repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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ray-project/ray | doc/source/tune/examples/nevergrad_example.ipynb | apache-2.0 | # !pip install ray[tune]
!pip install nevergrad==0.4.3.post7
"""
Explanation: Running Tune experiments with Nevergrad
In this tutorial we introduce Nevergrad, while running a simple Ray Tune experiment. Tune’s Search Algorithms integrate with Nevergrad and, as a result, allow you to seamlessly scale up a Nevergrad op... |
diegocavalca/Studies | deep-learnining-specialization/2. improving deep neural networks/week2/Optimization methods.ipynb | cc0-1.0 | import numpy as np
import matplotlib.pyplot as plt
import scipy.io
import math
import sklearn
import sklearn.datasets
from opt_utils import load_params_and_grads, initialize_parameters, forward_propagation, backward_propagation
from opt_utils import compute_cost, predict, predict_dec, plot_decision_boundary, load_data... |
AllenDowney/ModSimPy | soln/yoyo_soln.ipynb | mit | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *
"""
Explanation: Modeling and Simulati... |
SHDShim/pytheos | examples/5_calculate_nonstatic_terms_Dorogokupets2007.ipynb | apache-2.0 | %config InlineBackend.figure_format = 'retina'
"""
Explanation: For high dpi displays.
End of explanation
"""
import uncertainties as uct
import numpy as np
import matplotlib.pyplot as plt
from uncertainties import unumpy as unp
import pytheos as eos
v0 = 3.9231**3
v = np.linspace(v0, v0 * 0.8, 20)
"""
Explanation... |
jeffakolb/Data-Science-45min-Intros | long-tail-distributions-001/long-tails-001.ipynb | unlicense | # Plotting library
import matplotlib.pyplot as plt
%matplotlib inline
# mathematics
from math import exp, pi, sqrt, log
from numpy import linspace, hstack
from collections import Counter
from random import sample
from random import uniform
from functools import reduce
# special powerlaw fitting library
import powerla... |
dtamayo/MachineLearning | Day2/TitanicPandas.ipynb | gpl-3.0 | import pandas as pd
df = pd.read_csv('data/train.csv')
df.head()
df.tail()
df.shape
"""
Explanation: Intro to Data Manipulation and Pandas
Dan Tamayo
Material draws from a blog working through the Titanic dataset by Dave Novelli, as well as the Pandas Cookbook by Julia Evans
Follow Along
In a terminal, navigate to ... |
phobson/statsmodels | examples/notebooks/quantile_regression.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import print_function
import patsy
import numpy as np
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
from statsmodels.regression.quantile_regression import QuantReg
data = sm.datasets.engel.load_pandas().data
da... |
FinTechies/HedgingRL | notebooks/Environment.ipynb | mit | import json
from os import path
import pandas as pd
import gym.envs
import numpy as np
num_steps = 100
gym.envs.register(id='obs-v2',
entry_point='gym_bs.envs:EuropeanOptionEnv',
kwargs={'t': num_steps,
'n': 1,
's0': 49,
... |
DistrictDataLabs/yellowbrick | examples/iguk1987/Yellowbrick_Tour.ipynb | apache-2.0 | import numpy as np
import pandas as pd
%matplotlib inline
from cycler import cycler
import matplotlib.style
import matplotlib as mpl
mpl.style.use('seaborn-white')
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
from sklearn.cluster import KMeans
from sklearn.linear_model import RidgeCV
from sklear... |
statkclee/ThinkStats2 | code/chap09soln-kor.ipynb | gpl-3.0 | from __future__ import print_function, division
import first
import hypothesis
import scatter
import thinkstats2
import numpy as np
"""
Explanation: 통계적 사고 (2판) 연습문제 (thinkstats2.com, think-stat.xwmooc.org)<br>
Allen Downey / 이광춘(xwMOOC)
End of explanation
"""
def RunTests(live, iters=1000):
"""Runs the tests ... |
sbailey/knltest | doc/portland-extract.ipynb | bsd-3-clause | import numpy as np
%pylab inline
#- Raw data from runs on Cori Haswell and Cori KNL with
#- two different python distributions (IDP and ContinuumIO Anaconda+MKL)
nspec = np.array([5,10,15,20,25])
rate_hsw_idp = np.array([464.1, 623.0, 595.2, 303.6, 241.1])
rate_hsw_ana = np.array([466.3, 631.4, 599.6, 308.9, 244.1])
r... |
chris1610/pbpython | notebooks/Forecasting-with-prophet.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
from fbprophet import Prophet
import matplotlib.pyplot as plt
"""
Explanation: Notebook from Forecast Website Traffic Using Facebook's Prophet Library
Note: The recommended way to install prophet is to use the following command:
conda install -c conda-forge fbprophet
End of expl... |
johnnyliu27/openmc | examples/jupyter/triso.ipynb | mit | %matplotlib inline
from math import pi
import numpy as np
import matplotlib.pyplot as plt
import openmc
import openmc.model
"""
Explanation: OpenMC includes a few convenience functions for generationing TRISO particle locations and placing them in a lattice. To be clear, this capability is not a stochastic geometry ca... |
lmoresi/UoM-VIEPS-Intro-to-Python | Notebooks/Numpy+Scipy/8 - Numpy, Scipy Miscellany.ipynb | mit | import numpy as np
from scipy.io import netcdf
"""
Explanation: Data storage
Python provides file read write and object serialisation / reconstruction (python pickle module). numpy provides methods for storing and retrieving structured arrays quickly and efficiently (including data compression). scipy provides some he... |
InsightSoftwareConsortium/SimpleITK-Notebooks | Python/68_Registration_Errors.ipynb | apache-2.0 | import SimpleITK as sitk
import numpy as np
import copy
%matplotlib notebook
from gui import PairedPointDataManipulation, display_errors
import matplotlib.pyplot as plt
from registration_utilities import registration_errors
"""
Explanation: Registration Errors, Terminology and Interpretation <a href="https://mybinder... |
EvanBianco/Practical_Programming_for_Geoscientists | Build_and_test_environment.ipynb | apache-2.0 | !python -V
# Should be 3.5
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import ricker
"""
Explanation: Build and test the environment
This document explains how to set up your environment for the geocomputing course.
