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GoogleCloudPlatform/mlops-on-gcp | workshops/tfx-caip-tf23/lab-02-tfx-pipeline/solutions/lab-02.ipynb | apache-2.0 | import yaml
# Set `PATH` to include the directory containing TFX CLI and skaffold.
PATH=%env PATH
%env PATH=/home/jupyter/.local/bin:{PATH}
"""
Explanation: Continuous training with TFX and Google Cloud AI Platform
Learning Objectives
Use the TFX CLI to build a TFX pipeline.
Deploy a TFX pipeline version without tun... |
RobinKa/tfga | notebooks/em.ipynb | mit | ga = GeometricAlgebra([-1, 1, 1, 1])
"""
Explanation: Introduction
Classical electromagnetism is most often described using maxwell's equations. Instead, we can also describe it using a Lagrange density and an action which is the spacetime integral over the Lagrange density.
The field is represented by a 4-vector in t... |
NYUDataBootcamp/Projects | MBA_S17/Pittman-Renewable+Energy (2).ipynb | mit | import sys # system module
import pandas as pd # data package
import matplotlib.pyplot as plt # graphics module
import datetime as dt # date and time module
import numpy as np # foundation for Pandas
%matplotlib inline
# ch... |
sdpython/ensae_teaching_cs | _doc/notebooks/expose/expose_vigenere.ipynb | mit | def code_vigenere ( message, cle, decode = False) :
message_code = ""
for i,c in enumerate(message) :
d = cle[ i % len(cle) ]
d = ord(d) - 65
if decode : d = 26 - d
message_code += chr((ord(c)-65+d)%26+65)
return message_code
def DecodeVigenere(message, cle):
return ... |
benneely/qdact-basic-analysis | notebooks/primarydiagnoses.ipynb | gpl-3.0 | from IPython.core.display import display, HTML;from string import Template;
HTML('<script src="//d3js.org/d3.v3.min.js" charset="utf-8"></script>')
css_text2 = '''
#main { float: left; width: 750px;}#sidebar { float: right; width: 100px;}#sequence { width: 600px; height: 70px;}#legend { padding: 10px 0 0 3px;}... |
stanfordnmbl/osim-rl | examples/legacy/train.arm.ipynb | mit | import osim
import numpy as np
import sys
# Keras libraries
from keras.optimizers import Adam
import numpy as np
from helpers import *
from rl.agents import DDPGAgent
from rl.memory import SequentialMemory
from rl.random import OrnsteinUhlenbeckProcess
from keras.optimizers import RMSprop
import argparse
import m... |
enlighter/learnML | mini-projects/p0 - titanic survival exploration/.ipynb_checkpoints/Titanic_Survival_Exploration-checkpoint.ipynb | mit | import numpy as np
import pandas as pd
# RMS Titanic data visualization code
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# Load the dataset
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)
# Print the first few entries of the RMS Titanic data... |
Benedicto/ML-Learning | Classifier_5_boosting_assignment_2.ipynb | gpl-3.0 | import graphlab
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Boosting a decision stump
The goal of this notebook is to implement your own boosting module.
Brace yourselves! This is going to be a fun and challenging assignment.
Use SFrames to do some feature engineering.
Modify the decision tree... |
gfrubi/FM2 | Notebooks/Ejemplo-Serie-Fourier-Epiciclos.ipynb | gpl-3.0 | from presentation import *
import numpy as np
"""
Explanation: Epiciclos y series de Fourier
El código usado en este notebook ha sido adaptado desde la versión original disponible aquí.
Una introducción general a los epiciclos y su historia puede encontrarse en la correspondiente página en Wikipedia. Un buen video (e... |
jamesjia94/BIDMach | tutorials/NVIDIA/.ipynb_checkpoints/GeneralDNNregression-checkpoint.ipynb | bsd-3-clause | import BIDMat.{CMat,CSMat,DMat,Dict,IDict,Image,FMat,FND,GDMat,GMat,GIMat,GSDMat,GSMat,HMat,IMat,Mat,SMat,SBMat,SDMat}
import BIDMat.MatFunctions._
import BIDMat.SciFunctions._
import BIDMat.Solvers._
import BIDMat.JPlotting._
import BIDMach.Learner
import BIDMach.models.{FM,GLM,KMeans,KMeansw,ICA,LDA,LDAgibbs,Model,NM... |
NekuSakuraba/my_capstone_research | subjects/em/multivariate t - draft05 - Mixtures.ipynb | mit | def find_df(v, p, u, tau):
return -digamma(v/2.) + log(v/2.) + (tau * (log(u) - u)).sum()/tau.sum() + 1 + (digamma((v+p)/2.)-log((v+p)/2.))
