repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
kylemede/DS-ML-sandbox | notebooks/.ipynb_checkpoints/CS_and_Python-checkpoint.ipynb | gpl-3.0 | for _ in xrange(10):
print "Do something"
"""
Explanation: xrange vs range looping
For long for loops with no need to track iteration use:
End of explanation
"""
for i in range(1,10):
vars()['x'+str(i)] = i
"""
Explanation: This will loop through 10 times, but the iteration variable won't be unused as it wa... |
QInfer/qinfer-examples | custom_distributions.ipynb | agpl-3.0 | from __future__ import division
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
try: plt.style.use('ggplot')
except: pass
"""
Explanation: Making Custom Distributions
Introduction
By using the InterpolatedUnivariateDistribution class, you can easily create a single-variable distribution by speci... |
w4zir/ml17s | lectures/.ipynb_checkpoints/lec04-multinomial-regression-checkpoint.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt
# read data in pandas frame
dataframe = pd.read_csv('datasets/example1.csv', encoding='utf-8')
# assign x and y
X = np.array(dataframe[['x']])
y = np.array(dataframe[['y']])
m = y.size # number ... |
mbeyeler/opencv-machine-learning | notebooks/10.02-Combining-Decision-Trees-Into-a-Random-Forest.ipynb | mit | from sklearn.datasets import make_moons
X, y = make_moons(n_samples=100, noise=0.25, random_state=100)
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
plt.figure(figsize=(10, 6))
plt.scatter(X[:, 0], X[:, 1], s=100, c=y)
plt.xlabel('feature 1')
plt.ylabel('feature 2');
"""
Explanation: <!-... |
charlesreid1/empirical-model-building | ipython/Factorial - Two-Level Three-Factor Design.ipynb | mit | import pandas as pd
import numpy as np
from numpy.random import rand
"""
Explanation: A Two-Level, Three-Factor Full Factorial Design
<br />
<br />
<br />
Table of Contents
Introduction
Factorial Experimental Design:
Two-Level Three-Factor Full Factorial Design
Design of the Experiment
Inputs and Responses
Effects an... |
zzsza/Datascience_School | 30. 딥러닝/03. 신경망 성능 개선.ipynb | mit | sigmoid = lambda x: 1/(1+np.exp(-x))
sigmoid_prime = lambda x: sigmoid(x)*(1-sigmoid(x))
xx = np.linspace(-10, 10, 1000)
plt.plot(xx, sigmoid(xx));
plt.plot(xx, sigmoid_prime(xx));
"""
Explanation: 신경망 성능 개선
신경망의 예측 성능 및 수렴 성능을 개선하기 위해서는 다음과 같은 추가적인 고려를 해야 한다.
오차(목적) 함수 개선: cross-entropy cost function
정규화: regulariza... |
bspalding/research_public | lectures/drafts/Multiple linear regression.ipynb | apache-2.0 | # Import the libraries we'll be using
import numpy as np
import statsmodels.api as sm
# If the observations are in a dataframe, you can use statsmodels.formulas.api to do the regression instead
from statsmodels import regression
import matplotlib.pyplot as plt
# Construct and plot series
X1 = np.arange(100)
X2 = np.ar... |
liganega/Gongsu-DataSci | previous/notes2017/W10/GongSu23_Statistics_Correlation.ipynb | gpl-3.0 | from GongSu22_Statistics_Population_Variance import *
"""
Explanation: 자료 안내: 여기서 다루는 내용은 아래 사이트의 내용을 참고하여 생성되었음.
https://github.com/rouseguy/intro2stats
상관분석
안내사항
지난 시간에 다룬 21장과 22장 내용을 활용하고자 한다.
따라서 아래와 같이 21장과 22장 내용을 모듈로 담고 있는 파이썬 파일을 임포트 해야 한다.
주의: 아래 두 개의 파일이 동일한 디렉토리에 위치해야 한다.
* GongSu21_Statistics_Averages.py ... |
lin99/NLPTM-2016 | 4.Docs/assign1.ipynb | mit | # import word2vec model from gensim
from gensim.models.word2vec import Word2Vec
# load pre-trained model
model = Word2Vec.load_word2vec_format('eswikinews.bin', binary=True)
"""
Explanation: NLP and TM Módulo 4
Taller 1: word2vec
Nombres:
Obtenga el archivo del modelo word2vec entrenado con WikiNews en Español: eswiki... |
flaviobarros/spyre | tutorial/pydata2015_seattle/pydata2015_seattle.ipynb | mit | from spyre import server
class SimpleApp(server.App):
title = "Simple App"
app = SimpleApp()
app.launch() # launching from ipython notebook is not recommended
"""
Explanation: twitter: @adamhajari
github: github.com/adamhajari/spyre
this notebook: http://bit.ly/pydata2015_spyre
Before we start
make sure you ha... |
igabr/Metis_Projects_Chicago_2017 | 03-Project-McNulty/feature_reduction_35.ipynb | mit | df = unpickle_object("dummied_dataset.pkl")
df.shape
#this logic will be important for flask data entry.
