-2', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: IPSL
Source ID: SANDBOX-2
Sub-Topics: Radiative Forcings.
Properties: 85 (42 ... |
Bio204-class/bio204-notebooks | inclass-2016-02-24-CLT.ipynb | cc0-1.0 | %matplotlib inline
import numpy as np
import scipy.stats as stats
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
"""
Explanation: Standard Imports
End of explanation
"""
import statplots
"""
Explanation: Imports from a custom module
As you carry out your own analyses, probably build up a lib... |
citxx/sis-python | crash-course/builtin-sort.ipynb | mit | a = [5, 3, -2, 9, 1]
# Метод sort меняет существующий список
a.sort()
print(a)
"""
Explanation: <h1>Содержание<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Встроенная-сортировка" data-toc-modified-id="Встроенная-сортировка-1">Встроенная сортировка</a></span></li><li><spa... |
mathnathan/notebooks | mpfi/Research Outline.ipynb | mit | x1 = np.random.uniform(size=500)
x2 = np.random.uniform(size=500)
plt.scatter(x1,x2); plt.xlim(-0.25,1.25); plt.ylim(-0.25,1.25)
plt.grid(); plt.show()
"""
Explanation: Introduction
What are the underlying biophysics that govern astrocyte behavior? To explore this question we have at our disposal a large dataset of ob... |
B4cchus/wh40k-hitscalc | Mathhammer-Intro.ipynb | gpl-3.0 | profiles[0] = {'shots': 10, 'p_hit': 1 / 2, 'p_wound': 1 / 2, 'p_unsaved': 4 / 6, 'damage': '1'}
profile_damage = damage_dealt(profiles[0])
wound_chart(profile_damage, profiles)
"""
Explanation: Visual mathhammer for 8th edition
Introduction to plots
The charts and numbers below visually present the distribution of to... |
jpwhite3/python-analytics-demo | Part_2.ipynb | cc0-1.0 | from __future__ import division, unicode_literals
import pandas as pd
import numpy as np
import matplotlib
%matplotlib inline
matplotlib.style.use('ggplot')
"""
Explanation: 1.) Import the modules we will need
End of explanation
"""
df = pd.read_excel('./input/complete_data.xls')
df.head()
"""
Explanation: 2.) Prev... |
M0nica/python-foundations-hw | 07/pandas_cheatsheet.ipynb | mit | # !workon dataanalysis
import pandas as pd
"""
Explanation: 01: Building a pandas Cheat Sheet, Part 1
Use the csv I've attached to answer the following questions
Import pandas with the right name
End of explanation
"""
import matplotlib.pyplot as plt
#DISPLAY MOTPLOTLIB INLINE WITH THE NOTEBOOK AS OPPOSED TO POP UP ... |
sspickle/sci-comp-notebooks | P11-FourierSeries.ipynb | mit | L=1.0
N=500 # make sure N is even for simpson's rule
A=1.0
def fLeft(x):
return 2*A*x/L
def fRight(x):
return 2*A*(L-x)/L
def fa_vec(x):
"""
vector version
'where(cond, A, B)', returns A when cond is true and B when cond is false.
"""
return np.where(x<L/2, fLeft(x), fRight(x))
x=np... |
nerdcommander/scientific_computing_2017 | lesson15/Lesson15_individual.ipynb | mit | # Planet class definition at the end of Part 1
"""
Explanation: Lesson15 Individual Assignment
Individual means that you do it yourself. You won't learn to code if you don't struggle for yourself and write your own code. Remember that while you can discuss the general (algorithmic) way to solve a problem, you should... |
tpin3694/tpin3694.github.io | machine-learning/tokenize_text.ipynb | mit | # Load library
from nltk.tokenize import word_tokenize, sent_tokenize
"""
Explanation: Title: Tokenize Text
Slug: tokenize_text
Summary: How to tokenize text from unstructured text data for machine learning in Python.
Date: 2016-09-08 12:00
Category: Machine Learning
Tags: Preprocessing Text
Authors: Chris Albon
Prel... |
BinRoot/TensorFlow-Book | ch04_classification/Concept02_logistic.ipynb | mit | %matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
learning_rate = 0.01
training_epochs = 1000
"""
Explanation: Ch 04: Concept 02
Logistic regression
Import the usual libraries, and set up the usual hyper-parameters:
End of explanation
"""
x1 = np.random.normal(-4, 2, 1000... |
keras-team/keras-io | guides/ipynb/training_with_built_in_methods.ipynb | apache-2.0 | import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
"""
Explanation: Training & evaluation with the built-in methods
Author: fchollet<br>
Date created: 2019/03/01<br>
Last modified: 2020/04/13<br>
Description: Complete guide to training & evaluation with fit() and evaluate().
