repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
fpavogt/pyqz | docs/source/pyqz_demo_advanced.ipynb | gpl-3.0 | %matplotlib inline
import pyqz
import pyqz.pyqz_plots as pyqzp
"""
Explanation: Running pyqz II
First things first, let's start by importing pyqz and pyqz_plots.
End of explanation
"""
fn = pyqz.pyqzt.get_MVphotogrid_fn(Pk=6.7, calibs='GCZO', kappa =10, struct='pp')
print fn.split('/')[-1]
"""
Explanation: D) Using... |
mne-tools/mne-tools.github.io | 0.17/_downloads/a3032aa342891a49e0e20bef01b3ad38/plot_visualize_evoked.ipynb | bsd-3-clause | import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
# sphinx_gallery_thumbnail_number = 9
"""
Explanation: Visualize Evoked data
In this tutorial we focus on the plotting functions of :class:mne.Evoked.
End of explanation
"""
data_path = mne.datasets.sample.data_path()
fname = op.joi... |
hich28/mytesttxx | tests/python/randltl.ipynb | gpl-3.0 | import spot
"""
Explanation: Documentation for spot's randltl python binding
End of explanation
"""
f = spot.randltl(['a', 'b', 'c'])
for i in range(3):
print(next(f))
"""
Explanation: Basic usage
Generate random formulas from specified atomic propositions:
End of explanation
"""
f = spot.randltl(3)
for i in ... |
ray-project/ray | doc/source/tune/examples/tune-mlflow.ipynb | apache-2.0 | import os
import tempfile
import time
import mlflow
from ray import tune
from ray.tune.integration.mlflow import MLflowLoggerCallback, mlflow_mixin
"""
Explanation: Using MLflow with Tune
(tune-mlflow-ref)=
:::{warning}
If you are using these MLflow integrations with {ref}ray-client, it is recommended that you setup... |
GoogleCloudPlatform/nvidia-merlin-on-vertex-ai | 01-dataset-preprocessing.ipynb | apache-2.0 | import os
import json
from datetime import datetime
from google.cloud import aiplatform as vertex_ai
from kfp.v2 import compiler
"""
Explanation: Data Preprocessing at Scale with NVIDIA Merlin NVTabular and Vertex AI
This notebook demonstrates how to preprocess data using NVIDIA Merlin NVTabular and Vertex AI. The not... |
martinjrobins/hobo | examples/stats/multiplicative-gaussian-errors.ipynb | bsd-3-clause | import pints
import pints.toy as toy
import pints.plot
import numpy as np
import matplotlib.pyplot as plt
import pints.noise
# Use the toy logistic model
model = toy.LogisticModel()
# Set some parameters
real_parameters = [0.1, 50]
# Create fake data
times = np.linspace(0, 100, 500)
values = model.simulate(real_para... |
eco32i/biodata | sessions/examples/01 - Python machine learning - dimensionality reduction.ipynb | mit | def parse_barcodes(bcfile, bc_id='BC'):
res = {}
with open(bcfile, 'r') as fi:
for line in fi:
fields = line.strip().split(',')
if fields[0].startswith(bc_id):
res[fields[0]] = fields[1]
return res
def parse_exp_config(expfile, bc_dict):
res = []
fie... |
mintcloud/deep-learning | Project 2/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'
class DLProgress(tqdm):
last_block = 0
def hoo... |
zhenxu66/data_analysis_with_python_and_pandas | 3 - NumPy Basics/3-5 NumPy Array Basics - Querying, Slicing, Combining, and Splitting Arrays.ipynb | apache-2.0 | import sys
print(sys.version)
import numpy as np
print(np.__version__)
"""
Explanation: NumPy Array Basics - Querying, Slicing, Combining, and Splitting Arrays
End of explanation
"""
np.random.seed(10)
"""
Explanation: In this video we’ll be covering querying, slicing, combining, and splitting arrays. Now this info... |
WillenZh/deep-learning-project | tutorials/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... |
henchc/Data-on-the-Mind-2017-scraping-apis | 01-APIs/01-API_workbook.ipynb | mit | import requests # to make the GET request
import json # to parse the JSON response to a Python dictionary
import time # to pause after each API call
import csv # to write our data to a CSV
import pandas # to see our CSV
"""
Explanation: Accessing Databases via Web APIs
In this lesson we'll learn what an API (Ap... |
enakai00/jupyter_ml4se_commentary | 05-pandas DataFrame-01.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from pandas import Series, DataFrame
"""
Explanation: pandasのデータフレーム
End of explanation
"""
from numpy.random import randint
dices = randint(1,7,(5,2))
dices
"""
Explanation: ２次元の array を DataFrame に変換する例です。
End of explanation
"""
diceroll = D... |
djgagne/hagelslag | demos/unidata_users_workshop_2015.ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
from mpl_toolkits.basemap import Basemap
from IPython.display import display, Image
from ipywidgets import widgets, interact
from scipy.ndimage import gaussian_filter, find_objects
from cop... |
mne-tools/mne-tools.github.io | 0.18/_downloads/1048fcdfaf4847afa747b1cc9df74e2d/plot_movement_compensation.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
from os import path as op
import mne
from mne.preprocessing import maxwell_filter
print(__doc__)
data_path = op.join(mne.datasets.misc.data_path(verbose=True), 'movement')
head_pos = mne.chpi.read_head_pos(op.join(data_path, 'simulated_qu... |
dcavar/python-tutorial-for-ipython | notebooks/Python NLTK - Texts and Frequencies.ipynb | apache-2.0 | ifile = open("data/HOPG.txt", mode='r', encoding='utf-8')
text = ifile.read()
ifile.close()
print(text[:300], "...")
