repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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akseshina/dl_course | seminar_6/hw_tSNE.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from sklearn.manifold import TSNE
family_classification_metadata = pd.read_table('../seminar_5/data/family_classification_... |
phoebe-project/phoebe2-docs | 2.2/examples/extinction_BK_binary.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Extinction: B-K Binary
In this example, we'll reproduce Figures 1 and 2 in the extinction release paper (Jones et al. 2020).
"Let us begin with a rather extreme case, a synthetic binary comprised of a hot, B-type main sequence star(M=6.5 Msol,Teff=17000 K, ... |
kmclaugh/fastai_courses | kevin_files/lesson4.ipynb | apache-2.0 | ratings = pd.read_csv(path+'ratings.csv')
ratings.head()
len(ratings)
"""
Explanation: Set up data
We're working with the movielens data, which contains one rating per row, like this:
End of explanation
"""
movie_names = pd.read_csv(path+'movies.csv').set_index('movieId')['title'].to_dict()
users = ratings.userId.... |
HuanglabPurdue/NCS | clib/jupyter_notebooks/ncs_demo_simulation.ipynb | gpl-3.0 | import matplotlib
import matplotlib.pyplot as pyplot
import numpy
import os
import time
# python3-6 NCS.
import pyNCS
import pyNCS.denoisetools as ncs
# python3 and C NCS.
import pyCNCS.ncs_c as ncsC
# Generate the same random noise each time.
numpy.random.seed(1)
py_ncs_path = os.path.dirname(os.path.abspath(pyNC... |
rkastilani/PowerOutagePredictor | PowerOutagePredictor/Linear/Lasso.ipynb | mit | import numpy as np
import pandas as pd
from sklearn import linear_model
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
data = pd.read_csv("../../Data/2014outagesJerry.csv")
data.head()
"""
Explanation: Lasso Regression:
Performs L1 regularization, i.e. adds penalty equivalent to abs... |
iRipVanWinkle/ml | Data Science UA - September 2017/Lecture 05 - Modeling Techniques and Regression/Preparing Numeric Data.ipynb | mit | # This line lets me show plots
%matplotlib inline
#import useful modules
import numpy as np
import pandas as pd
from ggplot import mtcars
"""
Explanation: Preparing Numeric Data
There are variety of preprocessing tasks one should consider before using numeric data in analysis and predict... |
quantopian/research_public | research/Markowitz-Quantopian-Research.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import cvxopt as opt
from cvxopt import blas, solvers
import pandas as pd
np.random.seed(123)
# Turn off progress printing
solvers.options['show_progress'] = False
"""
Explanation: The Efficient Frontier: Markowitz Portfolio optimization in Python
By Dr. Thomas Sta... |
palrogg/foundations-homework | 14/Homework-14-Ronga.ipynb | mit | # If you'd like to download it through the command line...
!curl -O http://www.cs.cornell.edu/home/llee/data/convote/convote_v1.1.tar.gz
# And then extract it through the command line...
!tar -zxf convote_v1.1.tar.gz
"""
Explanation: Homework 14 (or so): TF-IDF text analysis and clustering
Hooray, we kind of figured ... |
tkurfurst/deep-learning | batch-norm/Batch_Normalization_Lesson.ipynb | mit | # Import necessary packages
import tensorflow as tf
import tqdm
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Import MNIST data so we have something for our experiments
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
"... |
yttty/python3-scraper-tutorial | Python_Spider_Tutorial_03.ipynb | gpl-3.0 | import re
import urllib.request
import urllib
from collections import deque
queue = deque()
visited = set()
url = 'http://news.dbanotes.net' # 入口页面, 可以换成别的
queue.append(url)
cnt = 0
while queue:
url = queue.popleft() # 队首元素出队
visited |= {url} # 标记为已访问
print('已经抓取: ' + str(cnt) + ' 正在抓取 <--- ' + ... |
GEMScienceTools/rmtk | notebooks/vulnerability/derivation_fragility/hybrid_methods/N2/N2.ipynb | agpl-3.0 | from rmtk.vulnerability.derivation_fragility.hybrid_methods.N2 import N2Method
from rmtk.vulnerability.common import utils
%matplotlib inline
"""
Explanation: N2 - Eurocode 8, CEN (2005)
This simplified nonlinear procedure for the estimation of the seismic response of structures uses capacity curves and inelastic spe... |
AshtonIzmev/deep-learning-python-snippets | python/notebook/keras-digits.ipynb | mit | # Here's a Deep Dumb MLP (DDMLP)
model = Sequential()
model.add(Dense(input_dim, 128, init='lecun_uniform'))
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(Dense(128, 128, init='lecun_uniform'))
model.add(Activation('relu'))
model.add(Dropout(0.25))
model.add(Dense(128, nb_classes, init='lecun_uniform... |
oseledets/talks-online | kaiserslautern-2018/lecture-1.ipynb | cc0-1.0 | import numpy as np
import matplotlib.pyplot as plt
from numpy.polynomial import Chebyshev as T
from numpy.polynomial.hermite import hermval
%matplotlib inline
def p_cheb(x, n):
"""
RETURNS T_n(x)
value of not normalized Chebyshev polynomials
$\int \frac1{\sqrt{1-x^2}}T_m(x)T_n(x) dx = \frac\pi2\delta_{... |
diegocavalca/Studies | programming/Python/tensorflow/exercises/Graph.ipynb | cc0-1.0 | # Q1. Create a graph
g = ...