float_columns = df.select_dtypes(include=['float64']).columns
for col in float_columns:
if "mths" not in col:
df[col].fillna(df[col].median(), inplace=True)
else:
if col == "inq_last_6mth... |
jbwhit/jupyter-tips-and-tricks | notebooks/08-old.ipynb | mit | df2 = df[df['Mine_State'] != "Wyoming"].groupby('Mine_State').sum()
df3 = df.groupby('Mine_State').sum()
# have to run this from the home dir of this repo
# cd insight/
# python setup.py develop
%aimport insight.plotting
insight.plotting.plot_prod_vs_hours(df3, color_index=1)
# insight.plotting.plot_prod_vs_hours(d... |
jdhp-docs/python-notebooks | python_re.ipynb | mit | s = "Maison 3 pièce(s) - 68.05 m² - 860 € par mois charges comprises"
re.findall(r'\d+\.?\d*', s)
re.findall(r'\b\d+\.?\d*\b', s)
"""
Explanation: Extract numbers from a string
End of explanation
"""
s = "Maison 3 pièce(s) - 68.05 m² - 860 € par mois charges comprises"
if re.search(r'Maison', s):
print("Found... |
CompPhysics/MachineLearning | doc/pub/week38/ipynb/week38.ipynb | cc0-1.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
from sklearn.linear_model import Ridge
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import PolynomialFeatures
# A seed just to ensure that the random numbers are the same f... |
cmorgan/toyplot | docs/table-axes.ipynb | bsd-3-clause | import numpy
import toyplot.data
data_table = toyplot.data.read_csv("temperatures.csv")
data_table = data_table[:10]
"""
Explanation: .. _table-axes:
Table Axes
Data tables, with rows containing observations and columns containing variables or series, are arguably the cornerstone of science. Much of the functionality... |
Unidata/unidata-python-workshop | notebooks/Metpy_Introduction/Introduction to MetPy.ipynb | mit | # Import the MetPy unit registry
from metpy.units import units
length = 10.4 * units.inches
width = 20 * units.meters
print(length, width)
"""
Explanation: <div style="width:1000 px">
<div style="float:right; width:98 px; height:98px;">
<img src="https://raw.githubusercontent.com/Unidata/MetPy/master/metpy/plots/_st... |
eshlykov/mipt-day-after-day | statistics/hw-11/11.7-9.ipynb | unlicense | import numpy
import scipy.stats
n = 800 # Размер выборки
mu = numpy.array([74, 92, 83, 79, 80, 73, 77, 75, 76, 91])
expected = n * numpy.full(10, 0.1)
"""
Explanation: Задача 11.7
Цифры $0, 1, 2, \ldots, 9$ среди $800$ первых десятичных знаков числа $\pi$ появились $74, 92, 83, 79,
80, 73, 77, 75, 76, 91$ раз соотв... |
geoneill12/phys202-2015-work | assignments/assignment04/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... |
ahwillia/RecNetLearn | tutorials/FORCE_Learning_recurrent_feedforward.ipynb | mit | from __future__ import division
from scipy.integrate import odeint,ode
from numpy import zeros,ones,eye,tanh,dot,outer,sqrt,linspace,pi,exp,tile,arange,reshape
from numpy.random import uniform,normal,choice
import pylab as plt
import numpy as np
%matplotlib inline
"""
Explanation: Embedding a Feedforward Cascade in a ... |
gourie/training_RL | gym_ex_taxi.ipynb | bsd-3-clause | env = gym.make("Taxi-v2")
env.reset() # init state value of env
env.observation_space.n # number of possible values in this state space
env.action_space.n # number of possible actions
# print(env.action_space)
# 0 = down
# 1 = up
# 2 = right
# 3 = left
# 4 = pickup
# 5 = drop-off
env.render()
# In this enviro... |
darkomen/TFG | medidas/03082015/.ipynb_checkpoints/modelado-checkpoint.ipynb | cc0-1.0 | #Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
from scipy import signal
#Mostramos las versiones usadas de cada librerías
print ("Numpy v{}".format(np.__version__))
print ("Pandas v{}".format(pd.__version__))
print ("Seaborn v{}".format(sns.__version__))
%pylab inlin... |
claudiuskerth/PhDthesis | Data_analysis/SNP-indel-calling/dadi/05_1D_model_synthesis.ipynb | mit | # load dadi module
import sys
sys.path.insert(0, '/home/claudius/Downloads/dadi')
import dadi
%ll
%ll dadiExercises
# import 1D spectrum of ery
fs_ery = dadi.Spectrum.from_file('dadiExercises/ERY.FOLDED.sfs.dadi_format')
# import 1D spectrum of ery
fs_par = dadi.Spectrum.from_file('dadiExercises/PAR.FOLDED.sfs.d... |
madHatter106/DataScienceCorner | posts/xarray-geoviews-a-new-perspective-on-oceanographic-data-part-ii.ipynb | mit | import xarray as xr
import os
import glob
"""
Explanation: In a previous post, I introduced xarray with some simple manipulation and data plotting. In this super-short post, I'm going to do some more manipulation, using multiple input files to create a new dimension, reorganize the data and store them in multiple outp... |
