repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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tensorflow/docs-l10n | site/ja/r1/tutorials/keras/basic_text_classification.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
crystalzhaizhai/cs207_yi_zhai | lectures/L18/L18.ipynb | mit | import sqlite3
"""
Explanation: Lecture 18
Wednesday, November 8th, 2017
Databases with SQlite
SQLite Exercises
Today you will work with the candidates and contributors datasets to create a database in Python using SQLite.
The exercises will consist of a sequence of steps to help illustrate basic commands.
<a id='deli... |
phoebe-project/phoebe2-docs | development/tutorials/latex_repr.ipynb | gpl-3.0 | #!pip install -I "phoebe>=2.4,<2.5"
import phoebe
from phoebe import u # units
import numpy as np
logger = phoebe.logger()
"""
Explanation: Advanced: Parameter Latex Representation
Setup
Let's first make sure we have the latest version of PHOEBE 2.4 installed (uncomment this line if running in an online notebook ses... |
mlund/faunus-notebooks | gvdw-interactive/gvdw.ipynb | mit | from __future__ import division, unicode_literals, print_function
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import quad
from ipywidgets import interact
from math import sqrt, pi
plt.rcParams.update({'font.size': 14, 'figure.figsize': [6.0, 4.0]})
"""
Explanation: Equati... |
au9ustine/org.edx.cs100 | 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... |
DistrictDataLabs/yellowbrick | examples/ndanielsen/Missing Values Examples.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
import sys
sys.path.append("./../..")
%reload_ext yellowbrick
%matplotlib inline
# Imports
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from sklearn.preprocessing import OneHotEncoder
from yellowbrick.contrib.missing import MissingValue... |
ray-project/ray | doc/source/ray-core/examples/highly_parallel.ipynb | apache-2.0 | import ray
import random
import time
import math
from fractions import Fraction
# Let's start Ray
ray.init(address='auto')
"""
Explanation: Using Ray for Highly Parallelizable Tasks
While Ray can be used for very complex parallelization tasks,
often we just want to do something simple in parallel.
For example, we may... |
eaton-lab/toytree | docs/5-toytrees.ipynb | bsd-3-clause | import toytree
import toyplot
import numpy as np
# load a tree for this tutorial
tre = toytree.tree("https://eaton-lab.org/data/Cyathophora.tre")
"""
Explanation: ToyTree objects
The main class object users interact with in toytree is called a ToyTree. This object contains a number of useful functions for interacting... |
ksooklall/deep_learning_foundation | 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... |
ptpro3/ptpro3.github.io | Projects/Project5/Neural Networks Summary.ipynb | mit | # plot y = x-squared
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
x = np.linspace(-5,5,1000)
y = x**2
plt.plot(x,y);
# create our function
def f(x):
return x**2
# define values
epsilon = 1e-5
x = 4
# calculate delta y / delta x
gradient = (f(x+epsilon) - f(x-epsilon)) / (2*epsilon)
# ... |
marmarko/ml101 | tensorflow/examples/tutorials/deepdream/deepdream.ipynb | bsd-2-clause | # boilerplate code
from __future__ import print_function
import os
from io import BytesIO
import numpy as np
from functools import partial
import PIL.Image
from IPython.display import clear_output, Image, display, HTML
import tensorflow as tf
"""
Explanation: DeepDreaming with TensorFlow
Loading and displaying the m... |
neuro-data-science/neuro_data_science | python/basics/comparing_two_datasets.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import (ttest_ind, ttest_1samp, ttest_rel,
wilcoxon, ks_2samp)
from statsmodels.distributions.empirical_distribution import ECDF
import statsmodels.api as sm
%matplotlib inline
"""
Explanation: Comparing t... |
molgor/spystats | notebooks/.ipynb_checkpoints/global_variogram-checkpoint.ipynb | bsd-2-clause | # Load Biospytial modules and etc.
%matplotlib inline
import sys
sys.path.append('/apps')
import django
django.setup()
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
## Use the ggplot style
plt.style.use('ggplot')
from external_plugins.spystats import tools
%run ../testvariogram.py
section.sha... |
madsenmj/ml-introduction-course | Class10/Class10.ipynb | apache-2.0 | import pandas as pd
df = pd.read_csv('Class10_wine_data.csv')
df.head()
"""
Explanation: Class 10
ML Techniques: Feature scaling
Another aspect of optimizing machine learning algorithms is to think about feature scaling. When we use multiple numeric features as inputs to a regression or classification algorithm, the... |
bsmithyman/zephyr | Demo 1 - Single forward problem [local].ipynb | mit | import numpy as np
"""
Explanation: Demo 1 - Single forward problem [local]
Brendan Smithyman | bsmithym@uwo.ca | March, 2015
Import NumPy
End of explanation
"""
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib
%matplotlib inline
from IPython.display import set_matplotlib_formats
set_ma... |
betatim/BlackBox | examples/bayesian-optimization.ipynb | bsd-3-clause | import numpy as np
np.random.seed(123)
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Bayesian optimization with skopt
Gilles Louppe, Manoj Kumar July 2016.
