repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
tensorflow/docs-l10n | site/en-snapshot/hub/tutorials/text_classification_with_tf_hub.ipynb | apache-2.0 | # Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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 app... |
tpin3694/tpin3694.github.io | statistics/spearmans_rank_correlation.ipynb | mit | import numpy as np
import pandas as pd
import scipy.stats
"""
Explanation: Title: Spearman's Rank Correlation
Slug: spearmans_rank_correlation
Summary: Spearman's Rank Correlation in Python.
Date: 2016-02-08 12:00
Category: Statistics
Tags: Basics
Authors: Chris Albon
Preliminaries
End of explanation
"""
# Creat... |
probml/pyprobml | notebooks/book1/20/skipgram_torch.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
np.random.seed(seed=1)
import math
import os
import random
try:
import torch
except ModuleNotFoundError:
%pip install -qq torch
import torch
from torch import nn
from torch.nn import functional as F
import requests
import zipfile
import hashlib
import c... |
mne-tools/mne-tools.github.io | 0.20/_downloads/20f35983ef279d1b30aa970c81aafe26/plot_read_events.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Chris Holdgraf <choldgraf@berkeley.edu>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvis_raw-eve.fi... |
whitead/numerical_stats | unit_10/hw_2019/homework_10_key.ipynb | gpl-3.0 | import scipy.stats as ss
import numpy as np
"""
Explanation: Homework 10 Key
CHE 116: Numerical Methods and Statistics
4/3/2019
End of explanation
"""
import scipy.stats as ss
ss.t.cdf(-2, 4) * 2
"""
Explanation: 1. Conceptual Questions
Describe the general process for parametric hypothesis tests.
Why would y... |
adityaka/misc_scripts | python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/04_02/Begin/.ipynb_checkpoints/Select-checkpoint.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
"""
Explanation: Select, Add, Delete, Columns
End of explanation
"""
cookbook_df = pd.DataFrame({'AAA' : [4,5,6,7], 'BBB' : [10,20,30,40],'CCC' : [100,50,-30,-50]})
cookbook_df['BBB']
"""
Explanation: dictionary like operations
dictionary selection with string index
End of exp... |
cavestruz/StrongCNN | notebooks/Bootstrap_Analysis.ipynb | mit | import glob
import numpy as np
import pandas as pd
from sklearn.metrics import precision_score, recall_score, roc_auc_score
def get_data(datadir):
"""
Read the data files from different subdirectories of datadir corresponding
to different HOG configurations.
Inputs
datadir: top level dire... |
akafael/unb-vc | notes/vc_aula3.ipynb | gpl-3.0 | from sympy import *
from IPython.display import display,Math
r1,r2,t1,t2 = symbols("rho_1 rho_2 theta_1 theta_2",constant=true,real=true)
z1 = r1*exp(I*t1)
z2 = r2*exp(I*t2)
"""
Explanation: Aula 3
Operações com números complexos
Forma Polar
Supondo dois números complexos $z_1$ e $z_2$ tais que
$$z_1 = r_1 e^{i\thet... |
hunterherrin/phys202-2015-work | assignments/assignment06/InteractEx05.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as mp
from IPython.html.widgets import interact, interactive, fixed
from IPython.html import widgets
from IPython.display import display
from IPython.display import Image,HTML,SVG
"""
Explanation: Interact Exercise 5
Imports
Put the standard imports ... |
abmantz/lrgs | notebooks/example_python.ipynb | mit | import lrgs
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Example in Python
This is a fairly minimal example, demonstrating the slightly different calling convention in the Python version of LRGS, compared with the R version.
One notable and practical difference is that the Pyt... |
NewKnowledge/punk | examples/Feature Selection.ipynb | mit | import punk
help(punk)
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from punk import feature_selection
"""
E... |
ryan-leung/PHYS4650_Python_Tutorial | notebooks/Feb2017/CH1 Syntax.ipynb | bsd-3-clause | print "Hello World!"
"""
Explanation: CH 1 Syntax
In this tutorial notebook, you will learn how to do programming in python and jupyter. Open a new notebook in ipython or https://try.jupyter.org/, try to "copy" and "paste" the following codes.
Jupyter
- Basic operations:
Click on the cell to select it.
Press SHIFT+EN... |
GoogleCloudPlatform/tf-estimator-tutorials | 00_Miscellaneous/text-similarity-analysis/bqml/classification_with_embeddings.ipynb | apache-2.0 | from google.colab import auth
auth.authenticate_user()
from google.cloud import bigquery
client = bigquery.Client(project='YOUR-PROJECT-NAME')
"""
Explanation: <a href="https://colab.research.google.com/github/GoogleCloudPlatform/tf-estimator-tutorials/blob/master/00_Miscellaneous/text-similarity-analysis/bqml/classi... |
bayesimpact/bob-emploi | data_analysis/notebooks/datasets/usa/soc_dmtf.ipynb | gpl-3.0 | from os import path
import pandas as pd
import seaborn as sns
DATA_FOLDER = %env DATA_FOLDER
sns.set()
dmtf = pd.read_excel(path.join(DATA_FOLDER, 'usa/soc/DMTF.xlsx'))
dmtf.head(10)
"""
Explanation: SOC Direct Match Title File
Author: pascal@bayesimpact.org
Date: 2020-06-19
The US Bureau of Labor Statistics (BLS) ... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_introduction.ipynb | bsd-3-clause | import mne
"""
Explanation: Basic MEG and EEG data processing
MNE-Python reimplements most of MNE-C's (the original MNE command line utils)
functionality and offers transparent scripting.