llvll/motionml | ip[y]/motionml.ipynb | bsd-2-clause | from tinylearn import KnnDtwClassifier
from tinylearn import CommonClassifier
import pandas as pd
import numpy as np
import os
train_labels = []
test_labels = []
train_data_raw = []
train_data_hist = []
test_data_raw = []
test_data_hist = []
# Utility function for normalizing numpy arrays
def normalize(v):
norm =... |
DiXiT-eu/collatex-tutorial | unit8/unit8-collatex-and-XML/Custom sort.ipynb | gpl-3.0 | import re
"""
Explanation: Defining a custom sort for a complex value
We need to sort data that is partially numeric and partially alphabetic, in this case the line numbers 1, 4008, 4008a, 4009, and 9. We can’t sort them numerically because the 'a' isn’t numeric. And we can’t sort them alphabetically because the numbe... |
nproctor/phys202-2015-work | assignments/assignment04/MatplotlibEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 2
Imports
End of explanation
"""
!head -n 30 open_exoplanet_catalogue.txt
"""
Explanation: Exoplanet properties
Over the past few decades, astronomers have discovered thousands of extrasolar planets. The follo... |
hiteshagrawal/python | udacity/nano-degree/.ipynb_checkpoints/L1_Starter_Code-checkpoint.ipynb | gpl-2.0 | import unicodecsv
## Longer version of code (replaced with shorter, equivalent version below)
# enrollments = []
# f = open('enrollments.csv', 'rb')
# reader = unicodecsv.DictReader(f)
# for row in reader:
# enrollments.append(row)
# f.close()
def readme(filename):
with open(filename, 'rb') as f:
rea... |
quantumlib/Cirq | docs/qubits.ipynb | apache-2.0 | try:
import cirq
except ImportError:
print("installing cirq...")
!pip install --quiet cirq
print("installed cirq.")
import cirq
"""
Explanation: Qubits
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://quantumai.google/cirq/qubits"><img src="https://quant... |
timothydmorton/VESPA | notebooks/predictions.ipynb | mit | from keputils import kicutils as kicu
stars = kicu.DATA # This is Q17 stellar table.
"""
Explanation: The question to explore is the following: given Kepler observations, how many events of the following type are we likely to observe as single eclipse events?
EBs
BEBs
HEBs
More specifically, given a period and depth... |
bill9800/House-Prediciton | HousePrediction.ipynb | mit | #missing data
total = df_train.isnull().sum().sort_values(ascending = False)
percent = (df_train.isnull().sum()/df_train.isnull().count()).sort_values(ascending = False)
missing_data = pd.concat([total,percent],axis=1,keys=['Total','Percent'])
missing_data.head(25)
# In the search for normality
sns.distplot(df_train[... |
brian-rose/climlab | docs/source/courseware/Reset-time.ipynb | mit | import numpy as np
import climlab
climlab.__version__
"""
Explanation: Resetting time to zero after cloning a climlab process
Brian Rose, 2/15/2022
Here are some notes on how to reset a model's internal clock to zero after cloning a process with climlab.process_like()
These notes may become out of date after the next ... |
fluffy-hamster/A-Beginners-Guide-to-Python | A Beginners Guide to Python/26. Design Decisions, How to Build Chess Game.ipynb | mit | # One use, "throw away" code:
def one_to_one_hundred():
for i in range(1, 101):
print (i)
# Multi use, 'generalised' code:
def n_to_x(n, m):
for i in range(n, m+1):
print(i)
"""
Explanation: Features of Good Design
Hi guys, this lecture is a bit different, today we are mostly glossing ... |
Lattecom/HYStudy | scripts/[HYStudy 15th] Matplotlib 2.ipynb | mit | # make point with cumulative sum
points = np.random.randn(50).cumsum()
points
"""
Explanation: Magic Command
%matplotlib inline: show() 생략
%matplotlib qt: 외부창 출력
End of explanation
"""
# plt.plot(x, y): x, y = point(x, y) on coordinate
# put y only(default x = auto)
plt.plot(points)
plt.show()
# put x and y points... |
kingb12/languagemodelRNN | old_comparisons/testcompare.ipynb | mit | report_files = ["/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing6_200_512_04drb/encdec_noing6_200_512_04drb.json", "/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing6_bow_200_512_04drb/encdec_noing6_bow_200_512_04drb.json"]
log_files = ["/Users/bking/IdeaProjects/Lang... |
Vvkmnn/books | AutomateTheBoringStuffWithPython/lesson22.ipynb | gpl-3.0 | #! usr/bin/env bash
# This is a shell script
#python3 runthisscript.py
#echo 'I'm running a python script'
"""
Explanation: Lesson 22:
Launching Python in Other Programs
The first line of any Pthon Script should be the Shebang Line.