End of explanation
"""
noise_level = 0.1
def f(x, noise_level=noise_level):
return np.sin(5 * x[0]) * (1 - np.tanh(x[0] ** 2)) + np.r... |
dennys-bd/Coursera-Machine-Learning-Specialization | Course 2 - ML, Regression/week-4-ridge-regression-assignment-1-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 4: Ridge Regression (interpretation)
In this notebook, we will run ridge regression multiple times with different L2 penalties to see which one produces the best fit. We will revisit the example of polynomial regression as a means to see the effect of L2 regularization.... |
ShinjiKatoA16/UCSY-sw-eng | Iterable object.ipynb | mit | # iterable objecct retuns iterator to iter() function
s = 'abc'
itr = iter(s)
print(next(itr))
print(next(itr))
print(next(itr))
## __iter__() function and __next__() function can be called directly as well
x = s.__iter__()
print(x.__next__())
print(next(x))
# StopIteration signal is raised after all items are ret... |
robblack007/clase-dinamica-robot | Practicas/practica1/numerico.ipynb | mit | def f(x, t):
# Se importan funciones matematicas necesarias
from numpy import cos
# Se desenvuelven las variables que componen al estado
q1, q̇1 = x
# Se definen constantes del sistema
g = 9.81
m1, J1 = 0.3, 0.0005
l1 = 0.2
τ1 = 0
# Se calculan las variables a devolver por e... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_sensor_permutation_test.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne import io
from mne.stats import permutation_t_test
from mne.datasets import sample
print(__doc__)
"""
Explanation: Permutation T-test on sensor data
One tests if the signal sign... |
weikang9009/giddy | notebooks/DirectionalLISA.ipynb | bsd-3-clause | import libpysal
import numpy as np
from giddy.directional import Rose
%matplotlib inline
f = open(libpysal.examples.get_path('spi_download.csv'), 'r')
lines = f.readlines()
f.close()
lines = [line.strip().split(",") for line in lines]
names = [line[2] for line in lines[1:-5]]
data = np.array([list(map(int, line[3:])... |
danielgoncalvesti/BIGDATA2017 | Atividade02/Lab/Lab2_Spark_PySpark.ipynb | gpl-3.0 | ListaPalavras = ['gato', 'elefante', 'rato', 'rato', 'gato']
palavrasRDD = sc.parallelize(ListaPalavras, 4)
print type(palavrasRDD)
print palavrasRDD.collect()
"""
Explanation: Spark + Python = PySpark
Esse notebook introduz os conceitos básicos do Spark através de sua interface com a linguagem Python. Como aplicação ... |
guruucsd/EigenfaceDemo | python/Backprop Demo.ipynb | mit | import pickle
dataset = pickle.load(open('data/cafe.pkl','r')) # or 'pofa.pkl'
# This is the neural network class, for your information.
from sklearn.decomposition import PCA
from sklearn.learning_curve import learning_curve
from sklearn.cross_validation import train_test_split
from numpy.linalg import pinv
from num... |
mne-tools/mne-tools.github.io | dev/_downloads/99a2efbcf51159fbb58f12830f81525d/compute_csd.ipynb | bsd-3-clause | # Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
# License: BSD-3-Clause
from matplotlib import pyplot as plt
import mne
from mne.datasets import sample
from mne.time_frequency import csd_fourier, csd_multitaper, csd_morlet
print(__doc__)
"""
Explanation: Compute a cross-spectral density (CSD) matrix
A cross-spe... |
tensorflow/workshops | tfx_labs/Lab_4_Data_Validation.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... |
phuijse/bagging_pu | PU_Learning_simple_example.ipynb | mit | N = 6000
known_labels_ratio = 0.1
X, y = make_moons(n_samples=N, noise=0.1, shuffle=True)
rp = np.random.permutation(int(N/2))
data_P = X[y==1][rp[:int(len(rp)*known_labels_ratio)]]
data_U = np.concatenate((X[y==1][rp[int(len(rp)*known_labels_ratio):]], X[y==0]), axis=0)
print("Amount of labeled samples: %d" % (data_P.... |
mediagit2016/workcamp-maschinelles-lernen-grundlagen | 20-10-28-ml-workcamp/wc-tf-data-preparation-10-aufgabe.ipynb | gpl-3.0 | # import pandas as pd
# Zum Arbeiten mit Series
# Für die Normalisierung von Daten verwenden wir den MinMaxScaler
# Definieren einer Panda Series
# data=[11.0,201.0,301.0,41.0,501.0,601.0,701.0,81.0,901.0,1001.0]
# Worin besteht hier das Problem ?
data=[11.0,201.0,301.0,41.0,501.0,601.0,701.0,81.0,901.0,1001.0]
# ... |
fmaussion/cleo | examples/DataLevels.ipynb | gpl-3.0 | import matplotlib as mpl
import numpy as np
from cleo.graphics import DataLevels
%matplotlib inline
a = np.array([-1., 0., 1.1, 1.9, 9.])