1. Install Anaconda
First, install Anaconda for Python 3.5, following the in... |
Cyb3rWard0g/ThreatHunter-Playbook | docs/notebooks/windows/05_defense_evasion/WIN-190407183310.ipynb | gpl-3.0 | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: Enable Remote Desktop Conections Registry
Metadata
| | |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2019/04/07 |
| modification date | 2020/09/20 |
| playbook rela... |
fdion/stemgraphic | notebooks/semi_graphic_displays_and_charsets.ipynb | mit | import pandas as pd
from stemgraphic.num import text_heatmap, heatmatrix, text_hist, text_dot, stem_tally, stem_text
from stemgraphic.helpers import available_charsets
"""
Explanation: Semi graphic displays and charsets
Some text or semi graphic displays included in stemgraphic.
imports
End of explanation
"""
df = p... |
yala/introdeeplearning | draft/part1.ipynb | mit | trainSet = p.load( open('data/train.p','rb'))
devSet = p.load( open('data/dev.p','rb'))
testSet = p.load( open('data/test.p','rb'))
## Let's look at the size of what we have here. Note, we could use a much larger train set,
## but we keep it mid-size so you can run this whole thing off your laptop
len(trainSet), len(... |
HaoMood/cs231n | assignment1/assignment1sol/2nnet/main.ipynb | gpl-3.0 | # Run some setup code
import numpy as np
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'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'ne... |
jinzishuai/learn2deeplearn | deeplearning.ai/C4.CNN/week4_SpecialApps/hw/Face Recognition/Face Recognition for the Happy House - v2.ipynb | gpl-3.0 | from keras.models import Sequential
from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate
from keras.models import Model
from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import MaxPooling2D, AveragePooling2D
from keras.layers.merge import Concatenate
from kera... |
d00d/quantNotebooks | Notebooks/quantopian_research_public/notebooks/lectures/Introduction_to_Futures/notebook.ipynb | unlicense | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Introduction to Futures Contracts
by Maxwell Margenot and Delaney Mackenzie
Part of the Quantopian Lecture Series:
www.quantopian.com/lectures
github.com/quantopian/research_public
Notebook released under the Creative Commons Att... |
ToqueWillot/M2DAC | FDMS/TME3/Kaggle_rain-Copy1.ipynb | gpl-2.0 | # from __future__ import exam_success
from __future__ import absolute_import
from __future__ import print_function
%matplotlib inline
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import random
import pandas as pd
# Sk cheats
from sklearn.cross_validation import cross_val_sco... |
kkhenriquez/python-for-data-science | Week-4-Pandas/.ipynb_checkpoints/Introduction to Pandas-checkpoint.ipynb | mit | import pandas as pd
"""
Explanation: <p style="font-family: Arial; font-size:3.75em;color:purple; font-style:bold"><br>
Pandas</p>
<br>
pandas is a Python library for data analysis. It offers a number of data exploration, cleaning and transformation operations that are critical in working with data in Python.
pandas ... |
AllenDowney/ThinkStats2 | workshop/hypothesis.ipynb | gpl-3.0 | %matplotlib inline
import numpy
import scipy.stats
import matplotlib.pyplot as plt
import first
"""
Explanation: Hypothesis Testing
Copyright 2016 Allen Downey
License: Creative Commons Attribution 4.0 International
End of explanation
"""
live, firsts, others = first.MakeFrames()
"""
Explanation: Part One
Suppos... |
ameliecordier/iutdoua-info_algo2015 | 2015-10-08 - TD7 - Les algos mystères, le monde à l'envers .ipynb | cc0-1.0 | a, b, c = 1, 1, 1
print(1)
while c<15:
a, b, c = b, a+b, c+1
print(b)
"""
Explanation: Exercice 1 : Que font les programmes suivants ?