u_test = np.array([[1,1], [2,2], [3,3]])
tau_test = np.array([[4,4], [5,5], [6,6]])
find_df(1, 2, u_test, tau_test)
def get_random(X):
size = len(X)
idx = np.random.... |
ondrolexa/sg2 | 11_Strain_ellipse.ipynb | mit | %pylab inline
from sg2lib import *
"""
Explanation: Strain ellipse
Polar decomposition
Pluging LEFT polar decomposition $\boldsymbol{F} = \boldsymbol{V} \cdot \boldsymbol{R}$ to equation for LEFT Cauchy-Green deformation tensor $\boldsymbol{B}=\boldsymbol{F}\cdot\boldsymbol{F}^T$ results in:
$$\boldsymbol{B}=\boldsymb... |
ocelot-collab/ocelot | demos/ipython_tutorials/7_lattice_design.ipynb | gpl-3.0 | # the output of plotting commands is displayed inline within
# frontends, directly below the code cell that produced it.
%matplotlib inline
from time import time
# this python library provides generic shallow (copy)
# and deep copy (deepcopy) operations
from copy import deepcopy
# import from Ocelot main modules... |
ocean-color-ac-challenge/evaluate-pearson | evaluation-participant-b.ipynb | apache-2.0 | w_412 = 0.56
w_443 = 0.73
w_490 = 0.71
w_510 = 0.36
w_560 = 0.01
"""
Explanation: E-CEO Challenge #3 Evaluation
Participant B
Weights
Define the weight of each wavelength
End of explanation
"""
run_id = '0000002-150625115710650-oozie-oozi-W'
run_meta = 'http://sb-10-16-10-55.dev.terradue.int:50075/streamFile/ciop/ru... |
esa-as/2016-ml-contest | esaTeam/esa_Submission01b.ipynb | apache-2.0 | # Import
from __future__ import division
get_ipython().magic(u'matplotlib inline')
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams['figure.figsize'] = (20.0, 10.0)
inline_rc = dict(mpl.rcParams)
from classification_utilities import make_facies_log_plot
import pandas as pd
import numpy as np
impo... |
ageron/ml-notebooks | 15_autoencoders.ipynb | apache-2.0 | # To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
import sys
try:
# %tensorflow_version only exists in Colab.
%tensorflow_version 1.x
except Exception:
pass
# to make this notebook's output stable across... |
ES-DOC/esdoc-jupyterhub | notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'gfdl-esm4', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: GFDL-ESM4
Topic: Landice
Sub-Topics: Glaciers, Ice.
P... |
tensorflow/docs-l10n | site/zh-cn/hub/tutorials/spice.ipynb | apache-2.0 | #@title Copyright 2020 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 ... |
tclaudioe/Scientific-Computing | SC1v2/Bonus - 07-08 - Gradient Descent and Nonlinear Least-Square.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
import scipy.linalg as spla
%matplotlib inline
# https://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets
from sklearn import datasets
import ipywidgets as widgets
from ipywidgets import interact, interact_manual, RadioButtons
import matplotlib as m... |
UDST/activitysim | activitysim/examples/example_estimation/notebooks/19_atwork_subtour_dest.ipynb | bsd-3-clause | import larch # !conda install larch #for estimation
import pandas as pd
import numpy as np
import yaml
import larch.util.excel
import os
"""
Explanation: Estimating At-Work Subtour Destination Choice
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes runnin... |
seewhydee/ntuphys_nb | jupyter/gradqm/entanglement.ipynb | gpl-3.0 | import numpy as np
a = np.array([2., -1.]) # vector in a 2D space
b = np.array([1., 2., 3.]) # vector in a 3D space
psi = np.kron(a, b) # vector in the 6D tensor product space
print(psi)
"""
Explanation: Numerical Studies of Quantum Entanglement
In this notebook, we will perform some numerical stu... |
nehal96/Deep-Learning-ND-Exercises | Sentiment Analysis/Handwritten Digit Recognition with TFLearn and MNIST/handwritten-digit-recognition-with-tflearn.ipynb | mit | # Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
"""
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/text_classification/solutions/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 paramete... |
NeuroDataDesign/seelviz | albert/prob/Probability+Based.ipynb | apache-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
from dipy.data import read_stanford_labels
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
from dipy.tracking import utils
from dipy.tracking.local import (ThresholdTissueClassifier, LocalTracking)
hardi_img, gtab, labels_img = read_stanford_labels(... |
cyucheng/skimr | jupyter/4_LDA_analysis.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import csv
from textblob import TextBlob, Word
import pandas as pd