with g.as_default():
# Define inputs
with tf.name_scope("inputs"):
a = tf.constant(2, tf.int32, name="a")
b = tf.constant(3, tf.int32, name="b")
# Ops
with tf.name_scope("ops"):
c = tf.multiply(a, b, name="c")
d = tf.add(a, b, name="d")
... |
tjwei/HackNTU_Data_2017 | Week11/DIY_AI/Softmax.ipynb | mit | # Weight
W = Matrix([1,2],[3,4], [5,6])
W
# Bias
b = Vector(1,0,-1)
b
# 輸入
x = Vector(2,-1)
x
"""
Explanation: Supervised learning for classification
給一堆 $x$, 和他的分類，我們找出計算 x 的分類的方式
One hot encoding
如果我們有三類種類別， 我們可以來編碼這三個類別
* $(1,0,0)$
* $(0,1,0)$
* $(0,0,1)$
問題
為什麼不直接用 1,2,3 這樣的編碼呢？
Softmax Regression 的模型是這樣的
我們的輸... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a_soft/td1a_cython_edit.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.soft - Calcul numérique et Cython
Python est très lent. Il est possible d'écrire certains parties en C mais le dialogue entre les deux langages est fastidieux. Cython propose un mélange de C et Python qui accélère la conception.
End of... |
tensorflow/docs-l10n | site/ja/tensorboard/get_started.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... |
Oli4/lsi-material | Foundations of Information Management/Sheet 3 - RA operators in SQL and SQL queries.ipynb | mit | import sqlite3
conn = sqlite3.connect('movie.db')
cur = conn.cursor()
"""
Explanation: Questions 1
(RA operators in SQL). Transform the following relational algebra expressions from the first exercise sheet into equivalent SQL queries.
Question 2
End of explanation
"""
cur.execute('''SELECT genre
... |
namiszh/fba | notebooks/yahoo-api-example.ipynb | mit | from rauth import OAuth2Service
import webbrowser
import json
"""
Explanation: Yahoo API Example
This notebook is an example of using yahoo api to get fantasy sports data.
End of explanation
"""
clientId= "dj0yJmk9M3gzSWJZYzFmTWZtJmQ9WVdrOU9YcGxTMHB4TXpnbWNHbzlNQS0tJnM9Y29uc3VtZXJzZWNyZXQmeD1kZg--"
clinetSecrect="db... |
azubiolo/itstep | it_step/ml_from_scratch/4_least-squares_continued/least-squares.ipynb | mit | X = [[1., 50.], [1., 76.], [1., 26.], [1., 102.]]
Y = [30., 48., 12., 90.]
# Y[3] = 300 # Outlier. Uncomment this line if you want to introduce an outlier.
"""
Explanation: Ordinary Least Squares -- Part II
Course recap
This lab consists in implementing the Ordinary Least Squares (OLS) algorithm, which is a linear re... |
atcemgil/notes | LogisticRegression.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib as mpl
import matplotlib.pylab as plt
from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets
from IPython.display import clear_output, display, HTML
from matplotlib import rc
import scipy as sc
import scipy.optimize as opt
mpl.rc('font... |
cloudmesh/book | notebooks/scikit-learn/scikit-learn-k-means.ipynb | apache-2.0 | ! pip install numpy
! pip install scipy -U
! pip install -U scikit-learn
"""
Explanation: Instalation
Source: ...
Scikit-learn requires:
Python (>= 2.6 or >= 3.3),
NumPy (>= 1.6.1),
SciPy (>= 0.9).