Setup... |
Unidata/netcdf4-python | examples/writing_netCDF.ipynb | mit | import netCDF4 # Note: python is case-sensitive!
import numpy as np
"""
Explanation: Writing netCDF data
Important Note: when running this notebook interactively in a browser, you probably will not be able to execute individual cells out of order without getting an error. Instead, choose "Run All" from the Cell m... |
JorisBolsens/PYNQ | Pynq-Z1/notebooks/examples/pmod_grove_light.ipynb | bsd-3-clause | from pynq import Overlay
Overlay("base.bit").download()
"""
Explanation: Grove Light Sensor 1.1
This example shows how to use the Grove Light Sensor v1.1. You will also see how to plot a graph using matplotlib.
The Grove Light Sensor produces an analog signal which requires an ADC.
The Grove Light Sensor, PYNQ Grove A... |
Upward-Spiral-Science/team1 | code/Imaging Cortical Layers.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 ... |
csdms/pymt | notebooks/ku.ipynb | mit | # Load standard Python modules
import numpy as np
import matplotlib.pyplot as plt
# Load PyMT model(s)
import pymt.models
ku = pymt.models.Ku()
"""
Explanation: Kudryavtsev Model
Link to this notebook: https://github.com/csdms/pymt/blob/master/notebooks/ku.ipynb
Install command: $ conda install notebook pymt_permamo... |
gschivley/Index-variability | Notebooks/Capacity.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os
import pathlib
from pathlib import Path
import sys
from os.path import join
import json
import calendar
sns.set(style='white')
idx = pd.IndexSlice
"""
Explanation: Calculate generation capacity by month
This noteboo... |
Unidata/unidata-python-workshop | notebooks/Model_Output/Downloading model fields with NCSS.ipynb | mit | # Resolve the latest GFS dataset
import metpy
from siphon.catalog import TDSCatalog
# Set up access via NCSS
gfs_catalog = ('http://thredds.ucar.edu/thredds/catalog/grib/NCEP/GFS/'
'Global_0p5deg/catalog.xml?dataset=grib/NCEP/GFS/Global_0p5deg/Best')
cat = TDSCatalog(gfs_catalog)
ncss = cat.datasets[0].... |
bmcfee/ismir2017_chords | notebooks/03 - Results.ipynb | bsd-2-clause | chordino = load_results('/home/bmcfee/git/chord_models/data/chordino/')
dnn = load_results('/home/bmcfee/git/chord_models/data/ejh2015_dnn/')
khmm = load_results('/home/bmcfee/git/chord_models/data/ejh2015_khmm/')
plain = load_results('/home/bmcfee/working/chords/model/')
aug = load_results('/home/bmcfee/working/ch... |
yangw1234/BigDL | python/chronos/use-case/fsi/stock_prediction.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import os
# S&P 500
FILE_NAME = 'all_stocks_5yr.csv'
SOURCE_URL = 'https://github.com/CNuge/kaggle-code/raw/master/stock_data/'
filepath = './data/'+ FILE_NAME
filepath = os.path.join('data', FILE_NAME)
print(filepath)
# download data
!if ! [ -d "data" ]; then mkdir data;... |
d-k-b/udacity-deep-learning | gan_mnist/Intro_to_GANs_Solution.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... |
harmsm/pythonic-science | labs/04_machine-learning/04_PCA-analysis_key.ipynb | unlicense | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from sklearn import datasets
from sklearn.decomposition import PCA
"""
Explanation: Machine Learning
End of explanation
"""
def load_pdb(pdb_file):
f = open(pdb_file,'r')
lines = f.readlines()
f.close()
all_coord = ... |
fajifr/recontent | gensim_trial.ipynb | mit | doc1="Electron acceleration in a post-flare decimetric continuum source Prasad Subramanian, S. M. White, M. Karlický, R. Sych, H. S. Sawant, S. Ananthakrishnan(Submitted on 23 Mar 2007)Aims: To calculate the power budget for electron acceleration and the efficiency of the plasma emission mechanism in a post-flare decim... |
dolittle007/dolittle007.github.io | notebooks/GLM-hierarchical.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm
import pandas as pd
import theano
data = pd.read_csv(pm.get_data('radon.csv'))
data['log_radon'] = data['log_radon'].astype(theano.config.floatX)
county_names = data.county.unique()
county_idx = data.county_code.values
n_countie... |
astarostin/MachineLearningSpecializationCoursera | course2/week1/peer_review_linreg_height_weight.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Линейная регрессия и основные библиотеки Python для анализа данных и научных вычислений