import sklearn
import pickle
import numpy as np
import scipy
import nltk.data
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm im... |
raphaelshirley/regphot | examples/XID+R.ipynb | mit | from regphot import git_version
print("This notebook was run with regphot version: \n{}".format(git_version()))
from regphot.utils import getPlateFits
from astropy.table import Table
from astropy.wcs import WCS
from astropy.io import fits
from astropy.coordinates import SkyCoord
from astropy.nddata import Cutout2D
fr... |
hetland/python4geosciences | materials/5_maps.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import cartopy
import cartopy.crs as ccrs # commonly used shorthand
import cartopy.feature as cfeature
"""
Explanation: Maps
1. Introduction
Maps are a way to present information on a (roughly) spherical earth on a flat plane, like a page or a sc... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/custom/sdk-custom-image-classification-batch.ipynb | apache-2.0 | import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = "--user"
! pip install {U... |
GoogleCloudPlatform/training-data-analyst | quests/endtoendml/labs/5_train_keras.ipynb | apache-2.0 | # Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.1
"""
Explanation: <h1>Training Keras model on Cloud AI Platform</h1>
<h2>Learning Objectives</h2>
<ol>
<li> Create a BigQuery Dataset and Google Cloud Storage Bucket</li>
<li> Export from BigQuery to CSVs in GCS</li>
<li> Trainin... |
prk327/CoAca | 7_Lambda_Functions_Pivot_Tables.ipynb | gpl-3.0 | # Loading libraries and files
import numpy as np
import pandas as pd
market_df = pd.read_csv("../global_sales_data/market_fact.csv")
customer_df = pd.read_csv("../global_sales_data/cust_dimen.csv")
product_df = pd.read_csv("../global_sales_data/prod_dimen.csv")
shipping_df = pd.read_csv("../global_sales_data/shipping_... |
neuro-data-science/neuroML | notebooks/introductory_nilearn.ipynb | apache-2.0 | from nilearn import datasets
# By default 2nd subject will be fetched
haxby_dataset = datasets.fetch_haxby()
"""
Explanation: Nilearn
If you're working on NeuroImaging data, you should check another Python library, Nilearn, that is design for fast and easy statistical learning on NeuroImaging data. It leverages the ... |
kingb12/languagemodelRNN | report_notebooks/encdec_noing6_bow_200_512_04drb.ipynb | mit | report_file = '/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing6_bow_200_512_04drb/encdec_noing6_bow_200_512_04drb.json'
log_file = '/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing6_bow_200_512_04drb/encdec_noing6_bow_200_512_04drb_logs.json'
import json
import matp... |
ES-DOC/esdoc-jupyterhub | notebooks/cams/cmip6/models/sandbox-2/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cams', 'sandbox-2', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: CAMS
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties:... |
ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/sandbox-3/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'sandbox-3', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: SANDBOX-3
Topic: Atmoschem
Sub-Topics: Trans... |
mercybenzaquen/foundations-homework | databases_hw/Homework_3_graded.ipynb | mit | !pip3 install bs4
from bs4 import BeautifulSoup
from urllib.request import urlopen
html_str = urlopen("http://static.decontextualize.com/widgets2016.html").read()
info = BeautifulSoup(html_str, "html.parser")
"""
Explanation: Graded = 9/9
Homework assignment #3
These problem sets focus on using the Beautiful Soup lib... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/sdk/sdk_automl_text_sentiment_analysis_batch.ipynb | apache-2.0 | import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex SDK: AutoML training text sentiment analysis model for batch prediction
<table align="le... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/gapic/custom/showcase_custom_tabular_regression_online_container.ipynb | apache-2.0 | import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex client library: Custom training tabular regression model with custom container for o... |
johnnycakes79/pyops | dashboard/pandas-highcharts-examples.ipynb | bsd-3-clause | %matplotlib inline
import string
import numpy as np
import pandas as pd
print pd.__version__
"""
Explanation: Pandas DataFrame plotting with Highcharts
pandas_highcharts is a Python library to turn your pandas DataFrame into a suited JSON for Highcharts, a Javascript library for interactive charts.