cm = mpl.cm.get_cmap('RdYlBu_r')
"""
Explanation: cleo.DataLevels examples
End of explanation
"""
dl = DataLevels(a, cmap=cm)
dl.visualize(orientation='horizontal', add_values=... |
thalesians/tsa | src/jupyter/python/pypeincoming.ipynb | apache-2.0 | import os, sys
sys.path.append(os.path.abspath('../../main/python'))
import thalesians.tsa.pypes as pypes
pype = pypes.Pype(pypes.Direction.INCOMING, name='EXAMPLE', port=5758); pype
"""
Explanation: This Jupyter notebook should be used in conjunction with pypeoutgoing.ipynb.
Run through the following cells...
End o... |
lamahechag/clubes_de_ciencia | Dia_2/Visualización de datos (calentamiento global).ipynb | mit | # ejemplo
def mi_funcion():
return 'Clubes de Ciencia!!'
print (mi_funcion())
#ejemplo
def contar_letras(texto):
n = len(texto)
return n
def contar_palabras(texto):
lista = texto.split(' ')
n = len(lista)
return n
def contar_palabras_letras(texto):
palabras = contar_palabras(texto)
... |
mromanello/SunoikisisDC_NER | participants_notebooks/Sunoikisis - Named Entity Extraction 1b_MR.ipynb | gpl-3.0 | ########
# NLTK #
########
import nltk
from nltk.tag import StanfordNERTagger
########
# CLTK #
########
import cltk
from cltk.tag.ner import tag_ner
##############
# MyCapytain #
##############
import MyCapytain
from MyCapytain.resolvers.cts.api import HttpCTSResolver
from MyCapytain.retrievers.cts5 import CTS
from M... |
bmbutle2/ethereum_blockchain | notebooks/feature_engineering.ipynb | gpl-3.0 | df['txcnt_second'] = df['tx_count'].values / df['blockTime'].values
df['avg_gasUsed_t_perblock'] = df.groupby('block_id')['gasUsed_t'].transform('mean')
df['avg_price_perblock'] = df.groupby('block_id')['price_gwei'].transform('mean')
def rolling_avg(window_size):
price = df[['block_id', 'avg_price_perblock']... |
heatseeknyc/data-science | src/bryan analyses/Hack for Heat #3.ipynb | mit | connection = psycopg2.connect('dbname = threeoneone user=threeoneoneadmin password=threeoneoneadmin')
cursor = connection.cursor()
"""
Explanation: Hack for Heat #3: Number of complaints over time
This time, we're going to look at raw 311 complaint data. The data that I was working with previously was summarized data.... |
Nozdi/first-steps-with-pandas-workshop | first-steps-with-pandas.ipynb | mit | import platform
print('Python: ' + platform.python_version())
import numpy as np
print('numpy: ' + np.__version__)
import pandas as pd
print('pandas: ' + pd.__version__)
import scipy
print('scipy: ' + scipy.__version__)
import sklearn
print('scikit-learn: ' + sklearn.__version__)
import matplotlib as plt
print('ma... |
brclark-usgs/flopy | examples/Notebooks/flopy3_SpatialReference_demo.ipynb | bsd-3-clause | import sys
sys.path.append('../..')
