repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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tiagoantao/biopython-notebook | notebooks/05 - Sequence Input and Output.ipynb | mit | from Bio import SeqIO
help(SeqIO)
"""
Explanation: Source of the materials: Biopython Tutorial and Cookbook (adapted)
Sequence Input/Output
In this notebook we'll discuss in more detail the Bio.SeqIO module, which was briefly introduced before. This aims to provide a simple interface for working with assorted sequenc... |
junhwanjang/DataSchool | Lecture/14. 선형 회귀 분석/1) 회귀 분석용 가상 데이터 생성 방법.ipynb | mit | from sklearn.datasets import make_regression
X, y, c = make_regression(n_samples=10, n_features=1, bias=0, noise=0, coef=True, random_state=0)
print("X\n", X)
print("y\n", y)
print("c\n", c)
plt.scatter(X, y, s=100)
plt.show()
"""
Explanation: 회귀 분석용 가상 데이터 생성 방법
Scikit-learn 의 datasets 서브 패키지에는 회귀 분석 시험용 가상 데이터를 생성하... |
jmhsi/justin_tinker | data_science/lendingclub_bak/dataprep_and_modeling/1.0.0_pytorch_regression.ipynb | apache-2.0 | platform = 'lendingclub'
use_cuda = True
dtype = torch.cuda.FloatTensor
save_path = "model_dump/nn_1_0_0/"
store = pd.HDFStore(
dc.home_path+'/justin_tinkering/data_science/lendingclub/{0}_store.h5'.
format(platform),
append=True)
loan_info = store['train_filtered_columns']
columns = loan_info.columns.val... |
GoogleCloudPlatform/tf-estimator-tutorials | 01_Regression/05.0 - TF Regression Model - Custom Estimator.ipynb | apache-2.0 | MODEL_NAME = 'reg-model-04'
TRAIN_DATA_FILES_PATTERN = 'data/train-*.csv'
VALID_DATA_FILES_PATTERN = 'data/valid-*.csv'
TEST_DATA_FILES_PATTERN = 'data/test-*.csv'
RESUME_TRAINING = False
PROCESS_FEATURES = True
EXTEND_FEATURE_COLUMNS = True
MULTI_THREADING = True
"""
Explanation: Steps to use the TF Experiment APIs... |
probml/pyprobml | notebooks/misc/git_lfs_colab_demo.ipynb | mit | !curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
!sudo apt-get install git-lfs
"""
Explanation: <a href="https://colab.research.google.com/github/always-newbie161/probml-notebooks/blob/lfs_git/notebooks/git_lfs_colab_demo.ipynb" target="_parent"><img src="https://colab.re... |
pybel/pybel-notebooks | algorithms/Subgraph Comparison.ipynb | apache-2.0 | import logging
import os
import sys
import time
from collections import Counter, defaultdict
from operator import itemgetter
import matplotlib.pyplot as plt
import networkx as nx
import pandas as pd
import numpy as np
import seaborn as sns
import pybel
import pybel_tools as pbt
from pybel.constants import *
from pybe... |
oditorium/blog | iPython/ECB-DataImport.ipynb | agpl-3.0 | import urllib.request as req
class DataImport:
""" web data import base class
delimiter_row - if files are parsed, what delimits rows (default "\n")
delimiter_col - if files are parsed, what delimits cols (default ",")
DEPENDENCIES
urllib.request
"""
__version__ = "... |
abezuglov/ANN | Storm-Surge/notebooks/ANN Analysis on synthetic hurricanes.ipynb | gpl-3.0 | import scipy
import scipy.stats
diff = (network_out-true_out[:,8:])
#print(diff.shape)
y = diff[:,5]
print(y.shape)
#y = np.square(y)
x = np.arange(-3,3,0.01)
size = diff.shape[0]
h = plt.hist(y, bins=100, color='w')
plt.xlim(-3,3)
plt.ylim(0,1000)
dist_names = ['t']
for dist_name in dist_names:
dist = getattr... |
marcelomiky/PythonCodes | scikit-learn/scikit-learn-book/Chapter 1 (2nd ed.) - A Gentle Introduction to Machine Learning with Python and Scikit-learn.ipynb | mit | %pylab inline
"""
Explanation: A Gentle Introduction to Machine Learning with Python and Scikit-learn
Guillermo Moncecchi, Diego Garat, Raúl Garreta
This Python 3 IPython notebook is based on the planned version of the Second Edition of the "Learning Scikit-learn: Machine Learning with Python". We do not know if this ... |
ronnydw/data-science-projects | historical-merchandise-export-USA-vs-CHN/evolution-merchandise-export-by-USA-CHN.ipynb | mit | import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
data = pd.read_csv('./world-development-indicators/Indicators.csv')
data.shape
data.head()
"""
Explanation: Evolution of the worlds biggest exporters: USA vs China
Load the Dataset and select the indicator and countries
End of expla... |
google-research/google-research | time_varying_optimization/tvsdp.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... |
willettk/insight | notebooks/neural_networks_and_deep_learning.ipynb | apache-2.0 | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
import random
"""
Explanation: Work with http://neuralnetworksanddeeplearning.com/
End of explanation
"""
def sigmoid(z):
return 1./(1. + np.exp(-z))
def sigmoid_vector(w,x,b):
return 1./(1. + np.exp(-1 * np.sum(w * x) - b)... |
yevheniyc/Projects | 1j_NLP_Python/ex02.ipynb | mit | x = "Rinôçérôse screams flow not unlike an encyclopædia, \
'TECHNICIÄNS ÖF SPÅCE SHIP EÅRTH THIS IS YÖÜR CÄPTÅIN SPEÄKING YÖÜR ØÅPTÅIN IS DEA̋D' to Spın̈al Tap."