Before introducin... |
13522364778/liupengyuan.github.io | chapter1/homework/localization/3-22/201611680254,3-22.ipynb | mit | name=input('请输入你的姓名')
print('hello',name)
birthday=float(input('请输入你的生日'))
if 3.20<birthday<4.20:
print(name,'你是非常热情的白羊座')
elif 4.20<birthday<5.21:
print(name,'你是非常稳重的金牛座')
elif 5.21<birthday<6.22:
print(name,'你是非常纠结的双子座')
elif 6.22<birthday<7.23:
print(name,'你是非常暖心的巨蟹座')
elif 7.23<birthday<8.23:
p... |
zhuanxuhit/deep-learning | tv-script-generation/.ipynb_checkpoints/dlnd_tv_script_generation-checkpoint.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
aryarohit07/machine-learning-with-python | linear_regression/linear_regression_gradient_descent_with_multiple_variables.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
df = pd.read_csv('ex1data2.txt', header=None)
print(df.head())
#Lets try to visualize the data
fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(df[0], df[1], df[2])
ax.set_zlabel('price')
plt.xlabel('size of the house (in squar... |
PMEAL/OpenPNM | examples/simulations/percolation/D_meniscus_model_comparison.ipynb | mit | import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import sympy as syp
from sympy import lambdify, symbols
from sympy import atan as sym_atan
from sympy import cos as sym_cos
from sympy import sin as sym_sin
from sympy import sqrt as sym_sqrt
from sympy import pi as sym_pi
from ipyw... |
nagordon/mechpy | tutorials/Curve_fitting_and_Optimization_with_python.ipynb | mit | # import modules
import numpy as np
from numpy import *
import matplotlib.pyplot as plt
from matplotlib.pyplot import *
import scipy
from scipy.optimize import curve_fit
from scipy.optimize import fmin
%matplotlib inline
import matplotlib as mpl
mpl.rcParams['figure.figsize'] = (12,8)
mpl.rcParams['font.size'] = 14
mpl... |
IanHawke/maths-with-python | 02-programs.ipynb | mit | import math
x = math.sin(1.2)
"""
Explanation: Programs
Using the Python console to type in commands works fine, but has serious drawbacks. It doesn't save the work for the future. It doesn't allow the work to be re-used. It's frustrating to edit when you make a mistake, or want to make a small change. Instead, we wan... |
johnhw/summerschool2017 | dynamic/kalman_filter.ipynb | mit | # import the things we need
from __future__ import print_function, division
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import pykalman
import ipywidgets
import IPython
import matplotlib, matplotlib.colors
matplotlib.rcParams['figure.figsize'] = (14.0, 8.0)
%matplotlib inline
from scipy.... |
TheMitchWorksPro/DataTech_Playground | PY_Basics/TMWP_List_Comprehension_Examples.ipynb | mit | # libraries used in the Notebook
import numpy as np
[i for i in range(3,10)] # in real code, range(3,10) would probably be a list or iterable that is the source
print([i for i in range(3,10)])
[i**2 for i in range(3,10) if i > 6] # squares all values in range(3,10) but only if original value was > 6
# example from... |
zauonlok/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.... |
Intel-Corporation/tensorflow | tensorflow/lite/g3doc/tutorials/model_maker_audio_classification.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... |
christophmark/bayesloop | docs/source/tutorials/hyperstudy.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt # plotting
import seaborn as sns # nicer plots
sns.set_style('whitegrid') # plot styling
import numpy as np
import bayesloop as bl
S = bl.HyperStudy()
S.loadExampleData()
L = bl.om.Poisson('accident_rate', bl.oint(0, 6, 1000))
T = bl.tm.SerialTransit... |
suresh/notes | python/Monte Carlo Explainer.ipynb | mit | simple = stats.uniform(loc=2, scale=3)
errscale = 0.25
err = stats.norm(loc=0, scale=errscale)
# cannot analytically convolve continuous PDFs in general.
# so we now make a probability mass function on a fine grid for fft convolution
delta = 1e-4
big_grid = np.arange(-10, 10, delta)
pmf1 = simple.pdf(big_grid) * del... |
mathemage/h2o-3 | h2o-py/demos/Predict_w_Unseen_Categorical_Levels.ipynb | apache-2.0 | import h2o, pandas, pprint, operator, numpy as np, matplotlib.pyplot as plt
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o.estimators.random_forest import H2ORandomForestEstimator
from h2o.estimators.deeplearning import H2ODeepLearningEs... |
tdeoskar/NLP1-2017 | pytorch-tutorial/intro_pytorch_for_nlp.ipynb | gpl-3.0 | %matplotlib inline
import torch
"""
Explanation: Introduction to Pytorch for NLP1
This notebook is meant to give a short introduction to Pytorch basics.