End of explanation
"""
a, b, c = 1, 1, 1
pri... |
charmasaur/digbeta | tour/traj_feature.ipynb | gpl-3.0 | %matplotlib inline
import os
import re
import math
import random
import pickle
import pandas as pd
import numpy as np
import scipy.stats
#from numba import jit
from datetime import datetime
from joblib import Parallel, delayed
import matplotlib.pyplot as plt
nfeatures = 8 # number of features
EPS = 1e-12 # smooth, de... |
rsignell-usgs/notebook | OOI/from_ooi_json-pwrsys-deployment.ipynb | mit | %matplotlib inline
import json
import pandas as pd
import numpy as np
import glob
from pyaxiom.netcdf.sensors import TimeSeries
path = '/sand/usgs/users/rsignell/data/ooi/endurance/cg_proc/ce02shsm/D00004/buoy/pwrsys/*.pwrsys.json'
odir = '/usgs/data2/notebook/data/nc'
ofile = 'ce02shsm_pwrsys_D00004.nc'
def json2df... |
Automating-GIS-processes/2017 | source/codes/Lesson5-interactive-map-Bokeh-advanced-plotting-published.ipynb | mit | from bokeh.palettes import YlOrRd as palette #Spectral6 as palette
from bokeh.plotting import figure, save
from bokeh.models import ColumnDataSource, HoverTool, LogColorMapper
from bokeh.palettes import RdYlGn10 as palette
import geopandas as gpd
import pysal as ps
import numpy as np
# Filepaths
fp = r"/home/geo/dat... |
kubeflow/examples | kserve/sdk/first_isvc_kserve.ipynb | apache-2.0 | !pip install kserve==0.7.0
"""
Explanation: Run your first InferenceService
The InferenceService custom resource is the primary interface that is used for deploying models on KServe. Inside an InferenceService, users can specify multiple
components that are used for handling inference requests. These components are th... |
jeanbaptistepriez/predicsis-ai-faq-tuto | 1.how_to_retrieve_discretization_attributes_using_PredicSis.ai_python_SDK/How to retrieve classes, using PredicSis.ai python SDK.ipynb | gpl-3.0 | # Load PredicSis.ai SDK
from predicsis import PredicSis
import predicsis.config as config, os, sys
os.environ['PREDICSIS_URL'] = 'your_instance'
if sys.version_info[0] >= 3:
from importlib import reload
reload(config)
"""
Explanation: Goal
Retrieve the discretization attributes available in PredicSis.ai GUI usin... |
cbuntain/TutorialSocialMediaCrisis | notebooks/T04-08 - Twitter Analytics.ipynb | apache-2.0 | crisisInfo = {
"boston": {
"name": "Boston Marathon Bombing",
"time": 1366051740, # Timestamp in seconds since 1/1/1970, UTC
# 15 April 2013, 14:49 EDT -> 18:49 UTC
"directory": "boston",
"keywords": ["boston", "exploision", "bomb", "marathon"],
"b... |
marioberges/F16-12-752 | homeworks/homework1/assignment1_solution.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import sys
print('Python version:')
print(sys.version)
print('Numpy version:')
print(np.__version__)
import sklearn
print('Sklearn version:')
print(sklearn.__version__)
"""
Explanation: 12-752: Data-Driven Building Energy Management
Fall 2016, Car... |
wikistat/Apprentissage | Adult-Census/Apprent-Python-AdultCensus.ipynb | gpl-3.0 | %matplotlib inline
# Importations
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
adult=pd.read_csv('adultTrainTest.csv')
adult.head()
"""
Explanation: <center>
<a href="http://www.insa-toulouse.fr/" ><img src="http://www.math.univ-toulouse.fr/~besse/Wikistat/Images/logo-insa.jpg" style="float:... |
eds-uga/csci1360e-su17 | assignments/A4/A4_Q2.ipynb | mit | import numpy as np
np.random.seed(578435)
x11 = np.random.random(10)
x12 = np.random.random(10)
d1 = np.array([ 0.24542374, 0.19098998, 0.20645088, 0.49097139, -0.56594091,
-0.13363814, 0.46859546, -0.32476466, -0.35938731, 0.17459786])
np.testing.assert_allclose(d1, difference(x11, x12))
np.random.seed(8... |
morphean/deep-learning | linear-regression/Linear-Regression.ipynb | apache-2.0 | import pandas as pd
from sklearn import linear_model
import matplotlib.pyplot as plt
"""
Explanation: Linear Regression
This is one of the simplest models to use, since it has uses linearly correlated data to 'predict' a value given
End of explanation
"""
df = pd.read_fwf('brain_body.txt')
x_values = df[['Brain']]
y... |
pgmpy/pgmpy_notebook | notebooks/1. Introduction to Probabilistic Graphical Models.ipynb | mit | from IPython.display import Image
"""
Explanation: Introduction to Probabilitic Graphical Models
End of explanation
"""
%run ../scripts/1/discretize.py
data
"""
Explanation: Contents
What is machine learning
Different ways of learning from data
Why probabilistic graphical models
Major types of PGMs
1. What is mac... |
smharper/openmc | examples/jupyter/candu.ipynb | mit | %matplotlib inline
from math import pi, sin, cos