"""
Explanation: Python NLTK: Texts and Frequencies
(C) 2017-2019 by Damir Cavar <dcavar@iu.edu>
Version: 0.5, October 2019
Download: This and... |
cseed/hail | hail/python/hail/docs/tutorials/01-genome-wide-association-study.ipynb | mit | import hail as hl
hl.init()
"""
Explanation: GWAS Tutorial
This notebook is designed to provide a broad overview of Hail's functionality, with emphasis on the functionality to manipulate and query a genetic dataset. We walk through a genome-wide SNP association test, and demonstrate the need to control for confounding... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_sensor_noise_level.ipynb | bsd-3-clause | # Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import mne
data_path = mne.datasets.sample.data_path()
raw_erm = mne.io.read_raw_fif(op.join(data_path, 'MEG', 'sample',
'ernoise_raw.fif'), preload=True)
"""
Explanation: Show nois... |
transcranial/keras-js | notebooks/layers/convolutional/ZeroPadding1D.ipynb | mit | data_in_shape = (3, 5)
L = ZeroPadding1D(padding=1)
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(240)
data_in = 2 * np.random.random(data_in_shape) - 1
result = model.predict(np.array([dat... |
tensorflow/hub | examples/colab/cross_lingual_similarity_with_tf_hub_multilingual_universal_encoder.ipynb | apache-2.0 | # Copyright 2019 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... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_eeg_erp.ipynb | bsd-3-clause | import mne
from mne.datasets import sample
"""
Explanation: EEG processing and Event Related Potentials (ERPs)
For a generic introduction to the computation of ERP and ERF
see tut_epoching_and_averaging. Here we cover the specifics
of EEG, namely:
- setting the reference
- using standard montages :func:`mne.channels.M... |
probml/pyprobml | deprecated/causal_impact_tfp.ipynb | mit | !pip install tfcausalimpact
%matplotlib inline
import sys
import os
# sys.path.append(os.path.abspath('../'))
import numpy as np
import pandas as pd
import tensorflow as tf
import tensorflow_probability as tfp
import matplotlib.pyplot as plt
from causalimpact import CausalImpact
tfd = tfp.distributions
plt.rcPa... |
endlesspint8/endlesspint8.github.io | code/reseen/ta_reviews.ipynb | mit | import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
df_ta_bar_reviews = pd.read_excel("ta_bar_reviews.xlsx")
df_ta_bar_reviews.date = pd.to_datetime(df_ta_bar_reviews.date)
print(df_ta_bar_reviews.dtypes)
df_ta_bar_reviews.head()
def first_... |
unpingco/python_for_prob_stats_ml | chapters/statistics/notebooks/Gauss_Markov.ipynb | mit | from __future__ import division
%pylab inline
"""
Explanation: Python for Probability, Statistics, and Machine Learning
End of explanation
"""
Q = np.eye(3)*0.1 # error covariance matrix
# this is what we are trying estimate
beta = matrix(ones((2,1)))
W = matrix([[1,2],
[2,3],
[1,1]])
"""
E... |
mintcloud/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... |
ES-DOC/esdoc-jupyterhub | notebooks/inm/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', 'inm', 'sandbox-1', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: INM
Source ID: SANDBOX-1
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties: 6... |
Kreiswolke/gensim | docs/notebooks/ldaseqmodel.ipynb | lgpl-2.1 | # setting up our imports
from gensim.models import ldaseqmodel
from gensim.corpora import Dictionary, bleicorpus
import numpy
from gensim.matutils import hellinger
"""
Explanation: Dynamic Topic Models Tutorial
What is this tutorial about?