import os
import numpy as np
import matplotlib.pyplot as plt
import flopy
from flopy.utils.reference import SpatialReference
import flopy.utils.binaryfile as bf
% matplotlib inline
outpath = 'temp/'
"""
Explanation: FloPy
SpatialReference demo
A short demonstration of functionality... |
andrew-lundgren/detchar | Notebooks/inverse_spectrum_truncation.ipynb | gpl-3.0 | sample_rate = 4096
nyquist = sample_rate/2
time_length_seconds = 512
"""
Explanation: We're going to simulate data discretely sampled in time. We need to set a sample rate in Hz. The highest resolvable frequency, the Nyquist frequency, is half the sample rate. We also need to decide how long the simulated data record ... |
mathnathan/notebooks | mpfi/GMM in High Dimensions.ipynb | mit | import matplotlib
#matplotlib.use('nbagg')
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from scipy import linalg
from sklearn.mixture import GaussianMixture
def generate_clusters(dims, num_pts=1000):
clust1 = np.random.multivariate_normal([-0.5]*dims, np.eye(dims), num_... |
Arn-O/kadenze-deep-creative-apps | session-1/lecture-1.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
"""
Explanation: Session 1: Introduction to Tensorflow
<p class='lead'>
Creative Applications of Deep Learning with Tensorflow<br />
Parag K. Mital<br />
Kadenze, Inc.<br />
</p>
<a name="learning-goals"></a>
Learning Goals
... |
cwehmeyer/pydpc | ipython/Example01.ipynb | lgpl-3.0 | %matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from pydpc import Cluster
from pydpc._reference import Cluster as RefCluster
"""
Explanation: Example and timings
This notebook gives a short introduction in how to use pydpc for a simple clustering problem.
End of explanati... |
alfkjartan/control-computarizado | approximating-cont-controller/notebooks/First-order-hold sampling of lead compensator.ipynb | mit | h, b, a,K = sy.symbols('h, b, a, K', real=True, positive=True)
s, z = sy.symbols('s, z', real=False)
F = K*(s+b)/(s+a)
U = F/s/s
Up = sy.apart(U, s)
Up
from sympy.integrals.transforms import inverse_laplace_transform
from sympy.abc import t
u = sy.simplify(inverse_laplace_transform(Up, s, t))
u
"""
Explanation: Fir... |
nudomarinero/mltier1 | Match_LOFAR_combined_generic.ipynb | gpl-3.0 | import numpy as np
from astropy.table import Table, join
from astropy import units as u
from astropy.coordinates import SkyCoord, search_around_sky
from IPython.display import clear_output
import pickle
import os
from mltier1 import (get_center, Field, MultiMLEstimatorU, parallel_process, get_sigma_all)
%load_ext aut... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/tfx_pipelines/guided_projects/guided_project_3_nlp_starter/keras_for_text_classification.ipynb | apache-2.0 | import os
import pandas as pd
from google.cloud import bigquery
%load_ext google.cloud.bigquery
"""
Explanation: Keras for Text Classification
Learning Objectives
1. Learn how to create a text classification datasets using BigQuery
1. Learn how to tokenize and integerize a corpus of text for training in Keras
1. Lea... |
dipanjank/ml | tensorflow/word_embeddings_in_tensorflow.ipynb | gpl-3.0 | import numpy as np
import tensorflow as tf
%pylab inline
pylab.style.use('ggplot')
import pandas as pd
"""
Explanation: In this notebook, we will explore word embeddings based on the news articles in the Brown Corpus. Note that the content is heavily borrowed from the official tensorflow tutorial: https://www.tensorfl... |
michaelaye/planet4 | notebooks/user_name_encoding.ipynb | isc | from planet4 import io
db = io.DBManager()
data = db.get_all()
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(data.user_name)
len(le.classes_)
data.user_name = le.transform(data.user_name)
le.transform(data.tail().user_name)
import pickle
folder = Path('/Users/klay6683/Dropbox/data/p... |
landmanbester/fundamentals_of_interferometry | 7_Observing_Systems/7_5_primary_beam.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
"""
Explanation: Outline
Glossary
7. Observing Systems
Previous: 7.4 Digital Correlators
Next: 7.6 Polarization and Antenna Feeds
Import standard modules:
End of ... |
jorisvandenbossche/DS-python-data-analysis | _solved/pandas_06_data_cleaning.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: <p><font size="6"><b> Pandas: Methods for data cleaning</b></font></p>
© 2021, Joris Van den Bossche and Stijn Van Hoey (jorisvandenbossche&... |
comp-journalism/Baseline_Problem_for_Algorithm_Audits | IMAGE_BOX/ImageAPI_analysis.ipynb | mit | def read_jsons(f, candidate):
tmp_dict = {}
with open(f) as json_file:
data = json.load(json_file)
for i in data[0]['scores']:
if data[0]['scores'][i] > 0.55: # confidence score threshold.