import numpy as np
import openmc
"""
Explanation: In this example, we will create a typical CANDU bundle with rings of fuel pins. At present, OpenMC does not have a specialized lattice for this type of fuel arrangement, so we must resort to manual creation of the array ... |
arnoldlu/lisa | ipynb/tutorial/00_LisaInANutshell.ipynb | apache-2.0 | import logging
from conf import LisaLogging
LisaLogging.setup()
# Execute this cell to enable verbose SSH commands
logging.getLogger('ssh').setLevel(logging.DEBUG)
# Other python modules required by this notebook
import json
import os
"""
Explanation: Linux Interactive System Analysis DEMO
Get LISA and start the Not... |
GoogleCloudPlatform/python-docs-samples | people-and-planet-ai/image-classification/README.ipynb | apache-2.0 | # We need libffi-dev to launch the Dataflow pipeline.
!apt-get -qq install libffi-dev
# Clone the python-docs-samples respository.
!git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git
# Navigate to the sample code directory.
%cd python-docs-samples/people-and-planet-ai/image-classification
# Ins... |
lukas/ml-class | examples/notebooks/Lesson-4-Evaluating-Classifiers.ipynb | gpl-2.0 | import pandas as pd
import numpy as np
df = pd.read_csv('../scikit/tweets.csv')
text = df['tweet_text']
target = df['is_there_an_emotion_directed_at_a_brand_or_product']
# Remove the blank rows:
fixed_target = target[pd.notnull(text)]
fixed_text = text[pd.notnull(text)]
# Perform feature extraction:
from sklearn.fe... |
ES-DOC/esdoc-jupyterhub | notebooks/awi/cmip6/models/awi-cm-1-0-hr/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'awi', 'awi-cm-1-0-hr', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: AWI
Source ID: AWI-CM-1-0-HR
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy... |
intel-analytics/BigDL | python/orca/colab-notebook/quickstart/pytorch_lenet_mnist_data_creator_func.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed un... |
ajrichards/bayesian-examples | data-ingestion/db-stuff/sqlalchemy-stuff/sqlalchemy.ipynb | bsd-3-clause | import os,sys,getpass,datetime
from sqlalchemy import Column, ForeignKey, Integer, String, Float, DateTime
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
Base = declarative_base()
class Membe... |
InterImm/marsCalendar | py/.ipynb_checkpoints/calendar-checkpoint.ipynb | bsd-2-clause | import math
import datetime ## to deal with dates
from IPython.display import Image
"""
Explanation: 这是一个用来说明火星历法和地球历法换算方法的文档。
火星历法的说明：火星历法@星际移民之书
火星和地球之间，一个非常最要的联系是地球上 UTC 时间的 2000 年 01 月 06 日，这一天的午夜正好是火星上零度经线的午夜。因此，一个比较好的算法是，以这一天为基准。
最终要达成的目的是，给定一个地球上的 UTC 日期和时间，我们能够换算到火星的零度经线的日期和时间。
Prep
End of explanation
"""
# ... |
eecs445-f16/umich-eecs445-f16 | handsOn_lecture11_info-theory-decision-trees/handsOn11.ipynb | mit | from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression, Perceptron
import numpy as np
import matplotlib.pyplot as plt
from mlxtend.evaluate import plot_decision_regions
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
"""
Explanation: EECS 445: Machine Le... |
mromanello/SunoikisisDC_NER | Sunoikisis - Named Entity Extraction 1b.ipynb | gpl-3.0 | ########
# NLTK #
########
import nltk
from nltk.tag import StanfordNERTagger
########
# CLTK #
########
import cltk
from cltk.tag.ner import tag_ner
##############
# MyCapytain #
##############
import MyCapytain
from MyCapytain.resolvers.cts.api import HttpCTSResolver
from MyCapytain.retrievers.cts5 import CTS
from M... |
wittawatj/fsic-test | ipynb/demo_nfsic.ipynb | mit | %load_ext autoreload
%autoreload 2
%matplotlib inline
import fsic.util as util
import fsic.data as data
import fsic.kernel as kernel
import fsic.indtest as it
import fsic.glo as glo
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import scipy
import theano
"""
Explanation: Introduction ... |
tensorflow/docs-l10n | site/ja/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... |
relopezbriega/mi-python-blog | content/notebooks/ComplexPy.ipynb | gpl-2.0 | # Creando un número complejo
c1 = 4 + 3j
c1
# Verificando el tipo de dato
type(c1)
# Creando un número complejo con complex
c2 = complex(2, -3)
c2
# sumando dos números complejos
c1 = 2 + 2j
c2 = 1 + 5j
c1 + c2
# Restando dos números complejos
c1 - c2
# Producto escalar
c1 = 2 + 3j
3 * c1
# Multiplicando dos núme... |
fastai/course-v3 | nbs/dl2/pascal.ipynb | apache-2.0 | path = untar_data(URLs.PASCAL_2007)
"""
Explanation: Data
We are going to use the Pascal dataset for object detection. There is a version from 2007 and a bigger version from 2012. We'll use the 2007 version here.