On top of that it extends MNE-C's functionality considerably
(customize events, compute contrasts, group statistics, time-frequenc... |
PG-TUe/tpot | tutorials/MAGIC Gamma Telescope/MAGIC Gamma Telescope.ipynb | lgpl-3.0 | # Import required libraries
from tpot import TPOTClassifier
from sklearn.cross_validation import train_test_split
import pandas as pd
import numpy as np
#Load the data
telescope=pd.read_csv('MAGIC Gamma Telescope Data.csv')
telescope.head(5)
"""
Explanation: MAGIC Gamma Telescope - TPOT Classification Study
The belo... |
rusucosmin/courses | ml/ex05/solution/ex05.ipynb | mit | from helpers import sample_data, load_data, standardize
# load data.
height, weight, gender = load_data()
# build sampled x and y.
seed = 1
y = np.expand_dims(gender, axis=1)
X = np.c_[height.reshape(-1), weight.reshape(-1)]
y, X = sample_data(y, X, seed, size_samples=200)
x, mean_x, std_x = standardize(X)
"""
Expla... |
SN-Isotropy/Isotropy | doc/esmeralda/Hubble+Diagram.ipynb | mit | import sys
import gzip, pickle
if sys.version.startswith('2'):
snFits = pickle.load(gzip.GzipFile('snFits.p.gz'))
else:
snFits = pickle.load(gzip.GzipFile('snFits.p.gz'),
encoding='latin1')
print(len(snFits))
snf = [s for s in snFits.values() if s is not None]
print(len(snf))
snf[0]
"""
E... |
landlab/landlab | notebooks/tutorials/terrain_analysis/flow__distance_utility/application_of_flow__distance_utility.ipynb | mit | from landlab.io import read_esri_ascii
from landlab.components import FlowAccumulator
from landlab.plot import imshow_grid
from matplotlib.pyplot import figure
%matplotlib inline
from landlab.utils import watershed
import numpy as np
from landlab.utils.flow__distance import calculate_flow__distance
"""
Explanation: <... |
tkzeng/molecular-design-toolkit | moldesign/_notebooks/Tutorial 1. Making a molecule.ipynb | apache-2.0 | import moldesign as mdt
import moldesign.units as u
"""
Explanation: <span style="float:right"><a href="http://moldesign.bionano.autodesk.com/" target="_blank" title="About">About</a> <a href="https://forum.bionano.autodesk.com/c/Molecular-Design-Toolkit" target="_blank" title="Forum... |
google/starthinker | colabs/sheets_clear.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: Sheet Clear
Clear data from a sheet.
License
Copyright 2020 Google LLC,
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... |
ktmud/deep-learning | 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: Language Translation
In this project, you’re going... |
egillanton/Udacity-SDCND | 1. Computer Vision and Deep Learning/L1 TensorFlow Lab/lab.ipynb | mit | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
print('All m... |
DawesLab/LabNotebooks | control-pulseoptim-CRAB-2qubitInerac.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import datetime
from qutip import Qobj, identity, sigmax, sigmaz, tensor, mesolve
import random
import qutip.logging_utils as logging
logger = logging.get_logger()
#Set this to None or logging.WARN for 'quiet' execution
log_level = logging.INFO
#QuT... |
HrWangChengdu/CS231n | assignment1/knn.ipynb | mit | # Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.... |
julianogalgaro/udacity | nd101/c2l8-sentiment-analysis/sentiment_network/Sentiment Classification - Mini Project 3.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()... |
gidden/salamanca | doc/notebooks/currencies.ipynb | apache-2.0 | from salamanca.currency import Translator
"""
Explanation: Translating between Currencies
End of explanation
"""
xltr = Translator()
"""
Explanation: Translating between currencies requires a number of different choices
do you want to consider the relative value of two currencies based on Market Exchange Rates or... |
mjuric/LSSTC-DSFP-Sessions | Session3/Day4/ANTARES/miniAntaresSolutions_parallel.ipynb | mit | # first we need to construct a client that will interface with our cluster
from ipyparallel import Client, require
worker = Client()
# once we create a client, we can decide how to allocate tasks across the cluster
# we've got however many 'engines' you started in the cluster
# lets just use all of them
lview = worke... |
AllenDowney/ProbablyOverthinkingIt | socks_and_skeets.ipynb | mit | from __future__ import print_function, division
%matplotlib inline
import warnings
warnings.filterwarnings("ignore")
from thinkbayes2 import Pmf, Hist, Beta
import thinkbayes2
import thinkplot
"""
Explanation: Socks, Skeets, and Space Invaders
This notebook contains code from my blog, Probably Overthinking It
Copyr... |
possnfiffer/py-emde | Py-EMDE-Kenya-GLOBE-01.ipynb | bsd-2-clause | import requests
import json
r = requests.get('http://3d-kenya.chordsrt.com/instruments/1.geojson?start=2016-09-01T00:00&end=2016-11-01T00:00')