type(x)
"""
Explanation: Exercise 02: concerns about characters
The following shows examples of how to use codecs and normalize unicode, and draws heavily fro... |
eneskemalergin/Data_Structures_and_Algorithms | Chapter3/3-Sets_and_Maps.ipynb | gpl-3.0 | from linearset import Set
smith = Set()
smith.add("CSCI-112")
smith.add("MATH-121")
smith.add("HIST-340")
smith.add("ECON-101")
robert = Set()
robert.add('POL-101')
robert.add('ANTH-230')
robert.add('CSCI-112')
robert.add('ECON-101')
if smith == robert:
print("Smith and Robert are taking the same courses.")
else... |
OceanPARCELS/parcels | parcels/examples/documentation_stuck_particles.ipynb | mit | from parcels import FieldSet, ParticleSet, JITParticle, AdvectionRK4, Variable, Field
from glob import glob
import numpy as np
import xarray as xr
from datetime import timedelta as delta
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from matplotlib.... |
liumengjun/cn-deep-learning | tutorials/intro-to-tflearn/TFLearn_Digit_Recognition.ipynb | mit | # Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
"""
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This... |
YangDS/recommenders | notebooks/Example 2 Collaborative Filtering Recommenders.ipynb | gpl-3.0 | import pandas as pd
from scipy.spatial.distance import cosine
# Data was already dlownloaded.
data = pd.read_csv('data/lastfm/lastfm-matrix-germany.csv')
# check out the data set you can do so using data.head():
data.head(6).ix[:,2:10]
"""
Explanation: Collaborative Filtering with Python (Python 2.7)
Originate from ... |
dianafprieto/SS_2017 | 05_NB_VTKPython_Scalar.ipynb | mit | %gui qt
import vtk
from vtkviewer import SimpleVtkViewer
#help(vtk.vtkRectilinearGridReader())
"""
Explanation: <img src="imgs/header.png">
Visualization techniques for scalar fields in VTK + Python
Recap: The VTK pipeline
<img src="imgs/vtk_pipeline.png", align=left>
$~$
Visualizing data within a rectilinear grid
The... |
tuanavu/coursera-university-of-washington | machine_learning/2_regression/assignment/week1/week-1-simple-regression-assignment-exercise.ipynb | mit | import sys
sys.path.append('C:\Anaconda2\envs\dato-env\Lib\site-packages')
import graphlab
"""
Explanation: Regression Week 1: Simple Linear Regression
In this notebook we will use data on house sales in King County to predict house prices using simple (one input) linear regression. You will:
* Use graphlab SArray and... |
esa-as/2016-ml-contest | HouMath/Face_classification_HouMath_XGB_02.ipynb | apache-2.0 | %matplotlib inline
import pandas as pd
from pandas.tools.plotting import scatter_matrix
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
import matplotlib.colors as colors
import xgboost as xgb
import numpy as np
from sklearn.metrics import confusion_matrix, f1_score, accuracy_score
from ... |
danielfather7/teach_Python | lecture/06.Procedural_Python.part3.ipynb | gpl-3.0 | instructors = ['Dave', 'Jim', 'Dorkus the Clown']
if 'Dorkus the Clown' in instructors:
print('#fakeinstructor')
"""
Explanation: Procedural programming in python
Topics
Flow control, part 2
Functions
In class exercise:
Functionalize this!
From nothing to something:
Pairwise correlation between rows in a pandas ... |
xtr33me/deep-learning | dcgan-svhn/DCGAN.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... |
mkcor/advanced-pandas | notebooks/04_testing.ipynb | cc0-1.0 | import pandas as pd
df = pd.read_csv('../data/tidy_who.csv')
df.sample(5)
"""
Explanation: Testing for data analysis
In a data analysis context, we want to test our code, as usual, but also our data (i.e., expected schema; e.g., data types) and our statistics (i.e., expected properties of distributions; e.g., value ... |
Benedicto/ML-Learning | Clustering_2_kmeans-with-text-data_blank.ipynb | gpl-3.0 | import graphlab
import matplotlib.pyplot as plt
import numpy as np
import sys
import os
from scipy.sparse import csr_matrix
%matplotlib inline
'''Check GraphLab Create version'''
from distutils.version import StrictVersion
assert (StrictVersion(graphlab.version) >= StrictVersion('1.8.5')), 'GraphLab Create must be ve... |
gfeiden/Notebook | Daily/20160712_Seasonal_Variation_Halflife.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Seasonal Time Dilation due to Earth's Eccentric Orbit
Just curious to see how large the effect is.