End of explanation
"""
import json
annots = json.load(open(path/'train.json'))
annots.keys()
annots['a... |
tzdwi/adapt | workflow.ipynb | mit | #Now, let's import some useful libraries
import numpy as np
from matplotlib import pyplot as plt
from adapt import *
from phot_tools import *
from glob import glob
import os
from astropy.io import fits
from astropy.coordinates import SkyCoord
from astropy.table import vstack, Table
%matplotlib inline
#What do all of t... |
NeuPhysics/aNN | ipynb/HomogeneousModel.ipynb | mit | # This line configures matplotlib to show figures embedded in the notebook,
# instead of opening a new window for each figure. More about that later.
# If you are using an old version of IPython, try using '%pylab inline' instead.
%matplotlib inline
%load_ext snakeviz
import numpy as np
from scipy.optimize import mi... |
tlapicka/IPythonNotebooks | Harmonicka_analyza--trojuhelnik.ipynb | gpl-2.0 | t=linspace(0,1,1000)
u1=8./pi/pi*cos(2*pi*1*t-pi/2)
u3=8./3/3/pi/pi*cos(2*pi*3*t+pi/2)
u5=8./5/5/pi/pi*cos(2*pi*5*t-pi/2)
u7=8./7/7/pi/pi*cos(2*pi*7*t+pi/2)
"""
Explanation: Součet harmonických složek
Mějme následující časový průběh:
$$ u(t)=\frac{8}{\pi^2} \cos(\omega t - {\pi\over 2}) + \frac{8}{(3\pi)^2} \cos(3\om... |
chemiskyy/simmit | Examples/MMSP/micromechanics_examples/micro_examples1.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
from numpy.linalg import inv
import pandas as pd
import matplotlib.pyplot as plt
from simmit import smartplus as sim
from simmit import identify as iden
import os
import itertools
dir = os.path.dirname(os.path.realpath('__file__'))
"""
Explanation: Mechanical properties of compo... |
cgpotts/cs224u | iit_equality.ipynb | apache-2.0 | __author__ = "Atticus Geiger"
__version__ = "CS224u, Stanford, Spring 2022"
"""
Explanation: Interchange Intervention Training: Equality learning tasks
End of explanation
"""
import torch
import random
import copy
import itertools
import numpy as np
from sklearn.metrics import classification_report
from torch_deep_n... |
psumank/DATA643 | Final/DATA643_pySpark_Final_Project.ipynb | mit | import os
import sys
import urllib2
import collections
import matplotlib.pyplot as plt
import math
from time import time, sleep
%pylab inline
"""
Explanation: DATA 643 - Final Project
Sreejaya Nair and Suman K Polavarapu
Description:
Explore the Apache Spark Cluster Computing Framework by analysing the movielens datas... |
CivicKnowledge/metatab-packages | ipums.org/ipums.org-income_homevalue/notebooks/executed-source.ipynb | mit | %matplotlib inline
%load_ext metatab
%load_ext autoreload
%autoreload 2
%mt_lib_dir lib
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import metatab as mt
import seaborn as sns; sns.set(color_codes=True)
import sqlite3
import statsmodels as sm
from statsmodels.nonpara... |
llulai/deep-learning | image-classification/dlnd_image_classification.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10... |
kimkipyo/dss_git_kkp | 통계, 머신러닝 복습/160704월_24일차_모형 최적화 Model optimization/2.모형 최적화 분산 처리.ipynb | mit | from ipyparallel import Client
c = Client()
c.ids
dview = c[:]
dview
"""
Explanation: 모형 최적화 분산 처리
ipyparallel
http://ipyparallel.readthedocs.org/en/latest/index.html
Engine <-> Client
Engine: 실제 계산이 실행되는 프로세스
Client: 엔진을 제어하기 위한 인터페이스
$ conda install ipyparallel
Engine 가동/중지
가동
$ ipcluster start -n 4
중지
... |
ES-DOC/esdoc-jupyterhub | notebooks/csiro-bom/cmip6/models/access-1-0/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csiro-bom', 'access-1-0', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: CSIRO-BOM
Source ID: ACCESS-1-0
Topic: Aerosol
Sub-Topics: Transport, Emis... |
dcavar/python-tutorial-for-ipython | notebooks/Python for Text Similarities.ipynb | apache-2.0 | text1 = """Our medicine cures baldness. No diagnostics needed.