if r.status_code == 200:
d = r.json()['Data']
else:
print("Please verify that the URL for the weather station is correct. You may just have to try again with a differe... |
bmorris3/gsoc2015 | finder_chart.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
from astroplan import FixedTarget
import astropy.units as u
from astropy.wcs import WCS
from astropy.coordinates import SkyCoord
from astropy.io import fits
from astroquery.skyview import SkyView
@u.quantity_input(fov_radius=u.deg)
def plot_finder_image(target, su... |
TheLampshady/tensor_tutorial | Convolutional_101.ipynb | mit | # 3 x 3 filter shape
filter1 = [
[.1, .1, .2],
[.1, .1, .2],
[.2, .2, .2],
]
# Each filter only has one input channel (grey scale)
# 3 x 3 x 1
channel_filters1 = [filter1]
# We want to output 2 channels which requires another set of 3 x 3 x 1
filter2 = [
[.9, .5, .9],
[.5, .3, .5],
[.9, .5, ... |
phuongxuanpham/SelfDrivingCar | CarND-LeNet-Lab/LeNet-Lab-Solution.ipynb | gpl-3.0 | from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", reshape=False)
X_train, y_train = mnist.train.images, mnist.train.labels
X_validation, y_validation = mnist.validation.images, mnist.validation.labels
X_test, y_test = mnist.test.images, mn... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/statespace_cycles.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from pandas_datareader.data import DataReader
endog = DataReader('UNRATE', 'fred', start='1954-01-01')
endog.index.freq = endog.index.inferred_freq
"""
Explanation: Trends and cycles in unemployment... |
tensorflow/hub | docs/tutorials/text_classification_with_tf_hub.ipynb | apache-2.0 | # Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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 app... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/.ipynb_checkpoints/n08_simple_q_learner_fast_learner-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
from multiprocessing import Pool
%matplotlib inline
%pylab inline
pylab.rcPar... |
jdsanch1/SimRC | 02. Parte 2/11. Clase 11/11Class NB.ipynb | mit | #importar los paquetes que se van a usar
import pandas as pd
import numpy as np
import datetime
from datetime import datetime
import scipy.stats as stats
import scipy as sp
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.covariance as skcov
import cvxopt as opt
from cvxopt import blas, solvers
solv... |
the-deep-learners/TensorFlow-LiveLessons | notebooks/tensor-fied_intro_to_tensorflow.ipynb | mit | import numpy as np
np.random.seed(42)
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
tf.set_random_seed(42)
xs = [0., 1., 2., 3., 4., 5., 6., 7.]
ys = [-.82, -.94, -.12, .26, .39, .64, 1.02, 1.]
fig, ax = plt.subplots()
_ = ax.scatter(xs, ys)
m = tf.Variable(-0.5)
b ... |
timothydmorton/usrp-sciprog | day2/python-intro.ipynb | mit | #Integers
a = 1
a
#floats
b = 2.
print(b)
print(type(b))
#strings
s = 'list of letters'
print(s)
print(type(s))
#lists
l = [4,5,2.,'hello', 'world']
l
#list elements
l[1]
#A word on indexing
# : means continuation from the preceding index
l[2:]
# or to the following index
l[-4]
#string are lists of letters:
l[-... |
AstroHackWeek/AstroHackWeek2016 | day4-sampling/Worksheet.ipynb | mit | def log_p_func(theta):
pass
"""
Explanation: A simple Metropolis MCMC
In this exercise, we'll implement the simplest MCMC algorithm and sample from a two-dimensional Gaussian to demonstrate the method.
First, implement the probability distribution as a function that takes in a 2-D vector $\theta$ and returns:
$$
... |
atulsingh0/MachineLearning | Sklearn_MLPython/cross_validation-0.18.ipynb | gpl-3.0 | # import
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score, KFold, train_test_split, cross_val_predict, LeaveOneOut, LeavePOut
from sklearn.model_selection import ShuffleSplit, StratifiedKFold, StratifiedShuffleSplit, GroupKFold, LeaveOneGroupOut
from sklearn.model_selection imp... |
simpeg/simpegExamples | SciPy2015/DCResistivityEx.ipynb | mit | # Define a unit square domain
# This can be hidden and imported depending on how much you want to show
nx,ny = 60,60 # number of cells in x,y
mesh = Mesh.TensorMesh([nx,ny]) # build a tensor mesh
sigma = np.ones(mesh.nC) # assign a conductivity model
# create source
xp, yp = 0.25, 0.5
xn, yn = 0.75, 0.5
sigmaback = ... |
lileiting/goatools | notebooks/cell_cycle.ipynb | bsd-2-clause | # Get http://geneontology.org/ontology/go-basic.obo
from goatools.base import download_go_basic_obo
obo_fname = download_go_basic_obo()
"""
Explanation: Cell Cycle genes
Using Gene Ontologies (GO), create an up-to-date list of all human protein-coding genes that are know to be associated with cell cycle.