This tutorial will exaplin what Dynamic Topic Models are, and how to use them ... |
dipanjank/ml | simple_implementations/Maximum_Likelihood_Estimation.ipynb | gpl-3.0 | %pylab inline
pylab.style.use('ggplot')
import networkx as nx
G = nx.DiGraph()
G.add_edges_from([('A', 'B'), ('A', 'C'), ('A', 'D')])
pos = {'A': (2, 2), 'B': (1, 1), 'C': (2, 1), 'D':(4, 1)}
nx.draw(G, pos, node_size=2000, node_color='lightblue')
labels = {'A': '$\\theta$', 'B': '$X[1]$', 'C': '$X[2]$', 'D': '$X[n... |
donlnz/nonconformist | README.ipynb | mit | from sklearn.datasets import load_iris
import numpy as np
from sklearn.svm import SVC
from nonconformist.cp import IcpClassifier
from nonconformist.nc import NcFactory
iris = load_iris()
idx = np.random.permutation(iris.target.size)
# Divide the data into proper training set, calibration set and test set
idx_trai... |
liganega/Gongsu-DataSci | previous/y2017/W12-pandas-intro2/GongSu26_Pandas_Introduction_2.ipynb | gpl-3.0 | from GongSu24_Pandas_Introduction_1 import *
"""
Explanation: Pandas 소개 2
GonsSu24 내용에 이어서 Pandas 라이브러리를 소개한다.
먼저 GongSu24를 임포트 한다.
End of explanation
"""
s6 = Series(range(3), index=['a', 'b', 'c'])
s6
"""
Explanation: 색인(Index) 클래스
Pandas에 정의된 색인(Index) 클래스는 Series와 DataFrame 자료형의 행과 열을 구분하는 이름들의 목록을 저장하는 데에 사용된... |
google/iree | samples/colab/tflite_text_classification.ipynb | apache-2.0 | %%capture
!python -m pip install iree-compiler iree-runtime iree-tools-tflite -f https://github.com/google/iree/releases/latest
!pip3 install --extra-index-url https://google-coral.github.io/py-repo/ tflite_runtime
import numpy as np
import urllib.request
import pathlib
import tempfile
import re
import tflite_runtime.... |
albahnsen/ML_SecurityInformatics | notebooks/06-data_preparation_evaluation.ipynb | mit | import pandas as pd
import zipfile
with zipfile.ZipFile('../datasets/titanic.csv.zip', 'r') as z:
f = z.open('titanic.csv')
titanic = pd.read_csv(f, sep=',', index_col=0)
titanic.head()
# check for missing values
titanic.isnull().sum()
"""
Explanation: 06 - Data Preparation and Advanced Model Evaluation
by Al... |
ga7g08/ga7g08.github.io | _notebooks/2015-02-23-Clustering-Google-location-data.ipynb | mit | # Create a dataframe from the json file in the filepath
raw = pd.io.json.read_json('LocationHistory.json')
df = raw['locations'].apply(pd.Series)
# Create a list from the latitude column, multiplied by -E7
df['latitude'] = df['latitudeE7'] * 1e-7
# Create a list from the longitude column, multiplied by -E7
df['lon... |
phoebe-project/phoebe2-docs | 2.2/tutorials/contact_binary_hierarchy.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Contact Binary Hierarchy
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
"""
%matplotlib inl... |
PyPSA/PyPSA | examples/notebooks/lopf_with_pyomo_False.ipynb | mit | import pypsa
import pandas as pd
import os
n = pypsa.examples.ac_dc_meshed(from_master=True)
"""
Explanation: Optimization without pyomo
In this example we demonstrate the behaviour of the Linear Optimal Power Flow (LOPF) calculation without using pyomo. This requires to set pyomo to False in the lopf function. Then,... |
ubcgif/gpgTutorials | notebooks/mag/Mag_Dipole.ipynb | mit | # define a dipole
dipoleloc = (0.,0.,-50.)
dipoleL = 100.
dipoledec, dipoleinc = 0., 90.
dipolemoment = 1e13
"""
Explanation: Define a magnetic dipole
A dipole is defined in the section below using
* dipoleloc: x, y, z location of the dipole center
* dipoledec: declination of the dipole's direction in degree; north = ... |
softEcon/course | lectures/economic_models/generalized_roy/notebook/lecture.ipynb | mit | %%capture
# Notebook metods
from IPython.core.display import HTML, Image
# Unix Pattern Extensions
import glob
# Operating System Interfaces
import os
# Lexical Analysis
import shlex
# Copy operations
import copy
# Encoders and Decoders
import codecs
# Statistical Modeling and Econometrics
import statsmodels.ap... |
AI-Innovation/cs231n_ass1 | two_layer_net.ipynb | mit | # A bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.neural_net import TwoLayerNet
%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-reloadi... |
sdpython/ensae_teaching_cs | _doc/notebooks/sklearn_ensae_course/02_basic_of_machine_learning_with_scikit-learn.ipynb | mit | %matplotlib inline
import numpy as np
from matplotlib import pyplot as plt
"""
Explanation: 2A.ML101.2: Basic principles of machine learning with scikit-learn
Classification and regression.