Это задание посвящено линейной регрессии. На примере прогнозирования роста человека по его весу Вы уви... |
steven-murray/halomod | docs/examples/component-showcase.ipynb | mit | import halomod
import hmf
import numpy as np
print(f"Using halomod v{halomod.__version__}")
print(f"Using hmf v{hmf.__version__}")
from halomod.bias import make_colossus_bias
from halomod.concentration import make_colossus_cm
"""
Explanation: A Showcase of Components in halomod
In this demo, we will showcase each and ... |
gagneurlab/concise | nbs/PWM_initialization.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
# RBP PWM's
from concise.data import attract
dfa = attract.get_metadata()
dfa
# TF PWM's
from concise.data import encode
dfe = encode.get_metadata()
dfe
# TF PWM's
from concise.data import hocomoco
dfh = hocomoco.get_metadata()
dfh
"""
Explanation: Initializing ... |
jseabold/statsmodels | examples/notebooks/discrete_choice_example.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.formula.api import logit
print(sm.datasets.fair.SOURCE)
print( sm.datasets.fair.NOTE)
dta = sm.datasets.fair.load_pandas().data
dta['affair'] = (dta['affai... |
CitrineInformatics/lolo | python/examples/regression-example.ipynb | apache-2.0 | %matplotlib inline
from matplotlib import pyplot as plt
from lolopy.learners import RandomForestRegressor
from sklearn.ensemble import RandomForestRegressor as SKRFRegressor
from sklearn.datasets import load_boston
import numpy as np
"""
Explanation: Comparing Lolo and Scikit-Learn
The purpose of this notebook is to c... |
GoogleCloudPlatform/training-data-analyst | quests/endtoendml/labs/3_keras_wd.ipynb | apache-2.0 | # Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.1
# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = RE... |
bjsmith/motivation-simulation | test-jupyter-widgets-clone.ipynb | gpl-3.0 | from matplotlib.pyplot import figure, plot, xlabel, ylabel, title, show
from IPython.display import display
text = widgets.FloatText()
floatText = widgets.FloatText(description='MyField',min=-5,max=5)
floatSlider = widgets.FloatSlider(description='MyField',min=-5,max=5)
#https://ipywidgets.readthedocs.io/en/stable/... |
mayank-johri/LearnSeleniumUsingPython | Section 2 - Advance Python/Chapter S2.12 - Weak Reference, Copy/copy.ipynb | gpl-3.0 | import copy
class MyTry:
def __init__(self):
self.lst = [1,2,3,4,5]
a = MyTry()
dup = copy.copy(a)
a.lst.append(6)
print(a.lst, dup.lst)
print(id(a), id(dup))
import copy
class MyTry:
def __init__(self):
self.lst = [1,2,3,4,5]
a = MyTry()
dup = copy.copy(a)
a.lst.append(6)
print(a.lst, du... |
nicolas998/wmf | Examples/PrePara_Altavista_AguasAbajo.ipynb | gpl-3.0 | %matplotlib inline
from wmf import wmf
import numpy as np
import pylab as pl
import datetime as dt
import os
ruta = '/media/nicolas/discoGrande/01_SIATA/'
"""
Explanation: Prepara Alta Vista Para Modelacion
Se prepara la cuenca de alta vista para que sea modelada en el SIATA en tiempo real, en este caso se preparan ... |
opalytics/opalytics-ticdat | examples/amplpy/netflow/netflow_other_data_sources.ipynb | bsd-2-clause | commodities = [['Pencils', 0.5], ['Pens', 0.2125]]
# a one column table can just be a simple list
nodes = ['Boston', 'Denver', 'Detroit', 'New York', 'Seattle']
cost = [['Pencils', 'Denver', 'Boston', 10.0],
['Pencils', 'Denver', 'New York', 10.0],
['Pencils', 'Denver', 'Seattle', 7.5],
['Pe... |
emsi/wordvectors | Build OpenSubtitles Corpus.ipynb | mit | %%bash
mkdir -p data
truncatefile > data/OpenSubtitles2016.txt 2&> /dev/null
echo "Truncated data/OpenSubtitles2016.txt"
"""
Explanation: OpenSubtitles corpus
The following code was used to extract Polish OpenSubtitles corpus.
It consists of ~775 milion tokens and ~143 milion sentences, vastly dialogues which makes it... |
mne-tools/mne-tools.github.io | 0.24/_downloads/2d3a2ce4cdcb2dad9804801c80816516/parcellation.ipynb | bsd-3-clause | # Author: Eric Larson <larson.eric.d@gmail.com>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD-3-Clause
import mne
Brain = mne.viz.get_brain_class()
subjects_dir = mne.datasets.sample.data_path() + '/subjects'
mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=subjects_dir,
... |
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