1. Download O... |
probml/pyprobml | notebooks/book2/12/smc_ibis_1d.ipynb | mit | #!git clone https://github.com/nchopin/particles.git
!pip install -qq git+https://github.com/nchopin/particles.git
try:
import particles
except ModuleNotFoundError:
%pip install -qq particles
import particles
import particles.state_space_models as ssm
import particles.distributions as dists
%matplotlib i... |
abevieiramota/data-science-cookbook | 2017/06-linear-regression/resp_abelardo_mota.ipynb | mit | import pandas as pd
df = pd.read_csv("insurance.csv", header=None, names=['r', 'p'])
df.head()
"""
Explanation: Regressão Linear Simples - Trabalho
Estudo de caso: Seguro de automóvel sueco
Agora, sabemos como implementar um modelo de regressão linear simples. Vamos aplicá-lo ao conjunto de dados do seguro de automóv... |
Copper-Head/the-three-stooges | Sandbox.ipynb | mit | # Load the network
from network import NetworkType, Network
# 3-layer LSTM
net = Network(NetworkType.LSTM, input_dim_file='data/onehot_size.npy')
net.set_parameters('data/seqgen_lstm.pkl')
char2ind = pickle.load(open("data/char_to_ind.pkl"))
# SimpleRecurrent LK
# net = Network(input_dim_file='data/lk_onehot_size.np... |
statsmodels/statsmodels.github.io | v0.13.2/examples/notebooks/generated/statespace_fixed_params.ipynb | bsd-3-clause | %matplotlib inline
from importlib import reload
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from pandas_datareader.data import DataReader
"""
Explanation: Estimating or specifying parameters in state space models
In this notebook we show how to fix specific val... |
littlepea/python-refactoring-talk | refactoring.ipynb | mit | """Movie Reviews.
Usage: movie_reviews.py <title>
movie_reviews.py (-h | --help)
movie_reviews.py --version
Arguments:
<title> Movie title
Options:
-h --help Show this screen.
--version Show version.
"""
from docopt import docopt
from TwitterSearch import *
from dateutil impor... |
Kaggle/learntools | notebooks/feature_engineering/raw/ex1.ipynb | apache-2.0 | # Set up code checking
from learntools.core import binder
binder.bind(globals())
from learntools.feature_engineering.ex1 import *
"""
Explanation: Introduction
In the exercise, you will work with data from the TalkingData AdTracking competition. The goal of the competition is to predict if a user will download an app... |
danielfrg/word2vec | examples/word2vec.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
"""
Explanation: word2vec
This notebook is equivalent to demo-word.sh, demo-analogy.sh, demo-phrases.sh and demo-classes.sh from the Google examples.
End of explanation
"""
import word2vec
"""
Explanation: Training
Download some data, for example: http://mattmahoney.net/dc/text8.z... |
DJCordhose/ai | notebooks/nlp/0-imdb-parse.ipynb | mit | # Based on
# https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/6.1-using-word-embeddings.ipynb
# https://machinelearningmastery.com/develop-word-embeddings-python-gensim/
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import tensorflow as tf
tf.logging.se... |
pschragger/big-data-python-class | Lectures/Lecture 10 - Graph Algorithms/Python Graphs - networkx intro .ipynb | mit | #Example Small social newtork as a connection matrix
sc1 = ([(0, 1, 1, 0, 0, 0, 0),
(1, 0, 1, 1, 0, 0, 0),
(1, 1, 0, 0, 0, 0, 0),
(0, 1, 0, 0, 1, 1, 1),
(0, 0, 0, 1, 0, 1, 0),
(0, 0, 0, 1, 1, 0, 1),
(0, 0, 0, 1, 0, 1, 0)])
"""
Explanation: Using the graph from figure 10... |
SunPower/pvfactors | docs/tutorials/pvfactors_demo.ipynb | bsd-3-clause | # Import external libraries
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
import pandas as pd
import warnings
warnings.filterwarnings("ignore", category=RuntimeWarning)
# Settings
%matplotlib inline
np.set_printoptions(precision=3, linewidth=300)
"""
Explanation: pvfactors: Jupyter... |
gschivley/Index-variability | Notebooks/archive/Trying geopandas to assign NERC regions.ipynb | bsd-3-clause | # EIA NERC region shapefile, which has an "Indeterminate" region