We guarantee Fast Viagra delivery.
We can provide Human growth hormone. The cheapest Life
Insurance with us. You can Lose weight with this treatment.
Our Medicine now and No medical exams necessary.
Our... |
kadircet/CENG | 783/HW2/task1_denoising_autoencoder.ipynb | gpl-3.0 | # A bit of setup
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# for auto-reloading external modules
# see http://stackoverflow.com/ques... |
eflegara/NetStruc | 5. Viral marketing on social media.ipynb | mit | import networkx as nx #import NetworkX
import numpy as np #import numpy for ...
#force drawing of graphs inline for ipython notebook
%matplotlib inline
import matplotlib.pyplot as plt #import matplotlib for plotting/drawing grpahs
import matplotlib.patches as mpatches #for legends in the graph
from __future__ import u... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/structured/labs/3c_bqml_dnn_babyweight.ipynb | apache-2.0 | %%bash
sudo pip freeze | grep google-cloud-bigquery==1.6.1 || \
sudo pip install google-cloud-bigquery==1.6.1
"""
Explanation: LAB 3c: BigQuery ML Model Deep Neural Network.
Learning Objectives
Create and evaluate DNN model with BigQuery ML
Create and evaluate DNN model with feature engineering with ML.TRANSFORM.
Ca... |
VadimMalykh/courses | deeplearning1/my/lessons/lesson1_my.ipynb | apache-2.0 | %matplotlib inline
"""
Explanation: Using Convolutional Neural Networks
Welcome to the first week of the first deep learning certificate! We're going to use convolutional neural networks (CNNs) to allow our computer to see - something that is only possible thanks to deep learning.
Introduction to this week's task: 'Do... |
ewulczyn/talk_page_abuse | src/retention/Harassment and Newcomer Retention Data Munging.ipynb | apache-2.0 | df_all_users = pd.read_csv("../../data/retention/user_start.tsv", "\t")
print(df_all_users.shape)
print(len(df_all_users.user_id.unique()))
# load data for resolving user pagetitles to user_ids
# only works for ns=user
df_i2ns = pd.read_csv("../../data/retention/user_id_to_names.tsv", "\t")
print(df_i2ns.shape)
# can... |
michaelgat/Udacity_DL | intro-to-tensorflow/intro_to_tensorflow_MG.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... |
JackWalpole/splitwavepy | devel/Splitting_for_novices.ipynb | mit | import sys
sys.path.append("..")
import splitwavepy as sw
import matplotlib.pyplot as plt
import numpy as np
data = sw.Pair(noise=0.05,pol=40,delta=0.1)
data.plot()
"""
Explanation: Shear Wave Splitting for the Novice
When a shear wave encounters an anisotropic medium, it splits its energy into orthogonally polaris... |
yw-fang/readingnotes | machine-learning/McKinney-pythonbook2013/chapter05-note.ipynb | apache-2.0 | from pandas import Series, DataFrame
import pandas as pd
"""
Explanation: 阅读笔记
作者：方跃文
Email: fyuewen@gmail.com
时间：始于2017年9月12日， 结束写作于
第五章笔记始于2018年1月6日，结束于2018年July 20th
这一章节虽然跨度很长，不过在1月到7月之间，pandas估计已经被上百次地应用到了我地实际研究中用于数据处理.
The note on this chapter was finally finished today, I was quite happy because it has ... |
gboeing/pynamical | examples/pynamical-demo-cobweb-plots.ipynb | mit | import glob
import IPython.display as IPdisplay
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pynamical import cobweb_plot
from PIL import Image
%matplotlib inline
"""
Explanation: Pynamical: cobweb plots of the logistic map
Citation info: Boeing, G. 2016. "Visual Analysis of Nonlinear Dy... |
opengeostat/pygslib | pygslib/Ipython_templates/declus_raw.ipynb | mit | #general imports
import matplotlib.pyplot as plt
import pygslib
import numpy as np
#make the plots inline
%matplotlib inline
"""
Explanation: PyGSLIB
Declustering
This is how declustering works
End of explanation
"""
#get the data in gslib format into a pandas Dataframe
mydata= pygslib.gslib.read_gslib_file(... |
GLiCom/CorpusAnalysis2016 | notebooks/Introduction.ipynb | apache-2.0 | import nltk
nltk.download('book')
"""
Explanation: Pequeña introducción a NLTK o cómo analizar un corpus propio
Primer paso: preparar utilización de NLTK (detalles sin importancia). Con import se definen lo módulos que se quieren usar.