You do not have to hand in this tutorial. It is just to help you get started with the projects.
We assume that you have pytorch installed with Python 3. See http://ww... |
brettc/causalinfo | notebooks/wet_grass.ipynb | mit | from causalinfo import *
# You only need this if you want to draw pretty pictures of the Networksa
from nxpd import draw, nxpdParams
nxpdParams['show'] = 'ipynb'
"""
Explanation: Is the Grass Wet?
This is an example used by Pearl in his book 'Causality'. I've used the conditional probability tables from here:
https://... |
intel-analytics/BigDL | python/orca/colab-notebook/quickstart/ncf_dataframe.ipynb | apache-2.0 | # Install jdk8
!apt-get install openjdk-8-jdk-headless -qq > /dev/null
import os
# Set environment variable JAVA_HOME.
os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64"
!update-alternatives --set java /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java
!java -version
"""
Explanation: <a href="https://cola... |
martinandersen/opfsdr | notebooks/demo.ipynb | gpl-3.0 | import json, re
import requests
testcases = {}
clist = []
# Retrieve list of MATPOWER test cases
response = requests.get('https://api.github.com/repos/MATPOWER/matpower/contents/data')
clist += json.loads(response.text)
# Retrieve list of pglib-opf test cases
response = requests.get('https://api.github.com/repos/powe... |
mne-tools/mne-tools.github.io | 0.24/_downloads/82d9c13e00105df6fd0ebed67b862464/ssp_projs_sensitivity_map.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
from mne import read_forward_solution, read_proj, sensitivity_map
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
fname = data_p... |
StudyExchange/Udacity | MachineLearning(Advanced)/p1_boston_housing/boston_housing.ipynb | mit | # Import libraries necessary for this project
# 载入此项目所需要的库
import numpy as np
import pandas as pd
import visuals as vs # Supplementary code
from sklearn.model_selection import ShuffleSplit
from IPython.display import display
# Pretty display for notebooks
# 让结果在notebook中显示
%matplotlib inline
# Load the Boston housing... |
sampathweb/movie-sentiment-analysis | 02-logisitc-regression-intro.ipynb | mit | from __future__ import print_function # Python 2/3 compatibility
from IPython.display import Image
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Objective
Overview of ML Model Build Process
Logistic Regression Introduction
Model Evaluations
End of expla... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session11/Day1/IntroductionToBasicStellarPhotometrySolutions.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
"""
Explanation: Introduction to Basic Stellar Photometry
Measuring Flux in 1D
Version 0.1
In this notebook we will introduce some basic concepts related to measuring the flux of a point source. As this is an introduction, several challenges asso... |
hhain/sdap17 | notebooks/robin_ue1/03_Cross_validation_and_grid_search.ipynb | mit | # imports
import pandas
import matplotlib.pyplot as plt
from timeit import default_timer as timer
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.grid_search import GridSearchCV
"""
Explanation: Aufgabe 3: Cross Validation and Grid Search
We use skl... |
d-k-b/udacity-deep-learning | autoencoder/Simple_Autoencoder.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)
"""
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to compres... |
AutuanLiu/Python | fastai_notes/LinearAlgebra/speech03.ipynb | mit | # 多行结果输出支持
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
"""
Explanation: Background Removal with Robust PCA
视频数据集 BMC | Background Models Challenge
https://www.cs.utexas.edu/~chaoyeh/web_action_data/dataset_list.html
Background Subtraction Website
End of e... |
melissawm/oceanobiopython | exemplos/exemplo_6/Diagrama TS.ipynb | gpl-3.0 | import gsw
"""
Explanation: Diagrama TS
Vamos elaborar um diagrama TS com o auxílio do pacote gsw [https://pypi.python.org/pypi/gsw/3.0.3], que é uma alternativa em python para a toolbox gsw do MATLAB:
End of explanation
"""
import numpy as np
import matplotlib.pyplot as plt
sal = np.linspace(0, 42, 100)
temp = np.... |
google/eng-edu | ml/cc/prework/tensorflow_programming_concepts.ipynb | apache-2.0 | # 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 the L... |
Hyperparticle/deep-learning-foundation | lessons/tensorboard/Anna_KaRNNa_Name_Scoped.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
weikang9009/pysal | notebooks/explore/segregation/aspatial_examples.ipynb | bsd-3-clause | %matplotlib inline
import geopandas as gpd
from pysal.explore import segregation
import pysal.lib
"""
Explanation: PySAL segregation module for aspatial indexes
This is an example notebook of functionalities for aspatial indexes of the segregation module. Firstly, we need to import the packages we need.