# path = os.path.join(data_path, 'NERC_Regions_EIA', 'NercRegions_201610.shp')
# regions = gpd.read_file(path)
# regions.crs
path = os.path.join(data_path, 'nercregions', 'NERCregions.shp')
regions_nerc = gpd.read_file(path)
regions_nerc['nerc'] = regio... |
gaufung/Data_Analytics_Learning_Note | DesignPattern/ChainofResponsibilityPattern.ipynb | mit | class manager():
def __init__(self, name):
self.name = name
def setSuccessor(self, successor):
self.successor = successor
def handleRequest(self, request):
pass
class lineManager(manager):
def handleRequest(self, request):
if request.requestType == 'DaysOff' and request.n... |
CopernicusMarineInsitu/INSTACTraining | PythonNotebooks/PlatformPlots/Read_TimeSeries_2.ipynb | mit | %matplotlib inline
import netCDF4
import matplotlib.pyplot as plt
"""
Explanation: Reading a remote file using OPeNDAP
Now we will read a remote file using the OPeNDAP protocol. The advantage is that the file has not to be downloaded on your computer, while you can access the variables you want using it as it were on ... |
SheffieldML/GPyOpt | manual/GPyOpt_context.ipynb | bsd-3-clause | %pylab inline
import GPyOpt
from numpy.random import seed
func = GPyOpt.objective_examples.experimentsNd.alpine1(input_dim=5)
"""
Explanation: GPyOpt: using context variables
Javier Gonzalez and Rodolphe Jenatton, Amazon.com
Last updated Monday, July 2017
In this notebook we are going to see how to used GPyOpt to s... |
empirical-org/WikipediaSentences | notebooks/BERT-4.1 Experiments Multilabel-QuillNLP.ipynb | agpl-3.0 | from multilabel import EATINGMEAT_BECAUSE_MAP, EATINGMEAT_BUT_MAP, JUNKFOOD_BECAUSE_MAP, JUNKFOOD_BUT_MAP
LABEL_MAP = JUNKFOOD_BUT_MAP
BERT_MODEL = 'bert-base-uncased'
BATCH_SIZE = 16 if "base" in BERT_MODEL else 2
GRADIENT_ACCUMULATION_STEPS = 1 if "base" in BERT_MODEL else 8
MAX_SEQ_LENGTH = 100
PREFIX = "junkfood_b... |
ethen8181/machine-learning | big_data/h2o/h2o_api_walkthrough.ipynb | mit | # code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(plot_style = False)
os.chdir(path)
# 1. magic for inline plot
# 2. magic to print ver... |
intel-analytics/analytics-zoo | pyzoo/zoo/chronos/use-case/AIOps/AIOps_anomaly_detect_unsupervised.ipynb | apache-2.0 | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
df_1932 = pd.read_csv("m_1932.csv", header=None, usecols=[1,2,3], names=["time_step", "cpu_usage","mem_usage"])
"""
Explanation: Unsupervised Anomaly Detection
Anomaly detection detects data points in data that does n... |
thalesians/tsa | src/jupyter/python/foundations/linear-algebra-2.ipynb | apache-2.0 | # Copyright (c) Thalesians Ltd, 2018-2019. All rights reserved
# Copyright (c) Paul Alexander Bilokon, 2018-2019. All rights reserved
# Author: Paul Alexander Bilokon <paul@thalesians.com>
# Version: 2.0 (2019.04.19)
# Previous versions: 1.0 (2018.08.03)
# Email: education@thalesians.com
# Platform: Tested on Windows 1... |
james-prior/cohpy | 20160708-dojo-user-input-loop-with-iter-partial-input-prompt-sentinel.ipynb | mit | from functools import partial
def convert(s):
converters = (int, float)
for converter in converters:
try:
value = converter(s)
except ValueError:
pass
else:
return value
return s
def process_input(s):
value = convert(s)
prin... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-3/cmip6/models/sandbox-3/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-3', 'sandbox-3', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: TEST-INSTITUTE-3
Source ID: SANDBOX-3
Topic: Ocean
Sub-Topics: Timestepp... |
mdalvi/financial-analysis-and-algo-trading | visualization_matplotlib_pandas/matplotlib_notes.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
x = np.linspace(0,5,11)
y = x ** 2
x
y
"""
Explanation: Matplotlib Basics
End of explanation
"""
plt.plot(x, y)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('Title')
plt.show()
# Multiplot on same canvas
plt.subplot(1,2,1) # rows, c... |