Source: Course on machine learning with scikit-learn by Gaël Varoquaux
Introducing the scikit-learn estimator object
End of expla... |
CompPhysics/MachineLearning | doc/Programs/IsingModel/onedim.ipynb | cc0-1.0 | import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import seaborn as sns
import scipy.linalg as scl
from sklearn.model_selection import train_test_split
import sklearn.linear_model as skl
import tqdm
%matplotlib inline
sns.set(color_codes=True)
cmap_args=dict(vm... |
astarostin/MachineLearningSpecializationCoursera | course4/week2 - Непараметрические критерии - quiz.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import itertools
from scipy import stats
from statsmodels.stats.descriptivestats import sign_test
from statsmodels.stats.weightstats import zconfint
%pylab inline
"""
Explanation: 4.
Давайте вернёмся к данным выживаемости пациентов с лейкоцитарной лимфомой из видео про критерий ... |
DrMud/SentimentHackPad | Sentiment Hackpad.ipynb | gpl-3.0 | !pip install textblob
from __future__ import unicode_literals, print_function
import textblob
import pandas as pd
import numpy as np
"""
Explanation: <h1> Sentiment Hackpad </h1>
Authors:
Daniela Huppenkothen, Phil Marshall, Madhura Killedar
We did some natural language processing by performing sentiment analysis on... |
BillyLjm/CS100.1x.__CS190.1x | lab0_student.ipynb | mit | # Check that Spark is working
largeRange = sc.parallelize(xrange(100000))
reduceTest = largeRange.reduce(lambda a, b: a + b)
filterReduceTest = largeRange.filter(lambda x: x % 7 == 0).sum()
print reduceTest
print filterReduceTest
# If the Spark jobs don't work properly these will raise an AssertionError
assert reduce... |
xunzhang/dynet | examples/jupyter-tutorials/tutorial-1-xor.ipynb | apache-2.0 | # we assume that we have the dynet module in your path.
# OUTDATED: we also assume that LD_LIBRARY_PATH includes a pointer to where libcnn_shared.so is.
import dynet as dy
# create a parameter collection and add the parameters.
m = dy.ParameterCollection()
pW = m.add_parameters((8,2))
pV = m.add_parameters((1,8))
pb =... |
UWashington-Astro300/Astro300-A17 | Python_OO.ipynb | mit | import numpy as np
from astropy import units as u
class SpaceRock(object):
def __init__(self, name=None, ab_mag=None, albedo=None):
self.name = name
self.ab_mag = ab_mag
self.albedo = albedo
# Create some fake data:
my_name = "Geralt of Rivia"
my_ab_mag = 5.13
my_albedo = 0.1... |
info-370/classification | logistic-regression/demo.ipynb | mit | # Set up
import numpy as np
from __future__ import division # division
import pandas as pd
import seaborn as sns # for visualiation
from scipy.stats import ttest_ind # t-tests
import statsmodels.formula.api as smf # linear modeling
import statsmodels.api as sm
import matplotlib.pyplot as plt # plotting
import matplotli... |
jmschrei/pomegranate | tutorials/B_Model_Tutorial_1_Distributions.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn; seaborn.set_style('whitegrid')
import numpy
from pomegranate import *
numpy.random.seed(0)
numpy.set_printoptions(suppress=True)
%load_ext watermark
%watermark -m -n -p numpy,scipy,pomegranate
"""
Explanation: Probability Distributions
author: Jacob... |
LeonhardFS/flightbbq | 03_DataExploration.ipynb | mit | import pandas as pd
import matplotlib
import numpy as np
matplotlib.style.use('fivethirtyeight')
%matplotlib inline
%pylab inline
pylab.rcParams['figure.figsize'] = (10, 6)
"""
Explanation: Data Exploration
End of explanation
"""
complete2014Data = pd.DataFrame.from_csv('cache/complete2014Data.csv')
"""
Explanation... |
machinelearningnanodegree/stanford-cs231 | solutions/levin/assignment1/features.ipynb | mit | import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
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 extenrnal modu... |
cjcopi/healpy | doc/healpy_tutorial.ipynb | gpl-2.0 | import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import healpy as hp
"""
Explanation: healpy tutorial
See the Jupyter Notebook version of this tutorial at https://github.com/healpy/healpy/blob/master/doc/healpy_tutorial.ipynb
See a executed version of the notebook with embedded plots at https://... |
AEW2015/PYNQ_PR_Overlay | Pynq-Z1/notebooks/examples/pmod_tc1.ipynb | bsd-3-clause | from pynq import Overlay
Overlay("base.bit").download()
from pynq.iop import Pmod_TC1
from pynq.iop import PMODB
# TC1 sensor is on PMODB
my_tc1 = Pmod_TC1(PMODB)
r = my_tc1.read()
print('Raw Register Value: %08x hex' % r)