tmp_dict[i] = data[0]['scores'][i]
else: tmp_dict[i] = n... |
tpin3694/tpin3694.github.io | python/matplotlib_stacked_bar_plot.ipynb | mit | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Title: Scatterplot In MatPlotLib
Slug: matplotlib_stacked_bar_plot
Summary: Scatterplot In MatPlotLib
Date: 2016-05-01 12:00
Category: Python
Tags: Data Visualization
Authors: Chris Albon
Based on: Sebastian R... |
erikdrysdale/erikdrysdale.github.io | _rmd/extra_hrt/hrt_python_copy.ipynb | mit | # import the necessary modules
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
import seaborn as sns
"""
Explanation: The HRT for mixed data types (Python implementation)
In my last post I showed how the holdout random test (HR... |
AndreySheka/dl_ekb | hw9/Seminar9-ru-Avito.ipynb | mit | low_RAM_mode = True
very_low_RAM = False #если у вас меньше 3GB оперативки, включите оба флага
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Использование глубокого обучения в NLP
Смотрите в этой серии:
* Простые способы работать с текстом, bag of words
... |
vicolab/neural-network-intro | 3-convnet/convolutional-network.ipynb | mit | # Plots will be displaying plots within the notebook
%matplotlib notebook
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
# NumPy is a package for manipulating N-dimensional array objects
import numpy as np
# Pandas is a data analysis package
import pandas as pd
#Library To test/verify som... |
hparik11/Deep-Learning-Nanodegree-Foundation-Repository | sentiment-network/Sentiment_Classification_Projects.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
ChildMindInstitute/HBN-wearable-analysis | analysis.ipynb | apache-2.0 | %matplotlib inline
%load_ext autoreload
%autoreload 2
import readline # for R magics
%load_ext rpy2.ipython
import warnings
from rpy2.rinterface import RRuntimeWarning
warnings.filterwarnings("ignore", category=RRuntimeWarning)
from utilities.chart_data import df_devices_qt, linechart, xcorr
from config import config
f... |
sdpython/pyquickhelper | _unittests/ut_helpgen/notebooks_docx/td2a_TD5_Traitement_automatique_des_langues_en_Python.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: Traitement automatique de la langue en Python
End of explanation
"""
import httplib2 # pip install httplib2
import json # déjà installée, sinon : pip install json
import apiclient.discovery # pip install google-api-python-client
import... |
bgroveben/python3_machine_learning_projects | introduction_to_ml_with_python/1_Introduction.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import mglearn
from IPython.display import display
%matplotlib inline
"""
Explanation: Chapter 1: Introduction
End of explanation
"""
import numpy as np
x = np.array([[1,2,3],[4,5,6]])
print("x:\n{}".format(x))
"""
Explanation: Essential Librar... |
quantopian/research_public | notebooks/data/quandl.adp_empl_sec/notebook.ipynb | apache-2.0 | # import the dataset
from quantopian.interactive.data.quandl import adp_empl_sec
# Since this data is public domain and provided by Quandl for free, there is no _free version of this
# data set, as found in the premium sets. This import gets you the entirety of this data set.
# import data operations
from odo import ... |
saketkc/notebooks | python/coursera-BayesianML/07_finding_suspect.ipynb | bsd-2-clause | %tensorflow_version 1.x
"""
Explanation: <a href="https://colab.research.google.com/github/saketkc/notebooks/blob/master/python/coursera-BayesianML/07_finding_suspect.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
First things first
Click File -> ... |
poppy-project/pypot | samples/notebooks/Record, Save, and Play Moves on a Poppy Creature.ipynb | gpl-3.0 | from pypot.creatures import PoppyErgo
poppy = PoppyErgo()
for m in poppy.motors:
m.compliant = False
m.goal_position = 0.0
"""
Explanation: Record, Save, and Play Moves on a Poppy Creature
This notebook is still work in progress! Feedbacks are welcomed!
In this tutorial we will show how to:
* record moves by... |
tensorflow/text | docs/guide/text_tf_lite.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... |
giacomov/3ML | docs/notebooks/synthetic_spectra.ipynb | bsd-3-clause | from threeML import *
import numpy as np
%matplotlib inline
jtplot.style(context="talk", fscale=1, ticks=True, grid=False)
import matplotlib.pyplot as plt
plt.style.use("mike")
import warnings
warnings.simplefilter("ignore")
# Select an astromodels function to from which to simualte
generating_function = Powerl... |
jsignell/MpalaTower | inspection/inspect_raw_netcdf.ipynb | mit | usr = 'Julia'
FILEDIR = 'C:/Users/%s/Dropbox (PE)/KenyaLab/Data/Tower/TowerData/'%usr
NETCDFLOC = FILEDIR + 'raw_netcdf_output/'
DATALOC = 'F:/towerdata/'
"""
Explanation: Inspect Raw Netcdf
Playing around with efficient ways to merge and view netcdf data from the tower. This ipython notebook depends on the python sc... |
seblabbe/MATH2010-Logiciels-mathematiques | NotesDeCours/13-boucle-for.ipynb | gpl-3.0 | from __future__ import division, print_function # Python 3
from sympy import init_printing
init_printing(use_latex='mathjax',use_unicode=False) # Affichage des résultats
"""
Explanation: $$
\def\CC{\bf C}
\def\QQ{\bf Q}
\def\RR{\bf R}
\def\ZZ{\bf Z}
\def\NN{\bf N}
$$
Boucle for
End of explanation
"""
for a in ran... |
JackDi/phys202-2015-work | assignments/assignment08/InterpolationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy.interpolate import interp1d
"""
Explanation: Interpolation Exercise 1
End of explanation
"""
# YOUR CODE HERE
with np.load('trajectory.npz') as data:
x = data['x']
t=data['t']
y=data['y']
plt.plo... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_clickable_image.ipynb | bsd-3-clause | # Authors: Christopher Holdgraf <choldgraf@berkeley.edu>
#
# License: BSD (3-clause)
from scipy.ndimage import imread
import numpy as np
from matplotlib import pyplot as plt
from os import path as op
import mne
from mne.viz import ClickableImage, add_background_image # noqa
from mne.channels import generate_2d_layout ... |
lmoresi/UoM-VIEPS-Intro-to-Python | Notebooks/Introduction/1 - Introduction to Jupyter notebooks.ipynb | mit | ## Example of a simple python code cell
print "Hello little world"