End of explanation
"""
d_perihelion = 1.47098074e11 # m
d_aphelion = 1.52097701e11 # m
d_average = 1.4959787e11 # m
GMsun... |
modin-project/modin | examples/tutorial/jupyter/execution/pandas_on_ray/local/exercise_4.ipynb | apache-2.0 | import modin.pandas as pd
import numpy as np
from tqdm import tqdm
from modin.config import ProgressBar
ProgressBar.enable()
frame_data = np.random.randint(0, 100, size=(2**18, 2**8))
df = pd.DataFrame(frame_data).add_prefix("col")
"""
Explanation: <center><h2>Scale your pandas workflows by changing one line of code<... |
google/assortment-quality-for-shopping-ads | Colab - Setup Guide.ipynb | apache-2.0 | #@title Copyright 2021 Google LLC.
# 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,... |
charlesreid1/empirical-model-building | ipython/Factorial - Two-Level Four-Factor Design.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
from numpy.random import rand, seed
import seaborn as sns
import scipy.stats as stats
from matplotlib.pyplot import *
effects = {}
effects[0] = {'x0': 0.0}
effects[1] = {'x1': 0.4,
'x2': -7.6,
'x3': 14.1,
'x4': 66.7}
... |
barjacks/pythonrecherche | 06 Rückblick, Scraping Beispiele/01 Rückblick For-Loop-Übungen.ipynb | mit | lst = [2, 3, 5, 7]
for prime in lst:
print(prime)
"""
Explanation: 10 For-Loop-Rückblick-Übungen
In den Teilen der folgenden Übungen habe ich den Code mit "XXX" ausgewechselt. Es gilt in allen Übungen, den korrekten Code auszuführen und die Zelle dann auszuführen.
1.Drucke alle diese Prim-Zahlen aus:
End of expla... |
ctherien/pysptools | pysptools/ml/nbex/nbex_xgb_cv.ipynb | apache-2.0 | %matplotlib inline
from __future__ import print_function
import os
import os.path as osp
import numpy as np
import pysptools.ml as ml
import pysptools.skl as skl
from sklearn.model_selection import train_test_split
home_path = os.environ['HOME']
source_path = osp.join(home_path, 'dev-data/CZ_hsdb')
result_path = ... |
muatik/my-coding-challenges | algorithm-questions.ipynb | mit | def rev(s):
b, e = 0, 0
for i in range(len(s)):
c = s[i]
if c != " " and i+1 != len(s):
e+=1
continue
if i+1 == len(s):
e += 1
for j in range((e-b)//2):
s[b+j], s[e-1-j]=s[e-1-j], s[b+j]
e +=... |
SciTools/cube_browser | doc/write_your_own/components/ValueCatcher.ipynb | bsd-3-clause | import ipywidgets
import IPython.display
"""
Explanation: Text Input Box
Example showing how to construct a text input widget that can be used to capture a string (such as a filepath).
End of explanation
"""
args = ipywidgets.Text(
description='Input string:',
value='cube')
IPython.display.display(args)
""... |
SylvainCorlay/bqplot | examples/Tutorials/Brush Interval Selector.ipynb | apache-2.0 | import numpy as np
from ipywidgets import Layout, HTML, VBox
import bqplot.pyplot as plt
"""
Explanation: Linking Plots Using Brush Interval Selector
Details on how to use the brush interval selector can be found in this notebook.
Brush interval selectors can be used where continuous updates are not desirable (for ex... |
bzamecnik/ml | snippets/scikit-learn/OneHotEncoder_decoding.ipynb | mit | from sklearn.preprocessing import OneHotEncoder
import numpy as np
orig = np.array([6, 9, 8, 2, 5, 4, 5, 3, 3, 6])
"""
Explanation: Decoding values from OneHotEncoder
Let's say we encoded a categorical variable into one-hot encoding, ie. a binary vector with each column indicating one of the values. The scikit-learn ... |
billzhao1990/CS231n-Spring-2017 | assignment2/.ipynb_checkpoints/Dropout-checkpoint.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... |
ubcgif/gpgLabs | notebooks/seismic/Seis_Refraction.ipynb | mit | plotWavelet()
"""
Explanation: Interpretation and data acquisition strategies of seismic refraction data
In the <a href="https://www.3ptscience.com/app/SeismicRefraction">3pt Science app</a>, you explored the expected arrival times for refractions and reflections from a two-layer over a half-space model.