OSX:
#! /usr/bin/env python3
Linux:
#! usr/bin/python3
Windows:
python3
This ... |
dalonlobo/GL-Mini-Projects | TweetAnalysis/Final/Q7/Dalon_4_RTD_MiniPro_Tweepy_Q7.ipynb | mit | import logging # python logging module
# basic format for logging
logFormat = "%(asctime)s - [%(levelname)s] (%(funcName)s:%(lineno)d) %(message)s"
# logs will be stored in tweepy.log
logging.basicConfig(filename='tweepyretweet.log', level=logging.INFO,
format=logFormat, datefmt="%Y-%m-%d %H:%M:%S... |
PySCeS/PyscesToolbox | example_notebooks/RateChar.ipynb | bsd-3-clause | mod = pysces.model('lin4_fb.psc')
rc = psctb.RateChar(mod)
"""
Explanation: RateChar
RateChar is a tool for performing generalised supply-demand analysis (GSDA) [5,6]. This entails the generation data needed to draw rate characteristic plots for all the variable species of metabolic model through parameter scans and t... |
nicococo/scRNA | notebooks/example.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from functools import partial
from sklearn.manifold import TSNE
import sklearn.metrics as metrics
from scRNA.simulation import generate_toy_data, split_source_target
from scRNA.nmf_clustering import NmfClustering_initW, NmfClustering, DaNmfCluste... |
kidpixo/multibinner | examples/example_multibinner.ipynb | mit | image_df = pd.DataFrame(image.reshape(-1,image.shape[-1]),columns=['red','green','blue'])
image_df.describe()
n_data = image.reshape(-1,image.shape[-1]).shape[0]*10 # 10 times the original number of pixels : overkill!
x = np.random.random_sample(n_data)*image.shape[1]
y = np.random.random_sample(n_data)*image.shape[0]... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_source_power_spectrum.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, compute_source_psd
print(__doc__)
"""
Explanation: Compute power spect... |
ContinuumIO/pydata-apps | Section_1_blaze_solutions.ipynb | mit | import pandas as pd
df = pd.read_csv('iris.csv')
df.head()
df.groupby(df.Species).PetalLength.mean() # Average petal length per species
"""
Explanation: <img src="images/continuum_analytics_logo.png"
alt="Continuum Logo",
align="right",
... |
mne-tools/mne-tools.github.io | 0.23/_downloads/ca1574468d033ed7a4e04f129164b25b/20_cluster_1samp_spatiotemporal.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import os.path as op
import numpy as np
from numpy.random import randn
from scipy import stats as stats
import mne
from mne.epochs import equalize_epoch_counts
from mne.stats import ... |
ffmmjj/intro_to_data_science_workshop | solutions/en_US/04-Example: Titanic survivors analysis.ipynb | apache-2.0 | import pandas as pd
raw_data = pd.read_csv('datasets/titanic.csv')
raw_data.head()
raw_data.info()
"""
Explanation: Titanic survival analysis
The Titanic survivors dataset is popularly used to illustrate concepts of data cleaning and exploration.
Let's start by importing the data to a pandas DataFrame from a CSV fi... |
patrickbreen/patrickbreen.github.io | notebooks/vae_my_version.ipynb | mit | import sys
import os
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(0)
tf.set_random_seed(0)
# get the script bellow from
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/mnist/input_data.py
import input_data
mnist =... |
guyk1971/deep-learning | sentiment-rnn/Sentiment_RNN.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
sailuh/perceive | Notebooks/CWE/Fielder_Parser/Legacy CWE Field Parser.ipynb | gpl-2.0 | tree = lxml.etree.parse('cwec_v3.0.xml')
root = tree.getroot()
# Remove namespaces from XML.
for elem in root.getiterator():
if not hasattr(elem.tag, 'find'): continue # (1)
i = elem.tag.find('}') # Counts the number of characters up to the '}' at the end of the XML namespace within the XML tag
if i >=... |
AllenDowney/ProbablyOverthinkingIt | falsepos.ipynb | mit | from __future__ import print_function, division
import thinkbayes2
from sympy import symbols
"""
Explanation: Exploration of a problem interpreting binary test results
Copyright 2015 Allen Downey
MIT License
End of explanation
"""
p, q, s, t1, t2 = symbols('p q s t1 t2')
"""
Explanation: p is the prevalence of a ... |
mne-tools/mne-tools.github.io | 0.22/_downloads/3674b896fc4e4a279156fa5c0f61aea8/plot_10_preprocessing_overview.ipynb | bsd-3-clause | import os
import numpy as np
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
raw.crop(0, 60).load_data() # just use a fr... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/tensorflow_extended/solutions/Simple_TFX_Pipeline_for_Vertex_Pipelines.ipynb | apache-2.0 | # Use the latest version of pip.
!pip install --upgrade pip
!pip install --upgrade "tfx[kfp]<2"
"""
Explanation: Creating Simple TFX Pipeline for Vertex Pipelines
Learning objectives
Prepare example data.