nltk.download('book') nos permite descargar varios recursos lingüísticos y corpus (... |
balarsen/pymc_learning | Learning/SABRS_demo_stats1.ipynb | bsd-3-clause | !date
# Standard Library Python Modules
# Common Python Modules
import matplotlib.pyplot as plt
import numpy as np
import spacepy.plot as spp
import spacepy.toolbox as tb
import pandas as pd
import pymc # this is the MCMC tool
# put plots into this document
%matplotlib inline
"""
Explanation: Brian Larsen - 28 Apri... |
alasdairtran/mclearn | projects/peerjcs16/active_learning_suggestions.ipynb | bsd-3-clause | %load_ext autoreload
%autoreload 2
import pandas as pd
import os
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from time import time
from mclearn.experiment import ActiveExperiment, load_results, save_results
from mclearn.tools import log
from sklearn.externals import joblib
from matplotlib.... |
AtmaMani/pyChakras | python_crash_course/python_cheat_sheet_2.ipynb | mit | def func_add_numbers(num1, num2=10):
return (num1 + num2)
func_add_numbers(2)
func_add_numbers(2,34)
func_add_numbers()
"""
Explanation: Python cheat sheet - iterations
Table of contents
- Functions
- Classes
- Exception handling
Functions
Specify optional parameters in the end. Specify the default values fo... |
ericmjl/influenza-global-reassortment | Revisions - Lineage Traces.ipynb | mit | import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import json
from collections import defaultdict
from datetime import datetime, date
from random import randint
from networkx.readwrite.json_graph import node_link_data
%matplotlib inline
G = nx.read_gpickle('20150902_all_ird... |
evanmiltenburg/python-for-text-analysis | Chapters-colab/Chapter_08_Comparison_of_lists_and_sets.ipynb | apache-2.0 | %%capture
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Data.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/images.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Extra_Material.zip
!unzip Data.zip -d ../
!unzip images.zip -d ./
!unzip Ext... |
tensorflow/lattice | docs/tutorials/shape_constraints.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... |
pauliacomi/pyGAPS | docs/examples/quickstart.ipynb | mit | import pygaps as pg
"""
Explanation: Quickstart
This document should get you started with pyGAPS, providing several common
operations like creating/reading isotherms, running characterisation routines,
model fitting and data export/plotting.
<div class="alert alert-info">
You can download this code as a Jupyter Notebo... |
JAmarel/Phys202 | Matplotlib/MatplotlibEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 1
Imports
End of explanation
"""
import os
assert os.path.isfile('yearssn.dat')
"""
Explanation: Line plot of sunspot data
Download the .txt data for the "Yearly mean total sunspot number [1700 - now]" from th... |
eflautt/ga-data-science | AdmissionsProject/Part3/starter-code/Flautt-project3-submission.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import statsmodels.api as sm
import pylab as pl
import numpy as np
df_raw = pd.read_csv("../assets/admissions.csv")
df = df_raw.dropna()
print df_raw.count() # showing 'old' df total count
print df.count() # showing 'new' df total count
print df.... |
jonwright/ImageD11 | docs/NotesOnNewDeformationGradientTensor.ipynb | gpl-2.0 | %matplotlib inline
import matplotlib.pyplot as pl, numpy as np
"""
Explanation: Finite strains
A quick notebook to try to reduce the problem to looking at some simple pictures. There seems to be a lot more than one way to define the strain tensor components. We use a 2D example for now so it is easier to draw pictur... |
balarsen/pymc_learning | Jeffreys prior/Jeffreys prior examples.ipynb | bsd-3-clause | import pymc3 as pm
import theano
import numpy as np
import scipy.stats as ss
import seaborn as sns
sns.set(font_scale=1.5)
"""
Explanation: This notebook is meant to capture experience with Jeffery's Priors
https://en.wikipedia.org/wiki/Jeffreys_prior
End of explanation
"""
data = np.random.normal(10, 2, size=20)
s... |
dormouse/laketai | .ipynb_checkpoints/pyqt5_qsetting_note-checkpoint.ipynb | gpl-2.0 | from PyQt5.QtCore import QSettings
"""
Explanation: PyQt5 QSettings Note
保存应用窗口大小和位置的例子
```
def read_settings(self):
settings = QSettings("Dormouse", "LakeTai")
pos = settings.value("pos", QPoint(200, 200))
size = settings.value("size", QSize(400, 400))
self.resize(size)
sel... |
Jackporter415/phys202-2015-work | assignments/assignment06/InteractEx05.ipynb | mit | from matplotlib import pyplot as plt
import numpy as np
from IPython.display import SVG
from IPython.display import display
from IPython.html.widgets import interactive, fixed
"""
Explanation: Interact Exercise 5
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.