In this note... |
mitdbg/modeldb | client/workflows/demos/registry/multiple-framework-example-end-to-end.ipynb | mit | from __future__ import print_function
import warnings
from sklearn.exceptions import ConvergenceWarning
warnings.filterwarnings("ignore", category=ConvergenceWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
import itertools
import os
import time
import six
import numpy as np
import pandas as pd
i... |
spro/practical-pytorch | glove-word-vectors/glove-word-vectors.ipynb | mit | import torch
import torchtext.vocab as vocab
glove = vocab.GloVe(name='6B', dim=100)
print('Loaded {} words'.format(len(glove.itos)))
"""
Explanation: Practical PyTorch: Exploring Word Vectors with GloVe
When working with words, dealing with the huge but sparse domain of language can be challenging. Even for a small... |
mne-tools/mne-tools.github.io | 0.17/_downloads/f44d9c0360e7806c2f8988ccd7a3b432/plot_point_spread.ipynb | bsd-3-clause | import os.path as op
import numpy as np
from mayavi import mlab
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse
from mne.simulation import simulate_stc, simulate_evoked
"""
Explanation: Corrupt known signal with point spread
The aim of this tutorial is to... |
daviddesancho/MasterMSM | examples/mueller_potential/mueller_potential_openmm.ipynb | gpl-2.0 | %matplotlib inline
%load_ext autoreload
%autoreload 2
import time
import numpy as np
import h5py
import scipy.stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="ticks", color_codes=True, font_scale=1.5)
sns.set_style({"xtick.direction": "in", "ytick.direction": "in"})
from collections import Ord... |
rvuduc/cse6040-ipynbs | 08--pandas-seaborn.ipynb | bsd-3-clause | import pandas as pd
"""
Explanation: CSE 6040, Fall 2015 [08]: Data analysis and visualization
In todays class, we will first introduce a data analysis tools called Pandas,
and then show how to visualize the data using a module called Seaborn.
Most of the examples come from
Pandas tutorial
and
Seaborn tutorial.
Part ... |
QuantCrimAtLeeds/PredictCode | examples/Self-exciting point processes 2.ipynb | artistic-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import datetime
import open_cp
"""
Explanation: A grid based model SEPP model
In (1), an explicitly grid-based self-exciting point process (SEPP) model was considered. The paper (1) is mostly concerned with two field-trials of the SEPP or epidemic-... |
ivannz/crossing_paper2017 | experiments/full_experiment.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: the Monte Carlo experiment
End of explanation
"""
import time, gzip
import os, pickle
def save(obj, path, prefix=None):
prefix_ = "" if prefix is None else "%s_"%(prefix,)
filename_ = os.path.join(path, "%s%s.gz"%(prefix... |
mdiaz236/DeepLearningFoundations | tensorboard/Anna_KaRNNa_Summaries.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
evanmiltenburg/python-for-text-analysis | Chapters/Chapter 11 - Functions and scope.ipynb | apache-2.0 | print("Happy Birthday to you!")
print("Happy Birthday to you!")
print("Happy Birthday, dear Emily.")
print("Happy Birthday to you!")
"""
Explanation: Chapter 11: Functions and scope
We use an example from this website to show you some of the basics of writing a function.
We use some materials from this other Python c... |
cfcdavidchan/Deep-Learning-Foundation-Nanodegree | face_generation/dlnd_face_generation.ipynb | mit | data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
"""
Explanation: Face Generation
In this project, you'll use generative adv... |
exowanderer/SpitzerDeepLearningNetwork | Notebooks/tensorflow_CoreTF_Spitzer - ELU.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, Min... |
MonicaGutierrez/PracticalMachineLearningClass | exercises/06-Titanic_cross_validation.ipynb | mit | import pandas as pd
url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/titanic.csv'
titanic = pd.read_csv(url, index_col='PassengerId')
titanic.head()
"""
Explanation: Exercise 06
Data preparation and model evaluation exercise with Titanic data
We'll be working with a dataset from Kaggle's Titanic c... |
zakandrewking/cobrapy | documentation_builder/sampling.ipynb | lgpl-2.1 | from cobra.test import create_test_model
from cobra.flux_analysis import sample
model = create_test_model("textbook")
s = sample(model, 100)
s.head()
"""
Explanation: Flux sampling
Basic usage
The easiest way to get started with flux sampling is using the sample function in the flux_analysis submodule. sample takes a... |
dnc1994/MachineLearning-UW | ml-regression/blank/week-3-polynomial-regression-assignment-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 3: Assessing Fit (polynomial regression)
In this notebook you will compare different regression models in order to assess which model fits best. We will be using polynomial regression as a means to examine this topic. In particular you will:
* Write a function to take a... |
IS-ENES-Data/submission_forms | test/forms/test/test_testsuite_1234.ipynb | apache-2.0 | from dkrz_forms import form_widgets
form_widgets.show_status('form-submission')
MY_LAST_NAME = "...." # e.gl MY_LAST_NAME = "schulz"
#-------------------------------------------------
from dkrz_forms import form_handler, form_widgets
form_info = form_widgets.check_pwd(MY_LAST_NAME)
sf = form_handler.init_form(form_... |
fluffy-hamster/A-Beginners-Guide-to-Python | A Beginners Guide to Python/06. Variable Names.ipynb | mit | print = 10.20 # Rule 1: "print" is a reserved word in Python and therefore we cannot use it.