If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is ... |
aattaran/Machine-Learning-with-Python | titanic/titanic_survival_exploration[1].ipynb | bsd-3-clause | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the dataset
in_file... |
eggie5/ipython-notebooks | avengers/Avengers.ipynb | mit | import pandas as pd
avengers = pd.read_csv("avengers.csv")
avengers.head(5)
"""
Explanation: Avengers Data
You can also see this notebook rendered on github: https://github.com/eggie5/ipython-notebooks/blob/master/avengers/Avengers.ipynb
Life and Death of the Avengers
The Avengers are a well-known and widely loved te... |
dedx/cpalice | training/05_LinearFits.ipynb | mit | %pylab inline
import numpy as np
import matplotlib.pyplot as plt
#Import the curve fitter from the scipy optimize package
from scipy.optimize import curve_fit
"""
Explanation: 05 Linear fits to some data
When you have a set of data that you would like to fit with some theoretical curve, you can use the SciPy optimize ... |
leoferres/prograUDD | clases/02-Sintaxis-de-Python.ipynb | mit | # set the midpoint
midpoint = 5
# make two empty lists
lower = []; upper = []
# split the numbers into lower and upper
for i in range(10):
if (i < midpoint):
lower.append(i)
else:
upper.append(i)
print("lower:", lower)
print("upper:", upper)
"""
Explanation: Metadata: Estos notebooks... |
tleonhardt/CodingPlayground | dataquest/DataCleaning/Analyzing_NYC_High_School_Data.ipynb | mit | import pandas as pd
import numpy as np
import re
data_files = ["ap_2010.csv",
"class_size.csv",
"demographics.csv",
"graduation.csv",
"hs_directory.csv",
"sat_results.csv"]
data = {}
for f in data_files:
d = pd.read_csv("../data/schools/{0}".fo... |
ceos-seo/data_cube_notebooks | notebooks/water/detection/water_interoperability_similarity.ipynb | apache-2.0 | import sys
import os
sys.path.append(os.environ.get('NOTEBOOK_ROOT'))
%matplotlib inline
import sys
import datacube
import numpy
import numpy as np
import xarray as xr
from xarray.ufuncs import isnan as xr_nan
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: <a id="water_interoperability_similari... |
joekasp/ionic_liquids | ionic_liquids/examples/.ipynb_checkpoints/Example_Workflow-checkpoint.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from rdkit import Chem
from rdkit.Chem import AllChem, Descriptors
from rdkit.ML.Descriptors.MoleculeDescriptors import MolecularDescriptorCalculator as Calculator
"""
Explanation: Example of the... |
xdnian/pyml | assignments/ex01_xdnian.ipynb | mit | # the function
def sort(values):
# insert your code here
for j in range(len(values)-1,0,-1):
for i in range(0, j):
if values[i] > values[i+1]:
values[i], values[i+1] = values[i+1], values[i]
return values
# main
import numpy as np
# different random seed
np.random.seed(... |
Kaggle/learntools | notebooks/embeddings/raw/1-embeddings.ipynb | apache-2.0 | #$HIDE_INPUT$
# Setup. Import libraries and load dataframes for Movielens data.
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import tensorflow as tf
from tensorflow import keras
import os
import random
tf.set_random_seed(1); np.random.seed(1); random.seed(1) # Set random seeds for reprod... |
miguelfrde/stanford-cs231n | assignment2/FullyConnectedNets.ipynb | mit | # As usual, a bit of setup
from __future__ import print_function
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solv... |
palrogg/foundations-homework | Data_and_databases/Homework_2_Paul_Ronga_Graded.ipynb | mit | import pg8000
conn = pg8000.connect(database="homework2")
"""
Explanation: Homework 2: Working with SQL (Data and Databases 2016)
This homework assignment takes the form of an IPython Notebook. There are a number of exercises below, with notebook cells that need to be completed in order to meet particular criteria. Yo... |
RaspberryJamBe/ipython-notebooks | notebooks/en-gb/102 - LEDs - Drive LEDS with the Raspberry Pi GPIO pins.ipynb | cc0-1.0 | #load GPIO library
import RPi.GPIO as GPIO
#Set BCM (Broadcom) mode for the pin numbering
GPIO.setmode(GPIO.BCM)
"""
Explanation: Drive LEDs with the Raspberry Pi GPIO pins
This notebook will walk you through using the Raspberry Pi General Purpose Input/Output (GPIO) pins to make a LED light burn.
The GPIO pins are th... |
epeios-q37/epeios | tools/xdhq/examples/PYH/Hello.ipynb | agpl-3.0 | try:
import atlastk
except:
!pip install atlastk
import atlastk
atlastk.setJupyterHeight("150px") # Adjusting the height of the iframe in which the application will be displayed…
"""
Explanation: If you haven't already done so, please take a look at this FAQ, especially if you run this notebook on Google Colab.... |
pauliacomi/pyGAPS | docs/examples/iast.ipynb | mit | # import isotherms
%run import.ipynb
# import the iast module
import pygaps
import pygaps.iast as pgi
import matplotlib.pyplot as plt
import numpy
"""
Explanation: IAST examples
The IAST method is used to predict the composition of the adsorbed phase in a
multicomponent adsorption system, starting from pure componen... |
probml/pyprobml | notebooks/book2/17/gp_poisson_1d.ipynb | mit | try:
import tinygp
except ImportError:
%pip install -q tinygp
try:
import numpyro
except ImportError:
# It is much faster to use CPU than GPU.
# This is because Colab has multiple CPU cores, so can run the 2 MCMC chains in parallel
%pip uninstall -y jax jaxlib
%pip install -q numpyro jax ja... |
Juanlu001/poliastro | docs/source/examples/Using NEOS package.ipynb | mit | from astropy import time
from poliastro.twobody.orbit import Orbit
from poliastro.bodies import Earth
from poliastro.frames import Planes
from poliastro.plotting import StaticOrbitPlotter
"""
Explanation: Analyzing NEOs
NEO stands for near-Earth object. The Center for NEO Studies (CNEOS) defines NEOs as comets and as... |
d-meiser/cold-atoms | examples/Optimization of Coulomb force evaluation.ipynb | gpl-3.0 | import coldatoms
import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
import time
"""
Explanation: Evaluation of performance of Coulomb force evaluation
In this notebook we have a quick look at the performance of the Coulomb force evalution and our optimizations. The timing results in this notebook ... |
kcyu1993/ML_course_kyu | labs/ex01/npprimer.ipynb | mit | # Useful starting lines
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
%load_ext autoreload
%autoreload 2
"""
Explanation: Welcome to the jupyter notebook! To run any cell, press Shit+Enter or Ctrl+Enter.
IMPORTANT : Please have a look at Help->User Interface Tour and Help->Keyboard Shortc... |
flothesof/LiveFFTPitchTracker | 20150723_inversion.ipynb | bsd-2-clause | data = """79,05 102,40 115,40 126,10 217,50 240,70
82,4 101,5 114,1 123,1 215,8 239
81,90 104,80 113,20 121,50 214,20 237,50"""
data = data.replace(',', '.')
lines = data.split('\n')
values = [line.split('\t') for line in lines]
values
import numpy as np
import pandas as pd
s = pd.DataFrame(values)
s
s.values.... |
yingchi/fastai-notes | deeplearning1/nbs/lesson6.ipynb | apache-2.0 | path = get_file('nietzsche.txt', origin="https://s3.amazonaws.com/text-datasets/nietzsche.txt")
text = open(path).read()
print('corpus length:', len(text))
chars = sorted(list(set(text)))
vocab_size = len(chars)+1
print('total chars:', vocab_size)
"""
Explanation: Setup
We're going to download the collected works of ... |
aaronmckinstry706/twitter-crime-prediction | notebooks/tweets_exploration.ipynb | gpl-3.0 | import os
import sys
# From https://stackoverflow.com/a/36218558 .
def sparkImport(module_name, module_directory):
"""
Convenience function.
Tells the SparkContext sc (must already exist) to load
module module_name on every computational node before
executing an RDD.
Args:
m... |
susantabiswas/Natural-Language-Processing | Notebooks/Word_Prediction_using_Quadgrams_Memory_Efficient_Encoded_keys.ipynb | mit | #import the modules necessary
from nltk.util import ngrams
from collections import defaultdict
from collections import OrderedDict
import nltk
import string
import time
start_time = time.time()
"""
Explanation: Word prediction based on Quadgram
This program reads the corpus line by line so it is slower than the progr... |
Vizzuality/gfw | docs/Update_GFW_Layers_Vault.ipynb | mit | !pip install LMIPy
from IPython.display import clear_output
clear_output()
print('LMI ready!')
"""
Explanation: Create Layer Config Backup
This notebook outlines how to run a process to create a remote backup of gfw layers.
Rough process:
Run this notebook from the gfw/data folder
Wait...