Create a pipeline.
Run the pipeline on Vertex Pipelines.
Introduction
In this notebook, you will create a simple... |
emsi/ml-toolbox | random/Atmosfera/MODEL-11-conv.ipynb | agpl-3.0 | # Dane wejściowe
with open("X-sequences.pickle", 'rb') as f:
X = pickle.load(f)
with open("Y.pickle", 'rb') as f:
Y = pickle.load(f)
# Zostaw tylko poniższe kategorie, pozostale zmień na -1
lista = [2183,
#325,
37, 859, 2655, 606, 412, 2729, 1683, 1305]
# Y=[y if y in lista else -1 for... |
KMFleischer/PyEarthScience | Tutorial/04_PyNGL_basics.ipynb | mit | import Ngl
"""
Explanation: 4. PyNGL basics
PyNGL is a Python language module for creating 2D high performance visualizations of scientific data. It is based on NCL graphics but still not as extensive as NCL's last version 6.6.2.
The aim of this notebook is to give you an introduction to PyNGL, read your data from fil... |
satishgoda/learning | web/html.ipynb | mit | from IPython.display import HTML, Javascript
HTML("Hello World")
"""
Explanation: HTML and w3 Schools
End of explanation
"""
!gvim draggable_1.html
HTML('./draggable_1.html')
"""
Explanation: Supporting Technologies
jQuery
Examples
Draggable Elements
https://www.w3schools.com/tags/att_global_draggable.asp
http... |
mathinmse/mathinmse.github.io | Lecture-13-Integral-Transforms.ipynb | mit | import sympy as sp
sp.init_printing(use_latex=True)
# symbols we will need below
x,y,z,t,c = sp.symbols('x y z t c')
# note the special declaration that omega is a positive number
omega = sp.symbols('omega', positive=True)
"""
Explanation: Lecture 13: Integral Transforms, D/FFT and Electron Microscopy
Background
A... |
kubeflow/pipelines | components/gcp/dataproc/submit_hive_job/sample.ipynb | apache-2.0 | %%capture --no-stderr
!pip3 install kfp --upgrade
"""
Explanation: Name
Data preparation using Apache Hive on YARN with Cloud Dataproc
Label
Cloud Dataproc, GCP, Cloud Storage, YARN, Hive, Apache
Summary
A Kubeflow Pipeline component to prepare data by submitting an Apache Hive job on YARN to Cloud Dataproc.
Details
... |
mne-tools/mne-tools.github.io | dev/_downloads/9e70404d3a55a6b6d1c1877784347c14/mixed_source_space_inverse.ipynb | bsd-3-clause | # Author: Annalisa Pascarella <a.pascarella@iac.cnr.it>
#
# License: BSD-3-Clause
import os.path as op
import matplotlib.pyplot as plt
from nilearn import plotting
import mne
from mne.minimum_norm import make_inverse_operator, apply_inverse
# Set dir
data_path = mne.datasets.sample.data_path()
subject = 'sample'
da... |
slundberg/shap | notebooks/image_examples/image_classification/Explain MobilenetV2 using the Partition explainer (PyTorch).ipynb | mit | import json
import numpy as np
import torchvision
import torch
import torch.nn as nn
import shap
from PIL import Image
"""
Explanation: Explain PyTorch MobileNetV2 using the Partition explainer
In this example we are explaining the output of MobileNetV2 for classifying images into 1000 ImageNet classes.
End of explana... |
fsilva/deputado-histogramado | notebooks/Deputado-Histogramado-5.ipynb | gpl-3.0 | %matplotlib inline
import pylab
import matplotlib
import pandas
import numpy
dateparse = lambda x: pandas.datetime.strptime(x, '%Y-%m-%d')
sessoes = pandas.read_csv('sessoes_democratica_org.csv',index_col=0,parse_dates=['data'], date_parser=dateparse)
del sessoes['tamanho']
total0 = numpy.sum(sessoes['sessao'].m... |
jinntrance/MOOC | coursera/ml-clustering-and-retrieval/assignments/2_kmeans-with-text-data_blank.ipynb | cc0-1.0 | import graphlab
import matplotlib.pyplot as plt
import numpy as np
import sys
import os
from scipy.sparse import csr_matrix
%matplotlib inline
'''Check GraphLab Create version'''
from distutils.version import StrictVersion
assert (StrictVersion(graphlab.version) >= StrictVersion('1.8.5')), 'GraphLab Create must be ve... |
GoogleCloudPlatform/mlops-on-gcp | environments_setup/mlops-composer-mlflow/caip-training-test.ipynb | apache-2.0 | import os
import re