a = 1
## The last statement in a cell prints its value
a
## (this is sometimes a little confusing - add a pass statement to get rid of this !)
#pass
"""
Explanation: Jupyter Notebooks
We are going to be using jupyter notebooks for this course and ... |
kitu2007/dl_class | 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... |
tensorflow/probability | tensorflow_probability/examples/jupyter_notebooks/TensorFlow_Probability_Case_Study_Covariance_Estimation.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... |
twhyntie/coding-challenges | 01_particle_counting.ipynb | apache-2.0 | # Import the data analysis tools we need from the Pandas toolkit.
import pandas as pd
## Our pixel data stored as a Panda DataFrame.
df000 = pd.read_table('./data/crookes/data000.txt', header=None, names=["x", "y", "C"])
# Show the first five rows of pixel data.
df000.head()
"""
Explanation: Challenge 01: Particle C... |
notenboomtest/MCDC_Graph | mcdc_algorithm.ipynb | bsd-3-clause | import graphviz as gv
from IPython.display import display, Image
import functools
import re
from datetime import datetime
import os
digraph = functools.partial(gv.Digraph, format='png')
"""
Explanation: Algorithm for MCDC with graph notation
Description:
This notebook converts a decision (a combination of predicates)... |
scoaste/showcase | machine-learning/regression/week-5-lasso-assignment-2-completed.ipynb | mit | import graphlab
"""
Explanation: Regression Week 5: LASSO (coordinate descent)
In this notebook, you will implement your very own LASSO solver via coordinate descent. You will:
* Write a function to normalize features
* Implement coordinate descent for LASSO
* Explore effects of L1 penalty
Fire up graphlab create
Make... |
QuantEcon/QuantEcon.notebooks | aiyagari_continuous_time.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import sparse
from scipy.sparse.linalg import spsolve
"""
Explanation: Aiyagari (1994) in Continuous Time
By SeHyoun Ahn and Benjamin Moll
The material in this notebook is based on Achdou et al. (2015) "Heterogeneous Agent Models in Conti... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_artifacts_detection.ipynb | bsd-3-clause | import numpy as np
import mne
from mne.datasets import sample
from mne.preprocessing import create_ecg_epochs, create_eog_epochs
# getting some data ready
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
raw = mne.io.read_raw_fif(raw_fname, preload=True)
"""
Explanation: In... |
lionfish0/Classification_talk | ipython/.ipynb_checkpoints/Classification-checkpoint.ipynb | mit | from matplotlib import pyplot as plt #plotting library (lets us draw graphs)
%matplotlib inline
from sklearn import datasets #the datasets from sklearn
digits = datasets.load_digits() #load the digits into the variable 'digits'
"""
Explanation: Classification
The Digit Dataset
For these classification examples we w... |
batfish/pybatfish | jupyter_notebooks/Introduction to Route Analysis.ipynb | apache-2.0 | # Import packages
%run startup.py
bf = Session(host="localhost")
"""
Explanation: Introduction to Route Computation and Analysis using Batfish
Network engineers routinely need to validate routing and forwarding in the network. They often do that by connecting to multiple network devices and executing a series of show ... |
MTG/essentia | src/examples/python/tutorial_rhythm_beatdetection.ipynb | agpl-3.0 | import essentia.standard as es
from tempfile import TemporaryDirectory
# Loading an audio file.
audio = es.MonoLoader(filename='../../../test/audio/recorded/dubstep.flac')()
# Compute beat positions and BPM.
rhythm_extractor = es.RhythmExtractor2013(method="multifeature")
bpm, beats, beats_confidence, _, beats_interv... |
bryantbiggs/movie_torrents | charts.ipynb | mit | # plotly validate with credentials
with open('../_credentials/plotly.txt', 'r') as infile:
user, pw = infile.read().strip().split(', ')
plotly.tools.set_credentials_file(username=user, api_key=pw)
text_color = 'rgb(107, 107, 107)'
colors_dict = {'grey':'rgb(189, 195, 199)', 'aqua':'rgb( 54, 215, 183)', 'navy'... |
ddandur/Twords | jupyter_example_notebooks/Examples of Tweet Collection.ipynb | mit | import sys
sys.path.append('..')