Check _metadata.json files... |
marcotcr/lime | doc/notebooks/Tutorial - MNIST and RF.ipynb | bsd-2-clause | import numpy as np
import matplotlib.pyplot as plt
from skimage.color import gray2rgb, rgb2gray, label2rgb # since the code wants color images
from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784')
# make each image color so lime_image works correctly
X_vec = np.stack([gray2rgb(iimg) for iimg in m... |
jakobrunge/tigramite | tutorials/tigramite_tutorial_assumptions.ipynb | gpl-3.0 | # Imports
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
%matplotlib inline
## use `%matplotlib notebook` for interactive figures
# plt.style.use('ggplot')
import sklearn
import tigramite
from tigramite import data_processing as pp
from tigramite.toymodels import structural_causal_proce... |
tyberion/jupyter_pdf_slides | Example.ipynb | mit | attend = sns.load_dataset("attention").query("subject <= 12")
g = sns.FacetGrid(attend, col="subject", col_wrap=4, size=2, ylim=(0, 10))
g.map(sns.pointplot, "solutions", "score", color=".3", ci=None);
"""
Explanation: Calculate the attention of subjects
Here we calculate the attention of subjects
End of explanation
"... |
RTHMaK/RPGOne | scipy-2017-sklearn-master/notebooks/03 Data Representation for Machine Learning.ipynb | apache-2.0 | from sklearn.datasets import load_iris
iris = load_iris()
"""
Explanation: The use of watermark (above) is optional, and we use it to keep track of the changes while developing the tutorial material. (You can install this IPython extension via "pip install watermark". For more information, please see: https://github.c... |
KaiSzuttor/espresso | doc/tutorials/02-charged_system/02-charged_system-2.ipynb | gpl-3.0 | from espressomd import System, electrostatics, electrostatic_extensions
from espressomd.shapes import Wall
from espressomd.minimize_energy import steepest_descent
import espressomd
import numpy
"""
Explanation: Tutorial 2: A Simple Charged System, Part 2
7 2D Electrostatics and Constraints
In this section, we use the ... |
ledeprogram/algorithms | class6/donow/Gruen_Gianna_6_donow.ipynb | gpl-3.0 | import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import statsmodels.formula.api as smf
"""
Explanation: 1. Import the necessary packages to read in the data, plot, and create a linear regression model
End of explanation
"""
df = pd.read_csv('hanford.csv')
df
"""
Explanation: 2. Read in the han... |
qutip/qutip-notebooks | development/development-ssesolver-new-methods.ipynb | lgpl-3.0 | %matplotlib inline
%config InlineBackend.figure_formats = ['svg']
from qutip import *
from qutip.ui.progressbar import BaseProgressBar
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint
y_sse = None
import time
"""
Explanation: Test for different solvers for stochastic equation
Base... |
statsmodels/statsmodels | examples/notebooks/statespace_tvpvar_mcmc_cfa.ipynb | bsd-3-clause | %matplotlib inline
from importlib import reload
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from scipy.stats import invwishart, invgamma
# Get the macro dataset
dta = sm.datasets.macrodata.load_pandas().data
dta.index = pd.date_range('1959Q1', '2009Q3', freq='Q... |
ogoann/StatisticalMethods | examples/Cepheids/FirstLook.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (15.0, 8.0)
"""
Explanation: A First Look at the Periods and Luminosities of Cepheid Stars
Cepheids are stars whose brightness oscillates with a stable period that appears to be strongly correlated with their lumi... |
nslatysheva/data_science_blogging | tricks_of_the_trade_ensembling/messy_modelling_simplified.ipynb | gpl-3.0 | # Creating the dataset
# e.g. make_moons generates crescent-shaped data
# Check out make_classification, which generates ~linearly-separable data
from sklearn.datasets import make_moons
X, y = make_moons(
n_samples=500, # the number of observations
random_state=1,
noise=0.3 #0.3
)
# Take a peek
print(X[:... |
wittawatj/fsic-test | ipynb/ex2_results.ipynb | mit | %load_ext autoreload
%autoreload 2
%matplotlib inline
#%config InlineBackend.figure_format = 'svg'
#%config InlineBackend.figure_format = 'pdf'
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import fsic.data as data
import fsic.glo as glo
import fsic.indtest as it
import fsic.kernel as kernel
imp... |
dsacademybr/PythonFundamentos | Cap05/Notebooks/DSA-Python-Cap05-Exercicios-Solucao.ipynb | gpl-3.0 | # Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
"""
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 5</font>
Download: http://github.com/dsacademybr
End of explanation
"""
# Exerc... |
guyk1971/deep-learning | batch-norm/Batch_Normalization_Lesson_GK.ipynb | mit | # Import necessary packages
import tensorflow as tf
import tqdm
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Import MNIST data so we have something for our experiments
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("../gan_mnist/MNIST_data/", one... |
bloomberg/bqplot | examples/Interactions/Mark Interactions.ipynb | apache-2.0 | x_sc = LinearScale()
y_sc = LinearScale()
x_data = np.arange(20)
y_data = np.random.randn(20)
scatter_chart = Scatter(
x=x_data,
y=y_data,
scales={"x": x_sc, "y": y_sc},
colors=["dodgerblue"],
interactions={"click": "select"},
selected_style={"opacity": 1.0, "fill": "DarkOrange", "stroke": "Re... |
GoogleCloudPlatform/ml-design-patterns | 06_reproducibility/feature_store.ipynb | apache-2.0 | import os
# Feast Core acts as the central feature registry
FEAST_CORE_URL = os.getenv('FEAST_CORE_URL', 'localhost:6565')
# Feast Online Serving allows for the retrieval of real-time feature data
FEAST_ONLINE_SERVING_URL = os.getenv('FEAST_ONLINE_SERVING_URL', 'localhost:6566')
# Feast Batch Serving allows for the ... |
jGaboardi/LP_MIP | .ipynb_checkpoints/Primal_v_Dual_Canonical_GUROBI-checkpoint.ipynb | lgpl-3.0 | # Imports
import numpy as np
import gurobipy as gbp
import datetime as dt
# Constants
Aij = np.random.randint(5, 50, 25)
Aij = Aij.reshape(5,5)
AijSum = np.sum(Aij)
Cj = np.random.randint(10, 20, 5)
CjSum = np.sum(Cj)
Bi = np.random.randint(10, 20, 5)
BiSum = np.sum(Bi)
# Matrix Shape
rows = range(len(Aij))
cols = r... |
keras-team/keras-io | examples/vision/ipynb/cutmix.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
np.random.seed(42)
tf.random.set_seed(42)
"""
Explanation: CutMix data augmentation for image classification
Author: Sayan Nath<br>
Date created: 2021/06/08<br>
Last modified: 2021/06/08<br>
Des... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch2-Example_2-10.ipynb | unlicense | %pylab notebook
"""
Explanation: Electric Machinery Fundamentals 5th edition
Chapter 2 (Code examples)
Example 2-10
Calculate and plot the magnetization current of a 230/115 transformer operating at 230 volts and 50/60 Hz. This program also calculates the rms value of the mag. current.