End ... |
Vvkmnn/books | AutomateTheBoringStuffWithPython/lesson20.ipynb | gpl-3.0 | spam = 'Hello World!'
print(spam)
print(spam.upper())
print(spam.lower())
print(spam) # variable is unmodified
"""
Explanation: Lesson 20:
String Methods
String methods, unlike list methods, return new strings, not changed strings. This is because strings are immutable.
.lower() and .upper() are methods that return ... |
AllenDowney/ThinkBayes2 | notebooks/chap07.ipynb | mit | # If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename... |
AbhilashReddyM/GeometricMultigrid | .ipynb_checkpoints/Making_a_Preconditioner-checkpoint.ipynb | mit | import numpy as np
"""
Explanation: The Making of a Preconditioner
This is a demonstration of a multigrid preconditioned krylov solver in python3. The code and more examples are present on github here. The problem solved is a Poisson equation on a rectangular domain with homogenous dirichlet boundary conditions. Fini... |
csc-training/python-introduction | notebooks/answers/7 - NumPy.ipynb | mit | a = np.arange(-2, 2, 0.2)
print(a)
"""
Explanation: Exercises
Simple array manipulation
Investigate the behavior of the statements below by looking
at the values of the arrays a and b after assignments:
a = np.arange(5)
b = a
b[2] = -1
b = a[:]
b[1] = -1
b = a.copy()
b[0] = -1
Generate a 1D NumPy array containing numb... |
jegibbs/phys202-2015-work | assignments/assignment03/NumpyEx04.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
"""
Explanation: Numpy Exercise 4
Imports
End of explanation
"""
import networkx as nx
K_5=nx.complete_graph(5)
nx.draw(K_5)
"""
Explanation: Complete graph Laplacian
In discrete mathematics a Graph is a set of vertices or n... |
dolittle007/dolittle007.github.io | notebooks/GLM-hierarchical-advi-minibatch.ipynb | gpl-3.0 | %matplotlib inline
import theano
theano.config.floatX = 'float64'
import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm
import pandas as pd
data = pd.read_csv(pm.get_data('radon.csv'))
county_names = data.county.unique()
county_idx = data['county_code'].values
n_counties = len(data.county.unique())
... |
HrantDavtyan/Data_Scraping | Week 6/Scrapy_3.ipynb | apache-2.0 | # -*- coding: utf-8 -*-
import scrapy
class QuoteSpider(scrapy.Spider):
name = "quote"
allowed_domains = ["quotes.toscrape.com"]
start_urls = ['http://quotes.toscrape.com/page/1/',
'http://quotes.toscrape.com/page/2/']
def parse(self, response):
for quote in response.css('di... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/sandbox-1/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'sandbox-1', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: NCC
Source ID: SANDBOX-1
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties: 6... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session05/Day2/GalaxyPhotometry.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
%matplotlib notebook
"""
Explanation: Galaxy photometry
Version 0.1
Today our goal is to compute the 1D radial profile of a simulated galaxy. While many software packages exist to do this, we will build this same functionality from scratch... |
csiu/100daysofcode | lol/acquiring-data.ipynb | mit | website_base_stats = "http://leagueoflegends.wikia.com/wiki/Base_champion_statistics"
# Save HTML to soup
html_data = requests.get(website_base_stats).text
soup = BeautifulSoup(html_data, "html5lib")
# Parse table
table = soup.find('table', attrs={'class' : 'wikitable'})
# Parse table header
lol_thead = [h.text.stri... |
AllenDowney/ThinkStats2 | solutions/chap10soln.ipynb | gpl-3.0 | from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve(url, filename)
print("Downloaded " + local)
download("https://github.com/AllenDowney/ThinkStats2/raw/master/code/th... |
azogue/esiosdata | notebooks/esiosdata - Facturación.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
from esiosdata import FacturaElec
from esiosdata.facturapvpc import (TIPO_PEAJE_GEN, TIPO_PEAJE_NOC, TIPO_PEAJE_VHC,
ZONA_IMPUESTOS_PENIN_BALEARES, ZONA_IMPUESTOS_CANARIAS)
from esiosdat... |
ajaybhat/DLND | Project 2/dlnd_image_classification.ipynb | apache-2.0 | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
import sklearn
import numpy as np
import time
import pylab as pl
from IPython import display
cifar10_dataset... |
Thomas-Schatz/vowel-discri | vowel_discri/Generating acoustic predictors for effect sizes.ipynb | mit | # Requirements:
# - sox + soundfile + our vowel_discri package
# - ABXpy.distances (could be made independent from ABXpy)
import soundfile
import os
import os.path as path
import pandas as pd
import numpy as np
import seaborn
from ast import literal_eval as make_tuple
import wave
import subprocess
import shutil
#... |
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