print('Ref Junction Temp: %.4f' % my_tc1.reg_to_ref(r))
print('Thermocouple Temp: %.2f' % my_... |
AllenDowney/ThinkStats2 | solutions/chap06soln.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... |
gcgruen/homework | data-databases-homework/Homework_6_Gruen_Graded.ipynb | mit | import requests
data = requests.get('http://localhost:5000/lakes').json()
print(len(data), "lakes")
for item in data[:10]:
print(item['name'], "- elevation:", item['elevation'], "m / area:", item['area'], "km^2 / type:", item['type'])
"""
Explanation: graded = 8/10
Homework 6: Web Applications
For this homework, y... |
sorig/shogun | doc/ipython-notebooks/evaluation/xval_modelselection.ipynb | bsd-3-clause | %pylab inline
%matplotlib inline
# include all Shogun classes
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from shogun import *
# generate some ultra easy training data
gray()
n=20
title('Toy data for binary classification')
X=hstack((randn(2,n), randn(2,n)+1))
Y=hstack((-ones(n), ones(n)))
_... |
eds-uga/csci1360e-su17 | assignments/A6/A6_Q1.ipynb | mit | u1 = ["green", "green", "blue", "green"]
a1 = set({("green", 3), ("blue", 1)})
assert a1 == set(urn_to_dict(u1).items())
u2 = ["red", "blue", "blue", "green", "yellow", "black", "black", "green", "blue", "yellow", "red", "green", "blue", "black", "yellow", "yellow", "yellow", "green", "blue", "red", "red", "blue", "re... |
DavidObando/carnd | Term1/Labs/CarND-LeNet-Lab/LeNet-Lab-Solution.ipynb | apache-2.0 | from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", reshape=False)
X_train, y_train = mnist.train.images, mnist.train.labels
X_validation, y_validation = mnist.validation.images, mnist.validation.labels
X_test, y_test = mnist.test.images, mn... |
wanderer2/pymc3 | docs/source/notebooks/GLM-hierarchical-ADVI.ipynb | apache-2.0 | %matplotlib inline
import sys, os
sys.path.insert(0, os.path.expanduser('~/work/git/github/taku-y/pymc3'))
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('../data/radon.csv')
county_names = data.county.unique... |
mined-gatech/pymks_overview | notebooks/python_intro.ipynb | mit | log10(10)
"""
Explanation: Quick Intro to Python
Math and Modules
In the space below and use Python as a calculator.
Now let's try some more advanced funtions.
End of explanation
"""
import math
math.log10(10)
"""
Explanation: The standard Python distribution only comes with the bare-bone capabilities. Other funct... |
rjleveque/binder_experiments | misc/ipyleaflet_polygon_selector.ipynb | bsd-2-clause | from IPython.display import display
import ipywidgets
from __future__ import print_function
import ipyleaflet
from ipyleaflet import (
Map,
Marker,
TileLayer, ImageOverlay,
Polyline, Polygon, Rectangle, Circle, CircleMarker,
GeoJSON,
DrawControl
)
"""
Explanation: Tool to draw polygons on ma... |
BeatHubmann/17F-U-DLND | tv-script-generation/dlnd_tv_script_generation.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... |
ES-DOC/esdoc-jupyterhub | notebooks/inpe/cmip6/models/sandbox-3/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: INPE
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Transport, Emissions, Conce... |
tpin3694/tpin3694.github.io | python/create_list_from_dictionary_keys_and_values.ipynb | mit | dict = {'county': ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', 'Yuma'],
'year': [2012, 2012, 2013, 2014, 2014],
'fireReports': [4, 24, 31, 2, 3]}
"""
Explanation: Title: Creating Lists From Dictionary Keys And Values
Slug: create_list_from_dictionary_keys_and_values
Summary: Creating Lists From Dict... |
cmshobe/dem_analysis_with_gdal | dem_processing_with_gdal_python.ipynb | mit | from osgeo import gdal
import numpy as np
"""
Explanation: Short introduction to working with DEMs in Python GDAL
Greg Tucker, CU Boulder, Feb 2016
Install GDAL library
You'll need to install the GDAL library. If you have Anaconda installed, you can do this from the command line by:
conda install gdal
Download some DE... |
pligor/predicting-future-product-prices | dfa/notebook/dfa_simulations.ipynb | agpl-3.0 | import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import scipy.signal as ss
import scipy.stats as st
plt.style.use('ggplot')
%matplotlib inline
"""
Explanation: DFA robustness simulations
Dominik Krzemiński
End of explanation
"""
def calc_rms(x, scale):
"""
Root Mean Square in windows wit... |
rubensfernando/mba-analytics-big-data | Python/2016-08-08/aula7-parte4-wikipedia.ipynb | mit | import requests