from twords.twords import Twords
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
# this pandas line makes the dataframe display all text in a line; useful for seeing entire tweets
pd.set_option('display.max_colwidth', -1)
twit_mars = Twords()
# set path to fold... |
tsivula/becs-114.1311 | demos_pystan/diabetes.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# import stan interface
import pystan
# add utilities directory to path
import os, sys
util_path = os.path.abspath(os.path.join(os.path.pardir, 'utilities_and_data'))
if util_path not in sys.path and os.path.exists(util_path):
... |
jhprinz/openpathsampling | examples/tests/test_snapshot.ipynb | lgpl-2.1 | import numpy as np
import openpathsampling as paths
import openpathsampling.engines.features as features
from __future__ import print_function
"""
Explanation: Some testing and analysis of the new Snapshot implementation
End of explanation
"""
from IPython.display import Markdown
def code_to_md(snapshot_class):
... |
ES-DOC/esdoc-jupyterhub | notebooks/inpe/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', 'inpe', 'sandbox-1', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: INPE
Source ID: SANDBOX-1
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy Balan... |
mrustl/flopy | examples/Notebooks/flopy3_array_outputformat_options.ipynb | bsd-3-clause | %matplotlib inline
import sys
import os
import platform
import numpy as np
import matplotlib.pyplot as plt
import flopy
#Set name of MODFLOW exe
# assumes executable is in users path statement
version = 'mf2005'
exe_name = 'mf2005'
if platform.system() == 'Windows':
exe_name = 'mf2005.exe'
mfexe = exe_name
#Set... |
jorisvandenbossche/DS-python-data-analysis | _solved/pandas_03a_selecting_data.ipynb | bsd-3-clause | import pandas as pd
# redefining the example DataFrame
data = {'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],
'population': [11.3, 64.3, 81.3, 16.9, 64.9],
'area': [30510, 671308, 357050, 41526, 244820],
'capital': ['Brussels', 'Paris', 'Berlin', 'Amsterdam', 'Lo... |
bcocanougher/errorbarjitter | errorbarjitter.ipynb | apache-2.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set_style('darkgrid')
sns.set_context('talk')
"""
Explanation: Errorbarjitter
Introduction
The purpose of this script is to provide modular code for generating errorbarjitter plots using python and pan... |
JarronL/pynrc | notebooks/Coronagraph_Basics.ipynb | mit | # Import the usual libraries
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
# Enable inline plotting at lower left
%matplotlib inline
"""
Explanation: Coronagraph Basics
This set of exercises guides the user through a step-by-step process of simulating NIRCam coronagraphic observations of the HR... |
quantopian/research_public | notebooks/data/eventvestor.mergers_and_acquisitions/notebook.ipynb | apache-2.0 | # import the dataset
from quantopian.interactive.data.eventvestor import mergers_and_acquisitions_free as dataset
# or if you want to import the free dataset, use:
#from quantopian.data.eventvestor import buyback_auth_free
# import data operations
from odo import odo
# import other libraries we will use
import pandas... |
JJINDAHOUSE/deep-learning | weight-initialization/weight_initialization.ipynb | mit | %matplotlib inline
import tensorflow as tf
import helper
from tensorflow.examples.tutorials.mnist import input_data
print('Getting MNIST Dataset...')
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print('Data Extracted.')
"""
Explanation: Weight Initialization
In this lesson, you'll learn how to fin... |
rdhyee/nypl50 | build_all_gitenberg.ipynb | apache-2.0 | from __future__ import print_function
from itertools import (islice, izip)
import arrow
import github3
import requests
from sqlalchemy import or_
from github_settings import (ry_username, ry_password,
username, password,
# token,
... |
mlperf/training_results_v0.5 | v0.5.0/google/cloud_v3.8/resnet-tpuv3-8/code/resnet/model/models/samples/outreach/demos/eager_execution.ipynb | apache-2.0 | # Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the L... |
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn | doc/notebooks/automaton.accessible.ipynb | gpl-3.0 | import vcsn
"""
Explanation: automaton.accessible
Create a new automaton from the accessible part of the input, i.e., the subautomaton whose states can be reached from an initial state.