Import the PyLab namespace (pr... |
mdeff/ntds_2016 | algorithms/02_sol_clustering.ipynb | mit | # Load libraries
# Math
import numpy as np
# Visualization
%matplotlib notebook
import matplotlib.pyplot as plt
plt.rcParams.update({'figure.max_open_warning': 0})
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy import ndimage
# Print output of LFR code
import subprocess
# Sparse matrix
import ... |
Oslandia/open-data-bikes-analysis | notebooks/Prediction-Lyon.ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
import graphviz
from xgboost import plot_tree, plot_importance, to_graphviz
import matplotlib as mpl
from matplotlib import pyplot as plt
import seaborn as sns
import folium
%load_ext watermark
%watermark -d -v -p numpy,pandas,xgboost,matplotlib,folium -g... |
zerothi/ts-tbt-sisl-tutorial | TB_08/run.ipynb | gpl-3.0 | graphene = sisl.geom.graphene(orthogonal=True)
# Graphene tight-binding parameters
on, nn = 0, -2.7
H_minimal = sisl.Hamiltonian(graphene)
H_minimal.construct([[0.1, 1.44], [on, nn]])
"""
Explanation: In TBtrans and TranSiesta one is capable of performing real space transport calculations by using real space self-ene... |
beyondvalence/biof509_wtl | Wk09-dataset-processing/Wk09_Dataset-preprocessing_wl.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
"""
Explanation: Week 9 - Dataset preprocessing
Before we utilize machine learning algorithms we must first prepare our dataset. This can often take a significant amount of time and can have a large impact on the performance of ... |
phoebe-project/phoebe2-docs | 2.3/tutorials/constraints_builtin.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.3,<2.4"
"""
Explanation: Advanced: Built-In Constraints
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
"""
import phoebe
from phoebe import u # units
import num... |
tkurfurst/deep-learning | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
tensorflow/docs-l10n | site/ja/probability/examples/Fitting_DPMM_Using_pSGLD.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... |
prasants/pyds | 12.Introduction_to_Pandas.ipynb | mit | import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Pandas:-Introduction" data-toc-modified-id="Pandas:-Introduction-1"><span class="toc-item-num">1 </span>Pandas: Introduction</a></div><div clas... |
jluttine/bayespy | doc/source/examples/multinomial.ipynb | mit | n_colors = 5 # number of possible colors
n_bags = 3 # number of bags
n_trials = 20 # number of draws from each bag
"""
Explanation: Multinomial distribution: bags of marbles
Written by: Deebul Nair (2016)
Edited by: Jaakko Luttinen (2016)
Inspired by https://probmods.org/hierarchical-models.html
Using multinomial ... |
ES-DOC/esdoc-jupyterhub | notebooks/ipsl/cmip6/models/sandbox-2/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'sandbox-2', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: IPSL
Source ID: SANDBOX-2
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balan... |
landmanbester/fundamentals_of_interferometry | 2_Mathematical_Groundwork/fft_implementation_assignment.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
import cmath
"""
Explanation: Implementation of a Radix-2 Fast Fourier Transform
Import standard modules:
End of explanation
"""
def loop_DFT(x):
"""
Impleme... |
ES-DOC/esdoc-jupyterhub | notebooks/fio-ronm/cmip6/models/sandbox-1/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'fio-ronm', 'sandbox-1', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: FIO-RONM
Source ID: SANDBOX-1
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Ener... |
tpin3694/tpin3694.github.io | sql/select_rows_based_on_text_string.ipynb | mit | # Ignore
%load_ext sql
%sql sqlite://
%config SqlMagic.feedback = False
"""
Explanation: Title: Select Rows Based On Text String
Slug: select_rows_based_on_text_string
Summary: Select Rows Based On Text String in SQL.