albahnsen/ML_RiskManagement | notebooks/01-IntroMachineLearning.ipynb | mit | # Import libraries
%matplotlib inline
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
plt.style.use('ggplot')
"""
Explanation: 01 - Introduction to Machine Learning
by Alejandro Correa Bahnsen & Iván Torroledo
version 1.2, Feb 2018
Part of the class Machine Learning for Risk Management
This... |
henchc/Rediscovering-Text-as-Data | 09-Topic-Modeling/01-Topic-Modeling.ipynb | mit | metadata_tb = Table.read_table('data/txtlab_Novel150_English.csv')
metadata_tb.show(5)
"""
Explanation: Topic Modeling in Python
In Lisa Rhody's article, "Topic Modeling and Figurative Language", she uses LDA topic modeling to look at ekphrasis poetry. She argues that ekphrasis poetry is particulary well-suited to an ... |
emsi/ml-toolbox | random/Atmosfera/LSTM-10-conv.ipynb | agpl-3.0 | root_services=np.sort(np.unique(Y))
# skonstruuj odwrtotny indeks kategorii głównych
services_idx={root_services[i]: i for i in range(len(root_services))}
# Zamień
Y=[services_idx[y] for y in Y]
Y=to_categorical(Y)
Y.shape
top_words = 5000
classes=Y[0,].shape[0]
print(classes)
# max_length (98th percentile is 476)... |
Merinorus/adaisawesome | Homework/03 - Interactive Viz/HW3_Interactive_Viz.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
# We will read json files, for instance API keys stored in our computers for using Google Maps API, so they're not publicly visible
import json
# Geolocation
import geopy
from geopy.geocoders import geonames
import math
import logging
p3_grant_export_data = pd.read_csv("P3_GrantE... |
FireCARES/data | sources/parcels/notebooks/parcel-loading.ipynb | mit | import psycopg2 as pg
import pandas as pd
import os
conn = pg.connect('service=parcels')
conn_str = os.environ.get('PARCELS_CONNECTION')
"""
Explanation: Parcel loading
Given a set of parcels (assumes GDB format) from the parcel provider, this notebook will load individual features (from the parcel provider -- curren... |
kunaltyagi/SDES | notes/python/p_norvig/logic/Cheryl-and-Eve.ipynb | gpl-3.0 | # Albert and Bernard just became friends with Cheryl, and they want to know when her birthday is.
# Cheryl gave them a set of 10 possible dates:
from __future__ import division, print_function
CHERYL_DATES = {
'May 15', 'May 16', 'May 19',
'June 17', 'June 18',
'July 14', 'July 16',
'August ... |
mrcslws/nupic.research | projects/archive/dynamic_sparse/notebooks/ExperimentAnalysis-ReplicateHSD-2x.ipynb | agpl-3.0 | %load_ext autoreload
%autoreload 2
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import tabulate
import pprint
import click
import numpy as np
import pandas as pd
from ray.tune.commands import *
from nupic.research.frameworks.dynamic... |
tensorflow/workshops | extras/amld/notebooks/solutions/2_keras.ipynb | apache-2.0 | # In Jupyter, you would need to install TF 2.0 via !pip.
%tensorflow_version 2.x
import tensorflow as tf
import json, os
# Tested with TensorFlow 2.1.0
print('version={}, CUDA={}, GPU={}, TPU={}'.format(
tf.__version__, tf.test.is_built_with_cuda(),
# GPU attached?
len(tf.config.list_physical_devices('GPU... |
dombrno/PG | Notebooks/test_DOS.ipynb | bsd-2-clause | Tc_mf = meV_to_K(0.5*250)
print '$T_c^{MF} = $', Tc_mf, "K"
print r"$T_{KT} = $", Tc_mf/10.0, "K"
"""
Explanation: TB Model
We pick the following parameters:
+ hopping constant $ t= 250$ meV
+ $\Delta = 1.0 t$ so that $T_c^{MF} = 0.5 t$, and so that $\xi_0 \simeq a_0$
+ $g = -0.25$, unitless, so as to match the artic... |
pm4py/pm4py-core | notebooks/3_process_discovery.ipynb | gpl-3.0 | import pandas as pd
import pm4py
df = pm4py.format_dataframe(pd.read_csv('data/running_example.csv', sep=';'), case_id='case_id',activity_key='activity',
timestamp_key='timestamp')
bpmn_model = pm4py.discover_bpmn_inductive(df)
pm4py.view_bpmn(bpmn_model)
"""
Explanation: Process Discovery... |
nilbody/h2o-3 | h2o-py/demos/kmeans_aic_bic_diagnostics.ipynb | apache-2.0 | import h2o