req = requests.get("https://pt.wikipedia.org/wiki/Unidades_federativas_do_Brasil")
from bs4 import BeautifulSoup
bs = BeautifulSoup(req.text, "html.parser")
print(bs.prettify())
print(bs.h1.string)
bs.h1
"""
Explanation: Recuperar informações da Wikipedia
End of explanation
"""
bs.a
"""
Explan... |
RoebideBruijn/datascience-intensive-course | exercises/statistics project 3/sliderule_dsi_inferential_statistics_exercise_3.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import bokeh.plotting as bkp
import seaborn as sns
from mpl_toolkits.axes_grid1 import make_axes_locatable
%matplotlib inline
sns.set_style('white')
# read in readmissions data provided
hospital_read_df = pd.read_csv('data/cms_hospital_readmissio... |
vatsalgit/Deep-Learning- | assignment2/Problem_3.ipynb | gpl-3.0 | # Import what we need
import os
import sys
import numpy as np
import scipy.io
import scipy.misc
import tensorflow as tf # Import TensorFlow after Scipy or Scipy will break
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
from PIL import Image
%matplotlib inline
"""
Explanation: [BONUS] Problem 3: ... |
naoyak/Agile_Data_Code_2 | ch09/Debugging Prediction Problems.ipynb | mit | APP_NAME = "Debugging Prediction Problems"
# If there is no SparkSession, create the environment
try:
sc and spark
except NameError as e:
import findspark
findspark.init()
import pyspark
import pyspark.sql
sc = pyspark.SparkContext()
spark = pyspark.sql.SparkSession(sc).builder.appName(APP_NAME).getOrCr... |
CamDavidsonPilon/lifelines | docs/jupyter_notebooks/Modelling time-lagged conversion rates.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
from matplotlib import pyplot as plt
import autograd.numpy as np
from autograd.scipy.special import expit, logit
import pandas as pd
plt.style.use('bmh')
N = 200
U = np.random.rand(N)
T = -(logit(-np.log(U) / 0.5) - np.random.exponential(2, N) - 6.00)... |
rachellevanger/tda-persistence-explorer | doc/Tutorial.ipynb | mit | from PersistenceExplorer import *
"""
Explanation: Persistence Explorer Tutorial
This Jupyter notebook will show you how to get up and running with the tda-d3-explorer tool. If you haven't installed the tool yet, please follow the instructions listed on the readme of the tda-d3-explorer repository.
End of explanation
... |
vicente-gonzalez-ruiz/YAPT | 01-hello_world/01-intro.ipynb | cc0-1.0 | # %load quicksort.c
// http://www.comp.dit.ie/rlawlor/Alg_DS/sorting/quickSort.c
#include <stdio.h>
void quickSort( int[], int, int);
int partition( int[], int, int);
void main()
{
int a[] = { 7, 12, 1, -2, 0, 15, 4, 11, 9};
int i;
printf("\n\nUnsorted array is: ");
for(i = 0; i < 9; ++i)
... |
texib/deeplearning_homework | tensor-flow-exercises/1_notmnist.ipynb | mit | print 'xxxx'
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
import matplotlib.pyplot as plt
import numpy as np
import os
import tarfile
import urllib
from IPython.display import display, Image
from scipy import ndimage
from sklearn.linear_model import Logis... |
ML4DS/ML4all | C4.Classification_SVM/svm_professor.ipynb | mit | from __future__ import print_function
# To visualize plots in the notebook
%matplotlib inline
# Imported libraries
#import csv
#import random
#import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
#import pylab
import numpy as np
#from sklearn.preprocessing import PolynomialFeatures
from... |
elenduuche/deep-learning | image-classification/.ipynb_checkpoints/dlnd_image_classification-checkpoint.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'
class DLProgress(tqdm):
last_block = 0
def hoo... |
dfm/dfm.io | static/downloads/notebooks/autocorr.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = "retina"
from __future__ import print_function
from matplotlib import rcParams
rcParams["savefig.dpi"] = 100
rcParams["figure.dpi"] = 100
rcParams["font.size"] = 20
"""
Explanation: Title: Autocorrelation time estimation
Date: 2017-10-16
Category: Data Analysi... |
geektoni/shogun | doc/ipython-notebooks/classification/SupportVectorMachines.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
import matplotlib.patches as patches
import shogun as sg
import numpy as np
%matplotlib inline
#Generate some random data
X = 2 * np.random.randn(10,2)
traindata=np.r_[X + 3, X + 7].T
feats_train=sg.create_feature... |
mmckerns/tutmom | mystic.ipynb | bsd-3-clause | %matplotlib notebook
"""
Explanation: Optimiztion with mystic
End of explanation
"""
"""
Example:
- Minimize Rosenbrock's Function with Nelder-Mead.