0sales = 10.20 # Rule 2: cannot start a name with a number.
this has spaces = 10.20 # Rule 2: space character is punctation.
thisnamehasa+init = 10.20 # Rule 3: Fails because "+" is a special charac... |
sdpython/ensae_teaching_cs | _doc/notebooks/exams/td_note_2019_2.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: 1A.e - Enoncé 23 octobre 2018 (2)
Correction du second énoncé de l'examen du 23 octobre 2018. L'énoncé propose une méthode pour renseigner les valeurs manquantes dans une base de deux variables.
End of explanation
"""
import numpy.rando... |
fullmetalfelix/ML-CSC-tutorial | ACSF-Dimer.ipynb | gpl-3.0 | # --- INITIAL DEFINITIONS ---
from sklearn.neural_network import MLPRegressor
import numpy, math, random
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from ase import Atoms
from visualise import view
# cutoff for all ACSF
Rcut = 5.0
# cutoff function
def fcut(r):
if r >= Rcut: return 0
... |
paoloRais/lightfm | examples/movielens/example.ipynb | apache-2.0 | import numpy as np
from lightfm.datasets import fetch_movielens
movielens = fetch_movielens()
"""
Explanation: An implicit feedback recommender for the Movielens dataset
Implicit feedback
For some time, the recommender system literature focused on explicit feedback: the Netflix prize focused on accurately reproducin... |
DJCordhose/ai | notebooks/2019_tf/autoencoders_intro.ipynb | mit | import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)
print(tf.__version__)
from tensorflow.keras.datasets import mnist
import numpy as np
(x_train, _), (x_test, _) = mnist.load_data()
"""
Explanation: <a href="https://colab.research.google.com/github/DJCordhose/ai/blob/master/notebooks/2019_tf/autoencod... |
rohanfray/Wk6_Proj | IS620_wk6_project.ipynb | mit | nReviewers = 24983
nJokes = 100
G=nx.generators.empty_graph(nReviewers+nJokes)
G.clear()
G.name="Jester Dataset 1"
Jokes = []
for i in range(0,100):
Jokes.append('Joke' + str(i))
with open('jester-data-1.csv', 'rb') as csvfile:
book = csv.reader(csvfile)
i = 0
for row in book:
for j in range(... |
tofgarion/lp-visu | lp_visu/ilp_visu_ex.ipynb | gpl-3.0 | from lp_visu import LPVisu
"""
Explanation: You can use LPVisu to visualize Integer Linear Programming problems in Jupyter notebooks. First import the LPVisu class:
End of explanation
"""
# problem definition
A = [[1.0, 0.0], [1.0, 2.0], [2.0, 1.0]]
b = [8.0, 15.0, 18.0]
c = [-4.0, -3.0]
x1_bounds = (0, None)
x2_bo... |
llulai/deep-learning | autoencoder/Convolutional_Autoencoder_Solution.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)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
"""
Explanation: C... |
bryanwweber/thermostate | docs/Tutorial.ipynb | bsd-3-clause | from thermostate import State, Q_, units, set_default_units
"""
Explanation: Tutorial
This tutorial will guide you in the basic use of ThermoState. The ThermoState
package is designed to ease the evaluation of thermodynamic properties for
common substances used in Mechanical Engineering courses. Rather than looking up... |
CyberCRI/dataanalysis-herocoli-redmetrics | v1.52.2/Functions/0.2 GF French localization.ipynb | cc0-1.0 | %run "../Functions/0.1 GF English localization.ipynb"
"""
Explanation: French localization
End of explanation
"""
processGFormFR = not ('gformFR1522' in globals())
if processGFormFR:
try:
gformFR1522 = pd.read_csv(\
frPath,\
dtype=str,\
... |
aschaffn/phys202-2015-work | days/day12/Integration.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
"""
Explanation: Numerical Integration
Learning Objectives: Learn how to numerically integrate 1d and 2d functions that are represented as Python functions or numerical arrays of data using scipy.integrate.
This lesson was orgi... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/introduction_to_tensorflow/labs/3_keras_sequential_api_vertex.ipynb | apache-2.0 | import datetime
import os
import shutil
import numpy as np
import pandas as pd
import tensorflow as tf
from google.cloud import aiplatform
from matplotlib import pyplot as plt
from tensorflow import keras
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.layers import Dense, DenseFeatures
from t... |
alberto-antonietti/nest-simulator | doc/model_details/IAF_neurons_singularity.ipynb | gpl-2.0 | import sympy as sp
sp.init_printing(use_latex=True)
from sympy.matrices import zeros
tau_m, tau_s, C, h = sp.symbols('tau_m, tau_s, C, h')
"""
Explanation: IAF neurons singularity
This notebook describes how NEST handles the singularities appearing in the ODE's of integrate-and-fire model neurons with alpha- or expone... |
leosartaj/scipy-2016-tutorial | tutorial_exercises/04-Matrices.ipynb | bsd-3-clause | rot = Matrix([[r*cos(theta), -r*sin(theta)],
[r*sin(theta), r*cos(theta)]])
rot
"""
Explanation: Matrices
The SymPy Matrix object helps us with small problems in linear algebra.