End of explan... |
ledeprogram/algorithms | class7/homework/shuyao_xiao_7_assignment.ipynb | gpl-3.0 | import pandas as pd
import pydotplus
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn import datasets, tree, metrics
from sklearn.cross_validation import train_test_split
from pandas.tools.plotting import scatter_matrix
"""
Explanation: We covered a lot of information today and I'd ... |
tritemio/FRETBursts | notebooks/FRETBursts - 8-spot smFRET burst analysis.ipynb | gpl-2.0 | from fretbursts import *
sns = init_notebook()
import lmfit; lmfit.__version__
import phconvert; phconvert.__version__
"""
Explanation: FRETBursts - 8-spot smFRET burst analysis
This notebook is part of a tutorial series for the FRETBursts burst analysis software.
For a step-by-step introduction to FRETBursts usag... |
KECB/learn | machine_learning/数据预处理.ipynb | mit | iris.data
from sklearn.preprocessing import StandardScaler
# 标准化, 返回值为标准化后的数据
iris_standard = StandardScaler().fit_transform(iris.data)
"""
Explanation: 数据预处理
通过特征提取，我们能得到未经处理的特征，这时的特征可能有以下问题：
不属于同一量纲：即特征的规格不一样，不能够放在一起比较。无量纲化可以解决这一问题。
信息冗余：对于某些定量特征，其包含的有效信息为区间划分，例如学习成绩，假若只关心“及格”或不“及格”，那么需要将定量的考分，转换成“1”和“0”表示及格和未及格。... |
RaRe-Technologies/gensim | docs/notebooks/ensemble_lda_with_opinosis.ipynb | lgpl-2.1 | elda_logger = logging.getLogger(EnsembleLda.__module__)
elda_logger.setLevel(logging.INFO)
elda_logger.addHandler(logging.StreamHandler())
def pretty_print_topics():
# note that the words are stemmed so they appear chopped off
for t in elda.print_topics(num_words=7):
print('-', t[1].replace('*',' ').re... |
mne-tools/mne-tools.github.io | stable/_downloads/efd09079125b2bd222e2dd62aaaccfa4/source_space_snr.ipynb | bsd-3-clause | # Author: Padma Sundaram <tottochan@gmail.com>
# Kaisu Lankinen <klankinen@mgh.harvard.edu>
#
# License: BSD-3-Clause
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
import numpy as np
import matplotlib.pyplot as plt
print(__doc__)
data_path = samp... |
tensorflow/text | docs/tutorials/uncertainty_quantification_with_sngp_bert.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
camillescott/ucd-ecs253 | ECS253 - Homework 3.ipynb | cc0-1.0 | %pylab inline
%config InlineBackend.figure_format='retina'
import numpy as np
import networkx as nx
import seaborn as sns
sns.set_style('ticks')
sns.set_context('poster')
np.set_printoptions(precision=4, linewidth=100)
"""
Explanation: This is the common problem set for Homework 3 from the spring quarter Network Theo... |
CAChemE/curso-python-datos | notebooks/051-Pandas-Ejercicios.ipynb | bsd-3-clause | !head ../data/model.txt
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib as mpl
from IPython.display import display
model = pd.read_csv(
"../data/model.txt", delim_whitespace=True, skiprows = 3,
parse_dates = {'Timestamp': [0, 1]}, index_col = 'Time... |
csdms/dakota | examples/hydrotrend-sampling-study.ipynb | mit | from dakotathon import Dakota
"""
Explanation: <img src="http://csdms.colorado.edu/mediawiki/images/CSDMS_high_res_weblogo.jpg">
HydroTrend Study with Sampling
HydroTrend is a numerical model that creates synthetic river discharge and sediment load time series as a function of climate trends and basin morphology.