from IPython.core.display import display, HTML
from datetime import datetime
import mlflow
import pymysql
# Jupyter magic template to create Python file with variable substitution
from IPython.core.magic import register_line_cell_magic
@register_line_cell_magic
def writetemplate(line, cell):
w... |
bretthandrews/marvin | docs/sphinx/jupyter/my-first-query.ipynb | bsd-3-clause | # Python 2/3 compatibility
from __future__ import print_function, division, absolute_import
from marvin import config
config.mode = 'remote'
config.setRelease('MPL-4')
from marvin.tools.query import Query
"""
Explanation: My First Query
One of the most powerful features of Marvin 2.0 is ability to query the newly cr... |
samuelshaner/openmc | docs/source/pythonapi/examples/mgxs-part-ii.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import openmoc
from openmoc.opencg_compatible import get_openmoc_geometry
import openmc
import openmc.mgxs as mgxs
import openmc.data
%matplotlib inline
"""
Explanation: This IPython Notebook illustrates the use of the openmc.mgxs module to ca... |
eecs445-f16/umich-eecs445-f16 | lecture11_info-theory-decision-trees/collocations/Collocations Example.ipynb | mit | # read text file
text_path = "data/crime-and-punishment.txt";
with open(text_path) as f:
text_raw = f.read().lower();
# remove punctuation
translate_table = dict((ord(char), None) for char in string.punctuation);
text_raw = text_raw.translate(translate_table);
# tokenize
tokens = nltk.word_tokenize(text_raw);
big... |
CompPhysics/MachineLearning | doc/src/week43/.ipynb_checkpoints/week43-checkpoint.ipynb | cc0-1.0 | %matplotlib inline
# Start importing packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
from tensorflow.keras.layers import Input
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.layer... |
tensorflow/probability | tensorflow_probability/examples/statistical_rethinking/notebooks/02_small_worlds_and_large_worlds.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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, sof... |
mxbu/logbook | blog-notebooks/arctic_crypto_database.ipynb | mit | import urllib
import json
import time
import pandas as pd
import datetime
from arctic import Arctic
import arctic
import subprocess
import platform
import os
import krakenex
if platform.system() == "Darwin":
os.chdir('/users/'+os.getlogin()+'/MEGA/App')
if platform.system() == "Darwin":
subprocess.Popen(['/usr/loc... |
parrt/lolviz | examples.ipynb | bsd-3-clause | from lolviz import *
objviz([u'2016-08-12',107.779999,108.440002,107.779999,108.18])
table = [
['Date','Open','High','Low','Close','Volume'],
['2016-08-12',107.779999,108.440002,107.779999,108.18,18612300,108.18],
]
objviz(table)
d = dict([(c,chr(c)) for c in range(ord('a'),ord('f'))])
objviz(d)
tuplelist =... |
computational-class/cjc2016 | code/0.common_questions.ipynb | mit | import graphlab as gl
from IPython.display import display
from IPython.display import Image
gl.canvas.set_target('ipynb')
"""
Explanation: 在anaconda 环境中运行jupyter notebook
问题及其解决方法
Mac电脑如何快速找到用户目录
- 1、在finder的偏好设置中选择边栏选中个人收藏下房子的图标，然后在边栏就可以看到用户目录，然后就可以找到目录了。
2、在finder的偏好设置中选择通用，然后选择磁盘，磁盘就出现在桌面了，这样也可以很方便的进入根目录，进而找... |
tensorflow/docs-l10n | site/ko/guide/checkpoint.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... |
mdeff/ntds_2016 | algorithms/06_sol_recurrent_nn.ipynb | mit | # Import libraries
import tensorflow as tf
import numpy as np
import collections
import os
# Load text data
data = open(os.path.join('datasets', 'text_ass_6.txt'), 'r').read() # must be simple plain text file
print('Text data:',data)
chars = list(set(data))
print('\nSingle characters:',chars)
data_len, vocab_size = le... |
elenduuche/deep-learning | gan_mnist/Intro_to_GANs_Solution.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
rvperry/phys202-2015-work | assignments/assignment05/InteractEx03.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 3
Imports
End of explanation
"""
np.sech?