End of explanation
"""
rot.det()
rot.inv()
rot.singular_values()
"""
Explanation: Standard methods
End of explanation
"""... |
linamnt/studyGroup | lessons/python/intro/IntroPython_HG_Code_along.ipynb | apache-2.0 | # % is the modulus operator
# x % y -> return the remainder of x / y
"""
Explanation: Intro to Python
UofT Coders, Sep 26 2019
Instructor: Haidy Giratallah
Lesson repeat from: UofT coder lessons (https://github.com/UofTCoders/studyGroup/blob/gh-pages/lessons/python/intro/IntroPython-AH.ipynb)
Before getting started... |
ContinuumIO/pydata-apps | Section_0_Introduction.ipynb | mit | from IPython.display import IFrame
IFrame('http://blaze.pydata.org/en/latest/', width='100%', height=350)
"""
Explanation: <img src="images/continuum_analytics_logo.png"
alt="Continuum Logo",
align="right",
width="30%">
Bu... |
ecell/ecell4-notebooks | en/tutorials/tutorial04.ipynb | gpl-2.0 | from ecell4_base.core import *
"""
Explanation: 4. How to Run a Simulation
In sections 2 and 3, we explained the way to build a model and to setup the intial state. Now, it is the time to run a simulation. Corresponding to World classes, six Simulator classes are there: spatiocyte.SpatiocyteSimulator, egfrd.EGFRDSimul... |
bwinkel/cygrid | notebooks/B01_OTF-map_exposure_calculator.ipynb | gpl-3.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import matplotlib.pyplot as pl
from astropy.wcs import WCS
from scipy import constants
import cygrid
np.set_printoptions(precision=1)
def gaincurve(elev, a0, a1, a2):
'''
Radio telescope ... |
kpolimis/kpolimis.github.io-src | output/downloads/notebooks/google_location_history.ipynb | gpl-3.0 | with open('LocationHistory.json', 'r') as fh:
raw = json.loads(fh.read())
ld = pd.DataFrame(raw['locations'])
del raw #free up some memory
# convert to typical units
ld['latitudeE7'] = ld['latitudeE7']/float(1e7)
ld['longitudeE7'] = ld['longitudeE7']/float(1e7)
ld['timestampMs'] = ld['timestampMs'].map(lambda x: ... |
fabriziocosta/EDeN_examples | graph_format.ipynb | gpl-2.0 | %matplotlib inline
%load_ext autoreload
%autoreload 2
from eden.util import configure_logging
import logging
logger = logging.getLogger()
configure_logging(logger,verbosity=1)
import pylab as plt
import networkx as nx
G=nx.Graph()
G.add_node(0, label='A')
G.add_node(1, label='B')
G.add_node(2, label='C')
G.add_edge(... |
IanHawke/Southampton-PV-NumericalMethods-2016 | solutions/03-Root-finding.ipynb | mit | from __future__ import division
import numpy
from matplotlib import pyplot
%matplotlib notebook
def f(Rs):
return 3.5 -3.2*(numpy.exp((10+3*Rs)/20.0 - 1.0) - 1.0) - (10.0 + 3.0*Rs)/300.0 - 3
Rs = numpy.linspace(0, 10)
pyplot.figure(figsize=(10,6))
pyplot.plot(Rs, f(Rs))
pyplot.xlabel(r"$R_s$")
pyplot.ylabel(r"$f$... |
crystalzhaizhai/cs207_yi_zhai | lectures/L12/L12.ipynb | mit | import collections
Card = collections.namedtuple('Card', ['rank', 'suit'])
class FrenchDeck():
ranks = [str(n) for n in range(2, 11)] + list('JQKA')
suits = 'spades diamonds clubs hearts'.split()
def __init__(self):
self._cards = [Card(rank, suit) for suit in self.suits for rank in self.ranks... |
kgrodzicki/machine-learning-specialization | course-2-regression/notebooks/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.... |
deepmind/deepmind-research | enformer/enformer-usage.ipynb | apache-2.0 | import tensorflow as tf
# Make sure the GPU is enabled
assert tf.config.list_physical_devices('GPU'), 'Start the colab kernel with GPU: Runtime -> Change runtime type -> GPU'
!pip install kipoiseq==0.5.2 --quiet > /dev/null
# You can ignore the pyYAML error
"""
Explanation: Copyright 2021 DeepMind Technologies Limit... |
flowersteam/explauto | notebook/DivaDMP.ipynb | gpl-3.0 | from __future__ import print_function
import os
import numpy as np
from explauto.environment.diva import DivaEnvironment
diva_cfg = dict(diva_path=os.path.join(os.getenv("HOME"), 'software/DIVAsimulink/'),
synth="octave",
m_mins = np.array([-1]*7), # motor bounds
m_maxs... |
crhaithcock/RushHour | RushHourPy/analysis_prototypes/solution_path_nodes_prototype.ipynb | cc0-1.0 | %matplotlib inline
import os