In t... |
CalPolyPat/phys202-2015-work | assignments/assignment07/AlgorithmsEx02.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
"""
Explanation: Algorithms Exercise 2
Imports
End of explanation
"""
def find_peaks(a):
"""Find the indices of the local maxima in a sequence."""
peaks = []
data = np.array(a)
deriv = np.diff(data)
i... |
psychemedia/parlihacks | notebooks/Quantity Parsing.ipynb | mit | sentences = [
'4 years and 6 months’ imprisonment with a licence extension of 2 years and 6 months',
'No quantities here',
'I measured it as 2 meters and 30 centimeters.',
"four years and six months' imprisonment with a licence extension of 2 years and 6 months",
'it cost £250... bargain...',
'i... |
histogrammar/histogrammar-python | histogrammar/notebooks/histogrammar_tutorial_exercises.ipynb | apache-2.0 | %%capture
# install histogrammar (if not installed yet)
import sys
!"{sys.executable}" -m pip install histogrammar
import histogrammar as hg
import pandas as pd
import numpy as np
import matplotlib
"""
Explanation: Histogrammar exercises
Histogrammar is a Python package that allows you to make histograms from numpy... |
fmfn/BayesianOptimization | examples/advanced-tour.ipynb | mit | from bayes_opt import BayesianOptimization
"""
Explanation: Advanced tour of the Bayesian Optimization package
End of explanation
"""
# Let's start by defining our function, bounds, and instanciating an optimization object.
def black_box_function(x, y):
return -x ** 2 - (y - 1) ** 2 + 1
"""
Explanation: 1. Sugg... |
grfiv/MNIST | svm.scikit/svm_rbf_pca.scikit_random_gridsearch.ipynb | mit | from __future__ import division
import os, time, math, csv
import cPickle as pickle
from operator import itemgetter
from tabulate import tabulate
import matplotlib.pyplot as plt
import numpy as np
import scipy
from print_imgs import print_imgs # my own function to print a grid of square images
from sklearn.utils ... |
trangel/Data-Science | deep_learning_ai/Tensorflow+Tutorial+dropout.ipynb | gpl-3.0 | import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict
%matplotlib inline
np.random.seed(1)
"""
Explanation: TensorFlow Tutorial
Welcome to this w... |
mne-tools/mne-tools.github.io | 0.24/_downloads/f574d1e7527e4460eb09a16f6f836e35/60_maxwell_filtering_sss.ipynb | bsd-3-clause | import os
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import mne
from mne.preprocessing import find_bad_channels_maxwell
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
... |
steven-murray/halomod | devel/robust_hankel_transforms.ipynb | mit | import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline as spline
import warnings
def pfunc_linear(logk, p, lnk, power_pos):
spl = spline(lnk, p, k=3)
result = np.zeros_like(logk)
inner_mask = (lnk.min() <= logk) & (logk <= lnk.max())
result[inner_mask] = spl(logk[inner_mask])
... |
OzgurBagci/pythonlessons | lesson1/jupyter1.ipynb | unlicense | 2 #Integer yani tam sayı
2.0 #Float yani ondalıklı sayı
1.67 #Yine bir float
4 #Yine bir int(Integer)
'Bu bir string'
"Bu da bir string"
True #Boolean
False #Boolean
"""
Explanation: Data Typelar
Aslında Data Type çok geniş bir kavram. datetime mesela, bir data type. Biz data type derken built-in typelardan, te... |
ray-project/ray | doc/source/tune/examples/bohb_example.ipynb | apache-2.0 | # !pip install ray[tune]
!pip install ConfigSpace==0.4.18
!pip install hpbandster==0.7.4
"""
Explanation: Running Tune experiments with BOHB
In this tutorial we introduce BOHB, while running a simple Ray Tune experiment.
Tune’s Search Algorithms integrate with BOHB and, as a result,
allow you to seamlessly scale up a ... |
austinburks/data-science-bowl-2017 | src/data/get_raw_data.ipynb | mit | from urllib import request
import zipfile, io
from pathlib import Path
import os
import re
from pyunpack import Archive
"""
Explanation: Raw Data Download
This scripts downloads all of the stage 1 raw data for the Kaggle Data Science Bowl 2017 (https://www.kaggle.com/c/data-science-bowl-2017)
We are pulling the raw d... |
nikbearbrown/Deep_Learning | NEU/Singh_Palod_DL/Autoencoders/Autoencoder for Text in TensorFlow.ipynb | mit | import os
from random import randint
from collections import Counter
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import numpy as np
import tensorflow as tf
corpus = "the quick brown fox jumped over the lazy dog from the quick tall fox".split()
test_corpus = "the quick brown fox jumped over the lazy dog from the quick tall... |
phoebe-project/phoebe2-docs | 2.2/tutorials/fti.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Finite Time of Integration (fti)
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
%matplo... |
jserenson/Python_Bootcamp | Regular Expressions.ipynb | gpl-3.0 | import re
# List of patterns to search for
patterns = [ 'term1', 'term2' ]
# Text to parse
text = 'This is a string with term1, but it does not have the other term.'