def soliton(x, t, c, a):
"""Return phi(x, t) for a soliton wa... |
jacobdein/alpine-soundscapes | Calculate elevation range.ipynb | mit | from geo.models import Raster
from geo.models import Boundary
import rasterio
from shapely.geometry import shape
import numpy
import numpy.ma
import rasterio.mask
from matplotlib import cm as colormaps
from matplotlib import pyplot
%matplotlib inline
"""
Explanation: Calculate elevation range
This notebook calculate... |
InsightSoftwareConsortium/ITKExamples | src/Core/Transform/MutualInformationAffine/MutualInformationAffine.ipynb | apache-2.0 | import os
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from urllib.request import urlretrieve
import itk
from itkwidgets import compare, checkerboard
"""
Explanation: Mutual Information Metric
The MutualInformationImageToImageMetric class computes the mutual information between two ima... |
cosmicBboy/mLearn | 02. Linear Regression.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('ggplot')
# X is the explanatory variable data structure
X = [[6], [8], [10], [14], [18]]
# Y is the response variable data structure
y = [[7], [9], [13], [17.5], [18]]
# instantiate a pyplot figure object
plt.figure()
plt.title('Fi... |
flsantos/startup_acquisition_forecast | dataset_preparation.ipynb | mit | #All imports here
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import preprocessing
from datetime import datetime
from dateutil import relativedelta
%matplotlib inline
#Let's start by importing our csv files into dataframes
df_companies = pd.read_csv('data/companies.csv')
df_acq... |
sandeep-n/incubator-systemml | samples/jupyter-notebooks/Linear_Regression_Algorithms_Demo.ipynb | apache-2.0 | !pip show systemml
"""
Explanation: Linear Regression Algorithms using Apache SystemML
This notebook shows:
- Install SystemML Python package and jar file
- pip
- SystemML 'Hello World'
- Example 1: Matrix Multiplication
- SystemML script to generate a random matrix, perform matrix multiplication, and compute th... |
karlstroetmann/Artificial-Intelligence | Python/4 Automatic Theorem Proving/Knuth-Bendix-Algorithm-KBO.ipynb | gpl-2.0 | %run Parser.ipynb
!cat Examples/quasigroups.eqn || type Examples\quasigroups.eqn
def test():
t = parse_term('x * y * z')
print(t)
print(to_str(t))
eq = parse_equation('i(x) * x = 1')
print(eq)
print(to_str(parse_file('Examples/quasigroups.eqn')))
test()
"""
Explanation: The Knuth-Bendix ... |
darrenxyli/deeplearning | lessons/handwritten/handwritten-digit-recognition-with-tflearn-exercise.ipynb | apache-2.0 | # 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... |
mne-tools/mne-tools.github.io | 0.21/_downloads/a68128275cc59b074b8c9782296d1d4a/decoding_rsa.ipynb | bsd-3-clause | # Authors: Jean-Remi King <jeanremi.king@gmail.com>
# Jaakko Leppakangas <jaeilepp@student.jyu.fi>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
from pandas import read_csv
import matplotlib.pyplot as plt
from sklearn.model_sel... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/introduction_to_tensorflow/labs/2_dataset_api.ipynb | apache-2.0 | # Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.0 || pip install tensorflow==2.0
import json
import math
import os
from pprint import pprint
import numpy as np
import tensorflow as tf
print(tf.version.VERSION)
"""
Explanation: TensorFlow Dataset API
Learning Objectives
1. Lear... |
marcelomiky/PythonCodes | Coursera/CICCP2/Curso Introdução à Ciência da Computação com Python - Parte 2.ipynb | mit | def cria_matriz(tot_lin, tot_col, valor):
matriz = [] #lista vazia
for i in range(tot_lin):
linha = []
for j in range(tot_col):
linha.append(valor)
matriz.append(linha)
return matriz
x = cria_matriz(2, 3, 99)
x
def cria_matriz(tot_lin, tot_col, valor):
matriz =... |
quoniammm/happy-machine-learning | Udacity-ML/boston_housing-master_1/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
# Pretty display for notebooks
# 让结果在notebook中显示
%matplotlib inline
# Load the Boston housing dataset
# 载入波士顿房屋的数据集
data = pd.rea... |
dcavar/python-tutorial-for-ipython | notebooks/Python Scikit-Learn for Computational Linguists.ipynb | apache-2.0 | from sklearn import datasets
"""
Explanation: Python Scikit-Learn for Computational Linguists
(C) 2017 by Damir Cavar
Version: 1.0, January 2017
License: Creative Commons Attribution-ShareAlike 4.0 International License (CA BY-SA 4.0)
This tutorial was developed as part of my course material for the course Machine Lea... |
mathemage/h2o-3 | examples/deeplearning/notebooks/deeplearning_anomaly_detection.ipynb | apache-2.0 | import h2o
from h2o.estimators.deeplearning import H2OAutoEncoderEstimator
h2o.init()
%matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import os.path
PATH = os.path.expanduser("~/h2o-3/")
train_ecg = h2o.import_file(PATH + "smalldata/anomaly/ecg_discord_train.csv")
test_ecg = h... |
mne-tools/mne-tools.github.io | dev/_downloads/70e603ce6ceb1fd2cb094ccee99a1920/resolution_metrics_eegmeg.ipynb | bsd-3-clause | # Author: Olaf Hauk <olaf.hauk@mrc-cbu.cam.ac.uk>
#
# License: BSD-3-Clause
import mne
from mne.datasets import sample
from mne.minimum_norm.resolution_matrix import make_inverse_resolution_matrix