Date: 2017-01-16 12:00
Category: SQL
Tags: Basics
Authors: Chris Albon
Note: This tutorial ... |
weikang9009/giddy | notebooks/Sequence.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import libpysal
import mapclassify as mc
f = libpysal.io.open(libpysal.examples.get_path("usjoin.csv"))
pci = np.array([f.by_col[str(y)] for y in range(1929,2010)])
q5 = np.array([mc.Quantiles(y,k=5).yb for y in pci]).transpose()
q5
q5.shape
"""
Explanation: Alignment-based seq... |
MikeLing/shogun | doc/ipython-notebooks/evaluation/xval_modelselection.ipynb | gpl-3.0 | %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)))
_... |
VlachosGroup/VlachosGroupAdditivity | docs/source/WorkshopJupyterNotebooks/pgradd_demo/pgradd_demo.ipynb | mit | import pgradd
print(pgradd.__file__)
from pgradd.GroupAdd import GroupLibrary
import pgradd.ThermoChem
lib = GroupLibrary.Load('GRWSurface2018')
"""
Explanation: Theory, Applications, and Tools for Multiscale Kinetic Modeling (July 2020)
pGrAdd Demonstration
1. Introduction
<img src="images/pGrAdd_RGB_github.png" w... |
ajs3g11/training-public | FEEG6016 Simulation and Modelling/02-Monte-Carlo_Lab-2.ipynb | mit | from IPython.core.display import HTML
css_file = 'https://raw.githubusercontent.com/ngcm/training-public/master/ipython_notebook_styles/ngcmstyle.css'
HTML(url=css_file)
"""
Explanation: Monte Carlo Methods: Lab 2
End of explanation
"""
p_JZG_T2 = [0.1776, 0.329, 0.489, 0.7, 1.071, 1.75, 3.028, 5.285, 9.12]
"""
Exp... |
fotis007/python_intermediate | Python_2_2.ipynb | gpl-3.0 | #beispiel
a = [1, 2, 3,]
my_iterator = iter(a)
my_iterator.__next__()
my_iterator.__next__()
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Python-für-Fortgeschrittene-2" data-toc-modified-id="Python-für-Fortgeschrittene-2-1"><span class="toc-item-num">1 </span>Python für Fortge... |
csc-training/python-introduction | notebooks/exercises/4 - Functions and exceptions.ipynb | mit | def celsius_to_kelvin(c):
# implementation here
pass
celsius_to_kelvin(0)
"""
Explanation: Functions and exceptions
Functions
Write a function that converts from Celsius to Kelvin.
To convert from Celsius to Kelvin you add 273.15 from the value.
Try your solution for a few values.
End of explanation
"""
def... |
cathalmccabe/PYNQ | boards/Pynq-Z1/base/notebooks/pmod/pmod_grove_buzzer.ipynb | bsd-3-clause | from pynq.overlays.base import BaseOverlay
base = BaseOverlay("base.bit")
"""
Explanation: Grove Buzzer v1.2
This example shows how to use the Grove Buzzer v1.2.
A Grover Buzzer, and PYNQ Grove Adapter are required.
To set up the board for this notebook, the PYNQ Grove Adapter is connected to PMODB and the Grove Buzz... |
warrierr/cs109 | hw0/hw0.ipynb | mit | import sys
print sys.version
"""
Explanation: Homework 0
Survey due 4th September, 2015
Submission due 10th September, 2015
Welcome to CS109 / STAT121 / AC209 / E-109 (http://cs109.org/). In this class, we will be using a variety of tools that will require some initial configuration. To ensure everything goes smooth... |
teuben/astr288p | notebooks/03-arrays.ipynb | mit | a = [1,2,3]
b = [4,5,6]
c = a+b
print(c)
"""
Explanation: Arrays for Numerical work?
End of explanation
"""
a.append(b)
print(a)
def sum(data):
""" sum the elements of an array
"""
asum = 0.0
for i in data:
asum = asum + i
return asum
# the length of the array is defined here, and re-us... |
tomquisel/wine-tasting | Wine Tasting Analysis.ipynb | mit | %matplotlib inline
import pylab as plt
import seaborn as sns
import pandas as pd
import numpy as np
import scipy.stats
import datetime as dt
import random
from IPython.display import display
sns.set_context("notebook", font_scale=2)
raw_data = pd.read_csv('/Users/tom/Downloads/Wine Tasting Data - Sheet1.csv')
raw_da... |
GoogleCloudPlatform/mlops-on-gcp | immersion/explainable_ai/solutions/xai_structured_caip.ipynb | apache-2.0 | import os
PROJECT_ID = "" # TODO: your PROJECT_ID here.
os.environ["PROJECT_ID"] = PROJECT_ID
BUCKET_NAME = "" # TODO: your BUCKET_NAME here.