import imp
from h2o.estimators.kmeans import H2OKMeansEstimator
# Start a local instance of the H2O engine.
h2o.init();
"""
Explanation: Much data produced is unlabeled data, data where the target vale or class is unknown. Unsupervised learning gives us the tools to find hidden structure in unlabeled data... |
ozorich/phys202-2015-work | assignments/assignment03/NumpyEx01.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
"""
Explanation: Numpy Exercise 1
Imports
End of explanation
"""
def checkerboard(size):
"""Return a 2d checkboard of 0.0 and 1.0 as a NumPy array"""
# Y... |
wazaahhh/bountyhunt | jupyter/bhunt_dynamics.ipynb | mit | 1000*(1/10**1.5)
"""
Explanation: CDF(X > x) = 1/x^mu = x^-mu
PDF(X=x) = 1/x^(mu+1) = x^-(mu +1)
alpha = mu +1
mu = 1.5 # coinbase
CDF (X > x = 5) = 1/5^1.5
End of explanation
"""
bins = np.arange(1,max(date)+1,7)
H = pl.histogram(date,bins = bins)
x = H[1][:-1]
y = H[0]
c = (y>0)*(x > 30.0)
lx = np.log10(x[c] - m... |
fascow/bruker_compass_scripts | LibraryEditor/Library_Spectra_Export_process_results.ipynb | mit | folder = 'D:\data\Libraries\Example_Xpec'
archive = 'all_spectra.json'
"""
Explanation: Library Spectra Export process results
Read spectra files exported from the Bruker Spectra Library. All spectra files shall end with ".spectrum" and be located in one folder. Only one spectrum per file.
Please, specify the folder ... |
CNS-OIST/STEPS_Example | user_manual/source/well_mixed.ipynb | gpl-2.0 | import steps.model as smodel
"""
Explanation: Well-Mixed Reaction Systems
The simulation script described in this chapter is available at STEPS_Example repository.
In this chapter, we'll use some simple classical reaction systems as examples
to introduce the basics of using STEPS. More specifically, we'll focus on rea... |
jarthurgross/arxiv-submission-modeling | arxiv-modeling.ipynb | mit | from collections import Counter
import itertools as it
from IPython.display import display
import pandas
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
%matplotlib inline
%config InlineBackend.figure_formats = ['svg']
"""
Explanation: When will monthly arXiv submissions hit 10,000?
End of... |
PyladiesMx/Pyladies_ifc | 1. PrimitiveTypes_and_operators/objetos simples y operaciones básicas.ipynb | mit | import turtle
ventana = turtle.Screen()
ventana.bgcolor('lightblue')
ventana.title('Hello Erika!')
erika = turtle.Turtle()
erika.color('blue')
erika.pensize(5)
erika.forward(100)
erika.left(90)
erika.forward(100)
"""
Explanation: Bienvenid@s!!
En la reunión de hoy aprenderemos acerca de python y sus cimientos. Verem... |
tanmay987/deepLearning | intro-to-tflearn/TFLearn_Sentiment_Analysis.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
print(1)
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a ... |
feststelltaste/software-analytics | demos/20210630_WeAreDevelopersWorldCongress/Parsing and Analysing vmstat Data the Easy Way.ipynb | gpl-3.0 | %less ../dataset/vmstat_loadtest.log
"""
Explanation: Idea
Using the vmstat command line utility to quickly determine the root cause of performance problems.
End of explanation
"""
from ozapfdis.linux import vmstat
stats = vmstat.read_logfile("../dataset/vmstat_loadtest.log")
stats.head()
"""
Explanation: Data Inp... |
wem3/gems_vs_bomb | rez/all_bandits.ipynb | mit | # imports / display plots in cell output
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as ss
import pandas as pd
import seaborn as sns
import statsmodels
"""
Explanation: Reinforcement Learning Models of Social Group Preferences
Bandit Experiments 1-7
End of explanation
"""
... |
rpestourie/filters_AVX | Final_Report.ipynb | mit | import sys
import os.path
sys.path.append(os.path.join('.', 'util'))
import set_compiler
set_compiler.install()
import pyximport
pyximport.install()
from timer import Timer
import pylab as plt
import numpy as np
"""
Explanation: Gaussian and Bilateral filters with AVX
Table of Contents
<p><div class="lev1"><a href... |
sdpython/pyensae | _doc/notebooks/pyensae_text2table.ipynb | mit | import random, pandas
text = [ "one","two","three","four","five","six","seven","eight","nine","ten" ]
data = [ { "name": text[random.randint(0,9)], "number": random.randint(0,99)} \
for i in range(0,10000) ]
df = pandas.DataFrame(data)
df.head(n=3)
df.to_csv("flatfile.txt", sep="\t", encoding="... |
mdpiper/dakota-tutorial | notebooks/4-WMT.ipynb | mit | %pylab inline
import os
"""
Explanation: <img src="images/csdms_logo.jpg">
Example 4
Let's use IPython Notebook to download model output
from WMT and examine the results.
Set up with pylab magic, plus other global imports:
End of explanation
"""
os.chdir(os.path.join('..', 'examples', '4-WMT'))
os.getcwd()
"""
Exp... |
yangdikun/magLab | MagDipole.ipynb | mit | def MagneticMonopoleField(obsloc,poleloc=(0.,0.,0.),Q=1):
# relative obs. loc. to pole, assuming pole at origin
dx, dy, dz = obsloc[0]-poleloc[0], obsloc[1]-poleloc[1], obsloc[2]-poleloc[2]
r = np.sqrt(dx**2+dy**2+dz**2)
Bx = Q * 1e-7 / r**2 * dx
By = Q * 1e-7 / r**2 * dy
Bz = Q * 1e-7 / r**2 * ... |
deeplook/notebooks | mapping/here_maps_api_explorer_no_creds.ipynb | mit | import os
msg = "Error: Environment variable {} not found"
for varname in ["HEREMAPS_APP_ID", "HEREMAPS_APP_CODE"]:
assert os.getenv(varname), msg.format(varname)
import folium
import requests
import ipywidgets
print("You are ready to go!")