Preconditions:
- None
Postconditions:
- Result.is_accessible()
See also:
- automaton.is_accessible
- automaton.trim
Examples
End of e... |
LimeeZ/phys292-2015-work | assignments/assignment04/TheoryAndPracticeEx02.ipynb | mit | from IPython.display import Image
"""
Explanation: Theory and Practice of Visualization Exercise 2
Imports
End of explanation
"""
# Add your filename and uncomment the following line:
Image(filename='StockPicture.png')
"""
Explanation: Violations of graphical excellence and integrity
Find a data-focused visualizati... |
gdsfactory/gdsfactory | docs/notebooks/00_geometry.ipynb | mit | import gdsfactory as gf
# Create a blank component (essentially an empty GDS cell with some special features)
c = gf.Component("myComponent")
# Create and add a polygon from separate lists of x points and y points
# (Can also be added like [(x1,y1), (x2,y2), (x3,y3), ... ]
poly1 = c.add_polygon(
[(-8, 6, 7, 9), (... |
krosaen/ml-study | python-ml-book/ch04/ch04.ipynb | mit | import pandas as pd
from io import StringIO
csv_data = '''A,B,C,D
1.0,2.0,3.0,4.0
5.0,6.0,,8.0
10.0,11.0,12.0,'''
df = pd.read_csv(StringIO(csv_data))
df
"""
Explanation: Chapter 4: building good training sets
Handling missing values
Let's start by constructing a simple dataset with some missing values.
End of expla... |
ericmjl/Network-Analysis-Made-Simple | notebooks/02-algorithms/03-structures.ipynb | mit | from IPython.display import YouTubeVideo
YouTubeVideo(id="3DWSRCbPPJs", width="100%")
"""
Explanation: Introduction
End of explanation
"""
from nams import load_data as cf
G = cf.load_physicians_network()
"""
Explanation: If you remember, at the beginning of this book,
we saw a quote from John Quackenbush that ess... |
phanrahan/magmathon | notebooks/signal-generator/solutions/DDS.ipynb | mit | import math
import numpy as np
def sine(x):
return np.sin(2 * math.pi * x)
x = np.linspace(0., 1., num=256, endpoint=False)
import magma as m
m.set_mantle_target('ice40')
import mantle
def DefineDDS(n, has_ce=False):
class _DDS(m.Circuit):
name = f'DDS{n}'
IO = ['I', m.In(m.UInt(n)), "O", m... |
karlstroetmann/Algorithms | Python/Chapter-07/2-3-Trees-Frame.ipynb | gpl-2.0 | import graphviz as gv
"""
Explanation: 2-3 Trees
End of explanation
"""
class TwoThreeTree:
sNodeCount = 0
def __init__(self):
TwoThreeTree.sNodeCount += 1
self.mID = TwoThreeTree.sNodeCount
def getID(self):
return self.mID
def isNil(self):
retur... |
briangoodwin/nd101_udacityDeepLearning | tv-script-generation/dlnd_tv_script_generation.ipynb | gpl-3.0 | """
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... |
mne-tools/mne-tools.github.io | 0.23/_downloads/fcc5782db3e2930fc79f31bc745495ed/60_ctf_bst_auditory.ipynb | bsd-3-clause | # Authors: Mainak Jas <mainak.jas@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# Jaakko Leppakangas <jaeilepp@student.jyu.fi>
#
# License: BSD (3-clause)
import os.path as op
import pandas as pd
import numpy as np
import mne
from mne import combine_evoked
from mne.minimum_norm impor... |
google/edward2 | examples/notebooks/BayesianLSTM.ipynb | apache-2.0 | import numpy as np
import seaborn as sns
import pandas as pd
import tensorflow as tf
import edward2 as ed
import matplotlib.pyplot as plt
from tqdm import tqdm
from sklearn.model_selection import train_test_split, ParameterGrid
from tensorflow.keras.preprocessing import sequence
import embedded_reber_grammar as erg
d... |
bdestombe/flopy-1 | examples/Notebooks/flopy3_uzf_example.ipynb | bsd-3-clause | %matplotlib inline
import os
import sys
import glob
import platform
import shutil
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import flopy
print(sys.version)
print('numpy version: {}'.format(np.__version__))
print('matplotlib version: {}'.format(mpl.__version__))
pri... |
smharper/openmc | examples/jupyter/unstructured-mesh-part-ii.ipynb | mit | import os
from IPython.display import Image
import openmc
import openmc.lib
assert(openmc.lib._dagmc_enabled())
"""
Explanation: Unstructured Mesh Tallies with CAD Geometry in OpenMC
In the first notebook on this topic, we looked at how to set up a tally using an unstructured mesh in OpenMC.
In this notebook, we will... |
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