- Plot of parameter convergence to function minimum.
Demonstrates:
- standard models
- minimal solver interface
- parameter trajectories using reta... |
JoseGuzman/myIPythonNotebooks | Stochastic_systems/Normal_distribution.ipynb | gpl-2.0 | # create a normally distributed random variable with mu and sigma
mu = 28.74
sigma = 8.33 # standard deviation!
rv_X = norm(loc = mu, scale = sigma)
# plot the theoretical and empirical distributions
x = np.linspace(start = rv_X.ppf(0.001), stop = rv_X.ppf(0.999), num = 100)
plt.plot(x, rv_X.pdf(x), color = 'r', lw=2,... |
MBARIMike/biofloat | notebooks/explore_cached_biofloat_data.ipynb | mit | from biofloat import ArgoData
ad = ArgoData()
"""
Explanation: Explore locally cached Argo oxygen float data - second in a series of Notebooks
Use the biofloat module to get data and Pandas to operate on it for testing ability to easily perform calibrations
(See build_biofloat_cache.ipynb for for the work that leads t... |
myfunprograms/deep_learning | project2/files/dlnd_image_classification_instruction.ipynb | gpl-3.0 | """
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
cifar10_dataset_folder_path = 'cifar-10-batches-py'
class DLProgress(tqdm):
last_block = 0
def hoo... |
lmmentel/xrd-to-heatmap | notebooks/csv_to_heatmap.ipynb | mit | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
def create_matrix(filename, colnum, rank, sep=':', savecsv=False):
df = pd.read_csv(filename, sep=sep, header=None, comment='#')
matrix = df.iloc[:, [colnum]].values.reshape... |
JanetMatsen/Machine_Learning_CSE_546 | HW3/notebooks/Q-5_Run_K-Means.ipynb | mit | km_16 = KMeans(k=16, train_X=X_train, train_y=y_train,
pca_obj=pca_training,
max_iter = 500,
test_X=X_test, test_y=y_test,
verbose=False)
km_16.run()
"""
Explanation: Q 5.1:
k = 16, MNIST data transformed by first 50 PCA components.
End of explanation
"""
km_16_recon... |
CalPolyPat/phys202-2015-work | days/day10/Interpolation.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
"""
Explanation: Interpolation
Learning Objective: Learn to interpolate 1d and 2d datasets of structured and unstructured points using SciPy.
End of explanation
"""
x = np.linspace(0,4*np.pi,10)
x
"""
Explanation: Overview
W... |
probml/pyprobml | deprecated/advi_beta_binom_jax.ipynb | mit | try:
import jax
except ModuleNotFoundError:
%pip install -qqq jax jaxlib
import jax
import jax.numpy as jnp
from jax import lax
try:
from tensorflow_probability.substrates import jax as tfp
except ModuleNotFoundError:
%pip install -qqq tensorflow_probability
from tensorflow_probability.substra... |
pmgbergen/porepy | tutorials/compressible_flow_with_automatic_differentiation.ipynb | gpl-3.0 | import numpy as np
import scipy.sparse as sps
import matplotlib.pyplot as plt
# Porepy modules
import porepy as pp
"""
Explanation: Introduction
This notebook gives suggests how to solve the problem of non-linear compressible flow using the automatic differentiation library included in PorePy.
The Ad functionality i... |
GoogleCloudPlatform/ml-design-patterns | 04_hacking_training_loop/transfer_learning.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_hub as hub
import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow.keras import Sequential
"""
Explanation: Transfer Learning Design Pattern
In Transfer Learning, we take part of a previously trained m... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_read_inverse.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator
print(__doc__)
data_path = sample.data_path()
fname = data_path
fname += '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
inv = read_... |
vzg100/Post-Translational-Modification-Prediction | old/Phosphorylation Sequence Tests -XGB -dbptm+ELM -EnzymeBenchmarks-VectorAvr..ipynb | mit | from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
"""
Explanation: Template for test
End of explanation
"""
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
benchmarks = ["Data/Benchmarks/phos_CDK1.csv", "Data/Benchmarks/phos_CK2.csv", "Data/B... |
google/starthinker | colabs/dcm_api_to_bigquery.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: 1. Install Dependencies
First install the libraries needed to execute recipes, this only needs to be done once, then click play.