"""
Explanation: HERE Map Tiles Rest API Explorer
This notebook is inten... |
JakeColtman/BayesianSurvivalAnalysis | PyMC Part 2 Done.ipynb | mit | running_id = 0
output = [[0]]
with open("E:/output.txt") as file_open:
for row in file_open.read().split("\n"):
cols = row.split(",")
if cols[0] == output[-1][0]:
output[-1].append(cols[1])
output[-1].append(True)
else:
output.append(cols)
output = out... |
mne-tools/mne-tools.github.io | 0.19/_downloads/5405ec123125b53ac343bbc1ba002342/plot_stats_spatio_temporal_cluster_sensors.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
# Jona Sassenhagen <jona.sassenhagen@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mne.viz import plot_topomap
import mne
from mne.stats import spatio_... |
ronnydw/data-science-projects | class-central-survey-2016-17/Graduated.ipynb | mit | df = pd.read_csv('raw/2016-17-ClassCentral-Survey-data-noUserText.csv', decimal=',', encoding = "ISO-8859-1")
"""
Explanation: Read the survey data
End of explanation
"""
df['What is your level of formal education?'].value_counts()
target_name = 'Graduated'
graduated = (pd.to_numeric(df['What is your level of forma... |
mediagit2016/workcamp-maschinelles-lernen-grundlagen | 18-05-14-ml-workcamp/sensor-daten-10/Projekt-Sensordaten-Feature-Selektion-Workcamp-ML.ipynb | gpl-3.0 | # Laden der entsprechenden Module (kann etwas dauern !)
# Wir laden die Module offen, damit man einmal sieht, was da alles benötigt wird
# Allerdings aufpassen, dann werden die Module anderst angesprochen wie beim Standard
# zum Beispiel pyplot und nicht plt
from matplotlib import pyplot
pyplot.rcParams["figure.figsize... |
amitkaps/applied-machine-learning | Module-03e-Model-RandomForest.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('fivethirtyeight')
df = pd.read_csv("data/historical_loan.csv")
# refine the data
df.years = df.years.fillna(np.mean(df.years))
#Load the preprocessing module
from sklearn import preprocessing
categorical_variable... |
FowlerLab/Enrich2 | docs/notebooks/min_count.ipynb | bsd-3-clause | % matplotlib inline
from __future__ import print_function
import os.path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from enrich2.variant import WILD_TYPE_VARIANT
import enrich2.plots as enrich_plot
pd.set_option("display.max_rows", 10) # rows shown when pretty-printing
"""
Explanation: Sel... |
pysal/spaghetti | notebooks/quickstart.ipynb | bsd-3-clause | %config InlineBackend.figure_format = "retina"
%load_ext watermark
%watermark
import geopandas
import libpysal
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import matplotlib_scalebar
from matplotlib_scalebar.scalebar import ScaleBar
import shapely
import spaghetti
%matplotlib in... |
Upward-Spiral-Science/uhhh | code/.ipynb_checkpoints/[Assignment 14] JM-checkpoint.ipynb | apache-2.0 | import numpy as np
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import csv
data = open('../data/data.csv', 'r').readlines()
fieldnames = ['x', 'y', 'z', 'unmasked', 'synapses']
reader = csv.reader(data)
reader.next()
rows = [[int(col) for col in row] for row in reader]... |
wgong/open_source_learning | projects/Open_Food/data-incubator-challenge-100k.ipynb | apache-2.0 | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: Data Incubator Fellowship Semifinalist Challenge
<a href="mailto:wen.gong@gmail.com"><font size=+2>Wen Gong</font></a>
Motivation
<br>
<font color=red size=+2>Know what we eat, </font>
<font color=green size=+2> Gain insight into food, </... |
Boialex/MIPT-ML | hw3/Contest.ipynb | gpl-3.0 | import pandas as pd
from sklearn import model_selection, metrics
import numpy as np
import matplotlib.pyplot as plt
import seaborn
import xgboost
import os
%pylab inline
train = pd.read_csv("train.tsv")
test = pd.read_csv("test.tsv")
sample_submission = pd.read_csv("sample_submission.tsv")
sample_submission_a = pd.rea... |
minxuancao/shogun | doc/ipython-notebooks/regression/Regression.ipynb | gpl-3.0 | %pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from cycler import cycler
# import all shogun classes
from modshogun import *
slope = 3
X_train = rand(30)*10
y_train = slope*(X_train)+random.randn(30)*2+2
y_true = slope*(X_train)+2
X_test = concatenate((linspace... |
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