for pattern in patterns:
print 'Searching for "%s" in: \n"%s"' % (pattern, text),
#Check for match
if re.search(pattern, text):
... |
junhwanjang/DataSchool | Lecture/10. 기초 확률론 4 - 상관관계/2) 확률 밀도 함수의 독립.ipynb | mit | np.set_printoptions(precision=4)
pmf1 = np.array([[0, 1, 2, 3, 2, 1],
[0, 2, 4, 6, 4, 2],
[0, 4, 8,12, 8, 4],
[0, 2, 4, 6, 4, 2],
[0, 1, 2, 3, 2, 1]])
pmf1 = pmf1/pmf1.sum()
pmf1
sns.heatmap(pmf1)
plt.xlabel("x")
plt.ylabel("y")
plt.title("Joint Proba... |
Naereen/notebooks | Demo_of_RISE_for_slides_with_Jupyter_notebooks__Python.ipynb | mit | from sys import version
print(version)
"""
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Demo-of-RISE-for-slides-with-Jupyter-notebooks-(Python)" data-toc-modified-id="Demo-of-RISE-for-slides-with-Jupyter-notebooks-(Python)-1"><span class... |
GoogleCloudPlatform/bigquery-notebooks | notebooks/community/analytics-componetized-patterns/retail/propensity-model/bqml/bqml_kfp_retail_propensity_to_purchase.ipynb | apache-2.0 | # 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://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, so... |
zhuanxuhit/deep-learning | dcgan-svhn/DCGAN_Exercises.ipynb | mit | %matplotlib inline
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
!mkdir data
"""
Explanation: Deep Convolutional GANs
In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a De... |
diegocavalca/Studies | dsa-deep-learning-ii/1. Introducao/GridSearch/GridSearch.ipynb | cc0-1.0 | # Forçando o Keras a utilizar a CPU
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# Import dos Módulos
import numpy
from sklearn.model_selection import GridSearchCV
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn i... |
BjornFJohansson/pydna-examples | notebooks/golden_gate/golden_gate1.ipynb | bsd-3-clause | from pydna.all import *
"""
Explanation: Golden gate cloning simulation using pydna
The objective is to assemble three 50 bp sequences into one circular sequence.
We will use the assembly_fragments function and the Assembly class.
End of explanation
"""
frags = parse('''
>1|random sequence|A: 0.25|C: 0.25|G: 0.25|... |
flowersteam/explauto | notebook/full_tutorial.ipynb | gpl-3.0 | from __future__ import print_function
from explauto.environment import environments
environments.keys()
"""
Explanation: Explauto, an open-source Python library to study autonomous exploration in developmental robotics
Explauto is an open-source Python library providing a unified API to design and compare various exp... |
gsnyder206/mock-surveys | mocks_from_publicdata/summer2020/results/Early_pairs.ipynb | mit | from astropy.io import ascii
import photutils ; print("Photutils version:",photutils.__version__)
import numpy as np ; print("Numpy version:",np.__version__)
import matplotlib.pyplot as plt
import seaborn as sns; print("Seaborn version:",sns.__version__)
sns.set()
import tng_api_utils as tau
import os
"""
Explanati... |
jpilgram/phys202-2015-work | assignments/midterm/AlgorithmsEx03.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact
"""
Explanation: Algorithms Exercise 3
Imports
End of explanation
"""
def char_probs(s):
"""Find the probabilities of the unique characters in the string s.
Parameters
----------
... |
ComputationalModeling/spring-2017-danielak | past-semesters/fall_2016/day-by-day/day23-agent-based-modeling-day1/Numpy_2D_array_tutorial.ipynb | agpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import random
"""
Explanation: Numpy 2D arrays - some examples
This notebook demonstrates how to work with 2D numpy arrays, including array slicing, random numbers, and making plots with them. Note that this works with higher-dimensional arrays as ... |
ML4DS/ML4all | R_lab1_ML_Bay_Regresion/Pract_regression_student.ipynb | mit | # Import some libraries that will be necessary for working with data and displaying plots
# To visualize plots in the notebook
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import scipy.io # To read matlab files
from scipy import spatial
imp... |
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