REGION = "us-central1"
os.environ['BUCKET_NAME'] = BUCKET_NAME
os.environ['REGION'] = REGION
"""
Explanation: AI Explanations: Explaining a tabular data model
Overview
In th... |
pdwyys20/deep-learning | intro-to-tensorflow/intro_to_tensorflow.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... |
unnati-xyz/intro-python-data-science | wine/wine-selection.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
plt.style.use('ggplot')
plt.rcParams['figure.figsize'] = (13,8)
df = pd.read_csv("./winequality-red.csv")
df.head()
df.shape
"""
Explanation: Wine Selection
Framing
I want to buy a fine wine but I have no... |
statsmodels/statsmodels | examples/notebooks/kernel_density.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.distributions.mixture_rvs import mixture_rvs
"""
Explanation: Kernel Density Estimation
Kernel density estimation is the process of estimating an unknown probability density funct... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_time_frequency_mixed_norm_inverse.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
from mne.inverse_sparse import tf_mixed_norm
from mne.viz import plot_sparse_source_estimates
print(__doc__)
... |
chrisjsewell/jsonextended | README.ipynb | mit | from jsonextended import edict, plugins, example_mockpaths
"""
Explanation: JSON Extended
A module to extend the python json package functionality:
Treat a directory structure like a nested dictionary:
lightweight plugin system: define bespoke classes for parsing different file extensions and encoding/decoding obj... |
CentreForResearchInAppliedLinguistics/clic | docs/notebooks/Concordance/A serious concordance.ipynb | mit | # coding: utf-8
import os
from cheshire3.baseObjects import Session
from cheshire3.document import StringDocument
from cheshire3.internal import cheshire3Root
from cheshire3.server import SimpleServer
session = Session()
session.database = 'db_dickens'
serv = SimpleServer(session, os.path.join(cheshire3Root, 'con... |
3upperm2n/notes-deeplearning | projects/language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
#target_text
"""
Explanation: Language Translation
In this project... |
jackbrucesimpson/Machine-Learning-Workshop | training_testing.ipynb | mit | import cv2
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
%matplotlib inline
"""
Explanation: Building a machine learning program
In this section we put together everything we learned about images and features so that we can train a machine learning algorithm to distinguish between the i... |
fastai/course-v3 | zh-nbs/Lesson3_head_pose.ipynb | apache-2.0 | %reload_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.vision import *
"""
Explanation: Practical Deep Learning for Coders, v3
Lesson3_head_pose
Regression with BIWI head pose dataset<br>
用BIWI头部姿势数据集进行回归建模
This is a more advanced example to show how to create custom datasets and do regression with image... |
MatteusDeloge/opengrid | notebooks/DemoTmpo.ipynb | apache-2.0 | import sys
import os
import inspect
import tmpo
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt
import pytz
from opengrid.library import houseprint
from opengrid import config
c=config.Config()
%matplotlib inline
plt.rcParams['figure.figsize'] = 14,8
"""
Explanation: Quick Tmpo demo
To get ... |
dataDogma/Computer-Science | .ipynb_checkpoints/DAT208x - Week 1 - Python Basics-checkpoint.ipynb | gpl-3.0 | # working with print function
print(5 / 8)
# Add another print function on new line
print(7 + 10)
"""
Explanation: Lecture : Hello Python!
[RQ-1] : Which of the following statements is correct?
Ans: The Ipython Shell is typically used to work with Python interactively.
[RQ-2] : Which file extension is used for P... |
KECB/learn | BAMM.101x/Datetime_Example.ipynb | mit | #Unfortunatel, this won't work on Windows.
!head sample_data.csv
"""
Explanation: <h1>Bucketing time</h1>
<h4>The file "sample_data.csv" contains start times and processing times for all complaints registered with New York City's 311 complaint hotline on 01/01/2016. Our goal is to compute the average processing time ... |
ga7g08/ga7g08.github.io | _notebooks/2015-04-24-Gaussian-mixture-model-for-pulsar-population.ipynb | mit | %%writefile Makefile
DOWNLOADED = psrcat_pkg.tar
ATNF_DATABASE = psrcat_tar
DATA_FILE = ATNF_data_file.txt
PSRCAT_FILE_PATH = ./psrcat_tar/psrcat.db
all: $(DATA_FILE) $(ATNF_DATABASE)
.PHONY: clean
$(ATNF_DATABASE):
wget http://www.atnf.csiro.au/people/pulsar/psrcat/downloads/psrcat_pkg.tar.gz
gunzip psrcat_pkg... |
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