from mne.minimum_norm.spatial_resolution import resolution_metrics
print(__doc__)
data_path = sample.data_path()
subject... |
llclave/Springboard-Mini-Projects | Heights and Weights Using Logistic Regression/Mini_Project_Logistic_Regression.ipynb | mit | %matplotlib inline
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('display.notebook_repr... |
ES-DOC/esdoc-jupyterhub | notebooks/mpi-m/cmip6/models/icon-esm-lr/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'icon-esm-lr', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: MPI-M
Source ID: ICON-ESM-LR
Topic: Ocean
Sub-Topics: Timestepping Framework, Adv... |
AllenDowney/ModSimPy | soln/rabbits3soln.ipynb | mit | %matplotlib inline
from modsim import *
"""
Explanation: Modeling and Simulation in Python
Rabbit example
Copyright 2017 Allen Downey
License: Creative Commons Attribution 4.0 International
End of explanation
"""
system = System(t0 = 0,
t_end = 20,
juvenile_pop0 = 0,
... |
quantumlib/Cirq | docs/tutorials/basics.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 unde... |
martinggww/lucasenlights | MachineLearning/DataScience-Python3/MatPlotLib.ipynb | cc0-1.0 | %matplotlib inline
from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(-3, 3, 0.01)
plt.plot(x, norm.pdf(x))
plt.show()
"""
Explanation: MatPlotLib Basics
Draw a line graph
End of explanation
"""
plt.plot(x, norm.pdf(x))
plt.plot(x, norm.pdf(x, 1.0, 0.5))
plt.show()
"""
E... |
lknelson/DH-Institute-2017 | 01-Intro to NLP/.ipynb_checkpoints/Intro to NLP-checkpoint.ipynb | bsd-2-clause | print("For me it has to do with the work that gets done at the crossroads of digital media and traditional humanistic study. And that happens in two different ways. On the one hand, it's bringing the tools and techniques of digital media to bear on traditional humanistic questions; on the other, it's also bringing huma... |
kit-cel/lecture-examples | qc/quantization/Uniform_Quantization_Sine.ipynb | gpl-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import librosa
import librosa.display
import IPython.display as ipd
"""
Explanation: Illustration of Uniform Quantization
This code is provided as supplementary material of the lecture Quellencodierung.
This code illustrates
* Uniform scalar quantiz... |
amirziai/learning | python/Using reindex for adding missing columns to a dataframe.ipynb | mit | import pandas as pd
df = pd.DataFrame([
{
'a': 1,
'b': 2,
'd': 4
}
])
df
"""
Explanation: Use reindex for adding missing columns to a dataframe
End of explanation
"""
columns = ['a', 'b', 'c', 'd']
df.reindex(columns=columns, fill_value=0)
"""
Explanation: Using reindex to add mis... |
Mynti207/cs207project | docs/demo.ipynb | mit | # you must specify the length of the time series when loading the database
ts_length = 100
# when running from the terminal
# python go_server_persistent.py --ts_length 100 --db_name 'demo'
# here we load the server as a subprocess for demonstration purposes
server = subprocess.Popen(['python', '../go_server_persiste... |
rainyear/pytips | Tips/2016-03-08-Functional-Programming-in-Python.ipynb | mit | # map 函数的模拟实现
def myMap(func, iterable):
for arg in iterable:
yield func(arg)
names = ["ana", "bob", "dogge"]
print(map(lambda x: x.capitalize(), names)) # Python 2.7 中直接返回列表
for name in myMap(lambda x: x.capitalize(), names):
print(name)
# filter 函数的模拟实现
def myFilter(func, iterable):
for arg in ... |
CalPolyPat/phys202-2015-work | assignments/assignment06/InteractEx05.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.html import widgets
from IPython.display import display, SVG
"""
Explanation: Interact Exercise 5
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widge... |
ethen8181/machine-learning | time_series/fft/fft.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(css_style='custom2.css', plot_style=False)
os.chdir(path)
# 1. magic for inline plot... |
rvernagus/data-science-notebooks | Data Science From Scratch/7 - Hypothesis And Inference.ipynb | mit | def normal_approximation_to_binomial(n, p):
"""return mu and sigma corresponding to Binomial(n, p)"""
mu = p * n
sigma = math.sqrt(p * (1 - p) * n)
return mu, sigma
normal_probability_below = normal_cdf
def normal_probability_above(lo, mu=0, sigma=1):
return 1 - normal_cdf(lo, mu, sigma)
def norm... |
tbphu/fachkurs_bachelor | tellurium/loesungen/Roadrunner_Uebung_Loesung.ipynb | mit | Repressilator = urllib2.urlopen('http://antimony.sourceforge.net/examples/biomodels/BIOMD0000000012.txt').read()
"""
Explanation: Roadrunner Methoden
Antimony Modell aus Modell-Datenbank abfragen:
Lade mithilfe von urllib2 das Antimony-Modell des "Repressilator" herunter. Benutze dazu die urllib2 Methoden urlopen() un... |
dipanjanS/text-analytics-with-python | New-Second-Edition/Ch03 - Processing and Understanding Text/Ch03c - BONUS - Text Parsing with Stanford CoreNLP.ipynb | apache-2.0 | # set java path
import os
java_path = r'C:\Program Files\Java\jre1.8.0_192\bin\java.exe'
os.environ['JAVAHOME'] = java_path
from nltk.parse.stanford import StanfordParser
scp = StanfordParser(path_to_jar='E:/stanford/stanford-parser-full-2015-04-20/stanford-parser.jar',
path_to_models_jar='E:/sta... |
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