End of explanation
"""
CLOUD_PROJECT = 'PASTE PROJECT ID HERE'
print("Cloud Project Set To: %s" % CLOUD_PROJECT)
... |
tensorflow/model-optimization | tensorflow_model_optimization/g3doc/guide/quantization/training_example.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... |
statsmodels/statsmodels.github.io | v0.12.2/examples/notebooks/generated/theta-model.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import pandas_datareader as pdr
import matplotlib.pyplot as plt
import seaborn as sns
plt.rc("figure",figsize=(16,8))
plt.rc("font",size=15)
plt.rc("lines",linewidth=3)
sns.set_style("darkgrid")
"""
Explanation: The Theta Model
The Theta model of Assimakopoulos & Nikolopoulos (20... |
dbrattli/RxPY | notebooks/Getting Started.ipynb | apache-2.0 | %%bash
pip install rx
"""
Explanation: Getting Started with RxPY
ReactiveX, or Rx for short, is an API for programming with observable event streams. RxPY is a port of ReactiveX to Python. Learning Rx with Python is particularly interesting since Python removes much of the clutter that comes with statically typed lang... |
gmonce/datascience | src/Just some notes on PCA in Python.ipynb | gpl-3.0 | import numpy as np
%matplotlib inline
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d import proj3d
np.set_printoptions(suppress=True,precision=3)
from matplotlib.patches import FancyArrowPatch
"""
Explanation: Just some notes on PCA in Python
Guillermo Moncecchi
... |
ComputationalModeling/spring-2017-danielak | past-semesters/fall_2016/homework/HW3/Homework_3.ipynb | agpl-3.0 | # put your code here
"""
Explanation: Homework #3
This notebook is due on Friday, October 28th, 2016 at 11:59 p.m.. Please make sure to get started early, and come by the instructors' office hours if you have any questions. Office hours and locations can be found in the course syllabus. IMPORTANT: While it's fine... |
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn | doc/notebooks/automaton.is_isomorphic.ipynb | gpl-3.0 | import vcsn
def aut(e):
return vcsn.context('lal_char, b').expression(e, 'binary').standard()
a1 = aut('a*+b*'); a1
a2 = aut('b*+a*'); a2
a1.is_isomorphic(a2), a1 == a2
"""
Explanation: automaton.is_isomorphic(aut)
Whether this automaton isomorphic to aut, i.e., whether they are "the same graph."
Preconditions:... |
minh-doan/deepometry | STEP_4b_Test_and_Visualization_prebuilt_CNN.ipynb | bsd-3-clause | # Location of digested data
input_directory = '/digested/'
# Desired location to save temporary PNG outputs:
png_directory = '/digested_png/'
# Location of saved trained model
model_directory = '/model_directory/'
# Desired location for outputs
output_directory = '/output_directory_transferred/'
# Define native sha... |
mohanprasath/Course-Work | numpy/numpy_exercises_from_kyubyong/Array_manipulation_routines_Solutions.ipynb | gpl-3.0 | import numpy as np
np.__version__
"""
Explanation: Array manipulation routines
End of explanation
"""
x = np.ones([10, 10, 3])
out = np.reshape(x, [-1, 150])
print out
assert np.allclose(out, np.ones([10, 10, 3]).reshape([-1, 150]))
"""
Explanation: Q1. Let x be a ndarray [10, 10, 3] with all elements set to one. ... |
do-mpc/do-mpc | documentation/source/getting_started.ipynb | lgpl-3.0 | import numpy as np
# Add do_mpc to path. This is not necessary if it was installed via pip.
import sys
sys.path.append('../../')
# Import do_mpc package:
import do_mpc
"""
Explanation: Getting started: MPC
In this Jupyter Notebook we illustrate the core functionalities of do-mpc.
Open an interactive online Jupyter N... |
chrisfilo/fmri-analysis-vm | analysis/firstlevel/First level modeling.ipynb | mit | import nipype.algorithms.modelgen as model # model generation
import nipype.interfaces.fsl as fsl # fsl
from nipype.interfaces.base import Bunch
import os,json,glob
import numpy
import nibabel
import nilearn.plotting
from make_event_files_from_json import MakeEventFilesFromJSON
%matplotlib inline
import mat... |
banyh/ShareIPythonNotebook | NLP_With_Python/Ch2.ipynb | gpl-3.0 | import nltk
nltk.corpus.gutenberg.fileids()
"""
Explanation: Chapter 2: 取得Corpora及Lexical資源
2.1 文字型的Corpus
Gutenberg Corpus
古騰堡計畫是由志工參與，將文學資料電子化的計畫。古騰堡是活字印刷的發明人，以他為名表示希望知識的自由流通。在Gutenberg Corpus中有部分的文學作品，可以用nltk.corpus.gutenberg.fileids()查詢。
End of explanation
"""
emma = nltk.corpus.gutenberg.words('austen-emma.txt'... |
nholtz/structural-analysis | theory/frame2d/01-Derivation-Local-Stiffness.ipynb | cc0-1.0 | from sympy import *
init_printing(use_latex='mathjax')
from sympy.matrices import Matrix
"""
Explanation: Plane Frame Member Local Stiffness Matrix
This page develops the various member stiffness relationships needed to analyze a planar frame by the matrix stiffness method.
Numbering and Sign Convention, Local Coordin... |
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