import pandas as pd
import copy
from IPython.display import HTML
import sqlite3 as db
import networkx as nx
import collections
con = db.connect('C:/Users/cliff/workspace/rushHour/Data Model and SQL/Schema Definition and Bulk Load/rush_hour.db')
c = con.cursor()
"""
Explanation: The goals... |
ccasotto/rmtk | rmtk/vulnerability/derivation_fragility/NLTHA_on_SDOF/MSA_on_SDOF.ipynb | agpl-3.0 | import MSA_on_SDOF
from rmtk.vulnerability.common import utils
import numpy as np
import MSA_utils
%matplotlib inline
"""
Explanation: Multiple Stripe Analysis (MSA) for Single Degree of Freedom (SDOF) Oscillators
In this method, a single degree of freedom (SDOF) model of each structure is subjected to non-linear tim... |
squishbug/DataScienceProgramming | 07-Data-Visualization/CrimeData_descriptive_statistics_and_visualization.ipynb | cc0-1.0 | import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
# load data set
df = pd.read_csv('/home/data/APD/COBRA-YTD-multiyear.csv.gz')
print "Shape of table: ", df.shape
"""
Explanation: Atlanta Police Department
The Atlanta Police Department provides Part 1 crime data at http://www... |
maxday/spark.samples | streaming-twitter/notebook/Twitter Sentiment with Pixiedust.ipynb | apache-2.0 | import pixiedust
pixiedust.installPackage("https://github.com/ibm-cds-labs/spark.samples/raw/master/dist/streaming-twitter-assembly-1.6.jar")
"""
Explanation: Install Twitter Sentiment with Watson scala library from Github
End of explanation
"""
from pixiedust.utils.javaBridge import *
demo = JavaWrapper("com.ibm.cd... |
mne-tools/mne-tools.github.io | dev/_downloads/5b9edf9c05aec2b9bb1f128f174ca0f3/40_cluster_1samp_time_freq.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats
import mne
from mne.time_frequency import tfr_morlet
from mne.stats import permutation_cluster_1samp_te... |
rvernagus/data-science-notebooks | Data Science From Scratch/11 - Machine Learning.ipynb | mit | def split_data(data, prob):
"""split data into fractions [prob, 1 - prob]"""
results = [], []
for row in data:
results[0 if random.random() < prob else 1].append(row)
return results
"""
Explanation: Modeling
Models are specifications of a mathematical (or probabilistic) relationship between var... |
cdawei/digbeta | dchen/music/multilabel_example.ipynb | gpl-3.0 | %matplotlib inline
import os, sys, time
import pickle as pkl
import numpy as np
import pandas as pd
import sklearn as sk
import cython
import itertools
from scipy.io import arff
from scipy.optimize import minimize
from scipy.optimize import check_grad
from sklearn.linear_model import LogisticRegression
from sklearn.... |
MIT-LCP/mimic-code | mimic-iii/benchmark/benchmark-concepts.ipynb | mit | import os
import re
import psycopg2
import getpass
from collections import OrderedDict
# database config
sqluser=getpass.getuser()
# keep sqlpass blank if using peer authentication
sqlpass=''
# database
sqldb='mimic'
sqlschema='public,mimiciii'
query_schema = 'set search_path to ' + sqlschema + ';'
if (not sqlpass)... |
katie-truong/Udacity-DA | Project4/Stroop effect.ipynb | mit | import pandas as pd
import numpy as np
import scipy.stats as st
import matplotlib.pyplot as plt
import seaborn as sns
import math
"""
Explanation: Test a Perceptual Phenomenon - Stroop data
Katie Truong
End of explanation
"""
stroopdata = pd.read_csv("stroopdata.csv")
stroopdata
"""
Explanation: Introduction
In a S... |
weixuanfu/tpot | tutorials/MAGIC Gamma Telescope/MAGIC Gamma Telescope.ipynb | lgpl-3.0 | # Import required libraries
from tpot import TPOTClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
#Load the data
telescope=pd.read_csv('MAGIC Gamma Telescope Data.csv')
telescope.head(5)
"""
Explanation: MAGIC Gamma Telescope - TPOT Classification Study
The below... |
bioasp/meneco | meneco.ipynb | gpl-3.0 | pip install meneco
"""
Explanation: Meneco demo
First you need to install Meneco. For example with pip ...
End of explanation
"""
from clyngor.as_pyasp import TermSet,Atom
from urllib.request import urlopen
from meneco.meneco import query, utils, sbml
"""
Explanation: then you can import the necessary modules ...
E... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/cmip6/models/noresm2-lme/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-lme', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-LME
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Ra... |
jorisvandenbossche/DS-python-data-analysis | notebooks/case2_observations.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('seaborn-whitegrid')
"""
Explanation: <p><font size="6"><b>CASE - Observation data</b></font></p>
© 2021, Joris Van den Bossche and Stijn Van Hoey (jorisva... |
empirical-org/WikipediaSentences | notebooks/BERT-1 Experiments.ipynb | agpl-3.0 | import torch
from pytorch_transformers.tokenization_bert import BertTokenizer
from pytorch_transformers.modeling_bert import BertForSequenceClassification
BERT_MODEL = 'bert-base-uncased'
BATCH_SIZE = 16 if "base" in BERT_MODEL else 2
GRADIENT_ACCUMULATION_STEPS = 1 if "base" in BERT_MODEL else 8
tokenizer = BertTok... |
rouxinol/HybridSystem | Simulation - CPW-Filter-qubit/.ipynb_checkpoints/SimPSI - Qubit-Cavity-NR Dispersive limit-checkpoint.ipynb | cc0-1.0 | from sympy import *
init_printing()
from sympsi import *
from sympsi.boson import *
from sympsi.pauli import *
"""
Explanation: Symbolic derivation qubit-cavity Hamiltonian
<style>
p {
font-family: "Liberation Serif", serif;
font-size: 12pt;
}
</style>
Based on: J. R. Johansson (robert@riken.jp), http://jrjo... |
urinieto/msaf | examples/Run MSAF.ipynb | mit | from __future__ import print_function
import msaf
import librosa
import seaborn as sns
# and IPython.display for audio output
import IPython.display
# Setup nice plots
sns.set(style="dark")
%matplotlib inline
"""
Explanation: Running MSAF
The main MSAF functionality is demonstrated here.
End of explanation
"""
# C... |
buntyke/DeepLearningMOOC | Udacity/2_fullyconnected.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
import numpy as np
import tensorflow as tf
from six.moves import range
from __future__ import print_function
from six.moves import cPickle as pickle
"""
Explanation: Deep Learning
Assignment 2
Previously in 1_n... |
tkoyama010/getfem_presentation | doc/demo_tripod_alt.ipynb | cc0-1.0 | import getfem as gf
import numpy as np
"""
Explanation: この例では、GIDで生成されたメッシュを使用しています。メッシュは四面体要素です。
End of explanation
"""
file_msh = 'tripod.GiD.msh'
degree = 2
linear = False
incompressible = False # ensure that degree > 1 when incompressible is on..
E = 1e3
Nu = 0.3
Lambda = E*Nu/((1+Nu)*(1-2*Nu))
Mu = E/(2*(1+Nu))... |
cliburn/sta-663-2017 | scratch/Lecture11.ipynb | mit | %mkdir hello
%cd hello
%%file hello.cpp
#include <iostream>
using std::cout;
using std::endl;
int main() {
cout << "Hello, world" << endl;
}
! g++ hello.cpp -o hello
! ./hello
%cd ..
"""
Explanation: Using C++
Lecture 11 will cover the baiscs of C++ progamming.
Lecture 12 will cover how to c... |
AEW2015/PYNQ_PR_Overlay | Pynq-Z1/notebooks/Video_PR/Edge_Detection_Filter.ipynb | bsd-3-clause | from pynq.drivers.video import HDMI
from pynq import Bitstream_Part
from pynq.board import Register
from pynq import Overlay
Overlay("demo.bit").download()
"""
Explanation: Don't forget to delete the hdmi_out and hdmi_in when finished
Edge Detection Filter Example
In this notebook we will demonstrate a hardware accel... |
samuelsinayoko/kaggle-housing-prices | research/encoding_categorical_data.ipynb | mit | import pandas as pd
import numpy as np
"""
Explanation: Encoding Categorical Data
End of explanation
"""
data = pd.DataFrame(data=[[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]], columns=['feature 1', 'feature 2', 'feature 3'])
data
"""
Explanation: Simple data frame with categorical data
Represent each category as a... |
c22n/ion-channel-ABC | docs/examples/human-atrial/nygren_ina_original.ipynb | gpl-3.0 | import os, tempfile
import logging
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from ionchannelABC import theoretical_population_size
from ionchannelABC import IonChannelDistance, EfficientMultivariateNormalTransition, IonChannelAcceptor
from ionchannelABC.experimen... |
ronojoy/pylearn | presentations/.ipynb_checkpoints/PyCon-ReasoningUnderUncertainty-Lea-checkpoint.ipynb | mit | from lea import *
# the canonical random variable : a fair coin
faircoin = Lea.fromVals('Head', 'Tail')
# toss the coin a few times
faircoin.random(10)
# Amitabh Bachan's coin from Sholay
sholaycoin = Lea.fromVals('Head', 'Head')
# Amitabh always wins (and, heroically, sacrifices himself for Dharamendra!)
sholaycoi... |
googleinterns/bizview-semi-supervised-learning | Mixmatch/streetview_dataset/OCR_lib_test.ipynb | apache-2.0 | from OCR_lib import word_to_vec, reshape_embeddings, detect_text
import spacy
import tensorflow as tf
import numpy as np
from PIL import Image, ImageShow
import IPython.display as display
"""
Explanation: OCR_lib_test
This file includes test scripts for the OCR_lib functions.
End of explanation
"""
TEST_STRING = "Te... |
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