repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/recommendation_systems/labs/als_bqml_hybrid.ipynb | apache-2.0 | import os
PROJECT = "your-project-id-here" # REPLACE WITH YOUR PROJECT ID
# Do not change these
os.environ["PROJECT"] = PROJECT
%%bigquery --project $PROJECT
SELECT
processed_input,
feature,
TO_JSON_STRING(factor_weights),
intercept
FROM ML.WEIGHTS(MODEL movielens.recommender_16)
WHERE
(processed... |
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn | doc/notebooks/automaton.sum.ipynb | gpl-3.0 | import vcsn
ctx = vcsn.context('lal_char, q')
aut = lambda e: ctx.expression(e).standard()
"""
Explanation: automaton.sum(aut,algo="auto")
Build an automaton whose behavior is the sum of the behaviors of the input automata.
The algorithm has to be one of these:
"auto": default parameter, same as "standard" if paramet... |
mdeff/ntds_2017 | projects/reports/lastfm_recommendation/Report.ipynb | mit | %load_ext autoreload
%autoreload 1
import numpy as np
import pickle
import matplotlib.pyplot as plt
import scipy as sp
import pandas as pd
import os.path
import networkx as nx
from scipy.sparse import csr_matrix
from Dataset import Dataset
from plots import *
import os
from helpers import *
%matplotlib inline
"""
Exp... |
mir-group/flare | docs/source/tutorials/aps_tutorial.ipynb | mit | ! pip install --upgrade mir-flare
"""
Explanation: Introduction to FLARE: Fast Learning of Atomistic Rare Events
Jonathan Vandermause (jonathan_vandermause@g.harvard.edu)
<img src="https://github.com/mir-group/APS-2020-FLARE-Tutorial/blob/master/Tutorial_Images/flare_logo.png?raw=true" width="60%">
Learning objectives... |
encima/Comp_Thinking_In_Python | 3_Homework_Answers.ipynb | mit | is
"""
Explanation: When would 2 variables be equal but not have the same identity?
When they have different addresses in memory
What is the symbol for identity comparison?
End of explanation
"""
num_wheels >= 1024 and num_wheels < 2456
"""
Explanation: Which of the values below return True when used in the followi... |
tensorflow/docs-l10n | site/pt-br/tutorials/text/word_embeddings.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... |
mne-tools/mne-tools.github.io | dev/_downloads/81e58e463fcd949fd4ab7ab7ab8ef317/left_cerebellum_volume_source.ipynb | bsd-3-clause | # Author: Alan Leggitt <alan.leggitt@ucsf.edu>
#
# License: BSD-3-Clause
import os.path as op
import mne
from mne import setup_source_space, setup_volume_source_space
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
subject = 'sample'
aseg_f... |
phoebe-project/phoebe2-docs | 2.2/tutorials/logg.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Surface Gravity (logg)
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
%matplotlib inlin... |
gwulfs/research_public | lectures/pairs_trading/Pairs Trading.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import statsmodels
from statsmodels.tsa.stattools import coint
# just set the seed for the random number generator
np.random.seed(107)
import matplotlib.pyplot as plt
"""
Explanation: Researching a Pairs Trading Strategy
By Delaney Granizo-Mackenzie
Part of the Quantopian Lectu... |
Automating-GIS-processes/2017 | source/codes/Lesson3-point-in-polygon.ipynb | mit | from shapely.geometry import Point, Polygon
# Create Point objects
p1 = Point(24.952242, 60.1696017)
p2 = Point(24.976567, 60.1612500)
# Create a Polygon
coords = [(24.950899, 60.169158), (24.953492, 60.169158), (24.953510, 60.170104), (24.950958, 60.169990)]
poly = Polygon(coords)
# Let's check what we have
print(... |
kit-cel/wt | mloc/ch5_Algorithm_Unfolding/Deep_MIMO_Detection.ipynb | gpl-2.0 | import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import cm
%matplotlib inline
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print("We are using the following device for learning:",device)
"""
Explanation: Deep MIMO Dete... |
pastas/pastas | examples/notebooks/10_multiple_wells.ipynb | mit | import numpy as np
import pandas as pd
import pastas as ps
import matplotlib.pyplot as plt
ps.show_versions()
"""
Explanation: Adding Multiple Wells
This notebook shows how a WellModel can be used to fit multiple wells with one response function. The influence of the individual wells is scaled by the distance to the ... |
Chipe1/aima-python | vacuum_world.ipynb | mit | from agents import *
from notebook import psource
"""
Explanation: THE VACUUM WORLD
In this notebook, we will be discussing the structure of agents through an example of the vacuum agent. The job of AI is to design an agent program that implements the agent function: the mapping from percepts to actions. We assume thi... |
jphall663/bellarmine_py_intro | python_tricks.ipynb | apache-2.0 | type(4/2) # float
type(4//2) # int, double slash performs integer division
"""
Explanation: Python Tricks
License
Copyright (c) 2017 by Patrick Hall, jpatrickhall@gmail.com
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 ... |
yoheikikuta/arxiv_summary_translation | arxiv_translator.ipynb | mit | import os
from modules.DataArxiv import get_date
from modules.DataArxiv import execute_query
from modules.Translate import Translate
"""
Explanation: Arxiv summary auto translation
Set up
import modules.
End of explanation
"""
CREDENTIALS_JSON = "credentials.json"
CREDENTIALS_PATH = os.path.normpath(
os.path.j... |
AEW2015/PYNQ_PR_Overlay | Pynq-Z1/notebooks/Video_PR/Lines_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
Lines Filter Example
In this notebook, we will create a box formed of four lines on... |
mariusvniekerk/bayes_logistic | notebooks/bayeslogistic_demo.ipynb | bsd-3-clause | # import bayes_logistic
import bayes_logistic as bl
#-------------------------------------------------------------------------------------
# These are imported for use within the notebook.
# bayeslogistic imports numpy and scipy.optimize automatically
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inl... |
minesh1291/Practicing-Kaggle | zillow2017/H2Opy_v0.ipynb | gpl-3.0 | import h2o
import time,os
%matplotlib inline
#IMPORT ALL THE THINGS
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from h2o.estimators.deeplearning import H2OAutoEncoderEstimator, H2ODeepLearningEstimator
from h2o.estimators.gbm import H2OGradientBoostingEstimator
fro... |
tiagoantao/biopython-notebook | notebooks/18 - KEGG.ipynb | mit | !wget http://rest.kegg.jp/get/ec:5.4.2.2 -O ec_5.4.2.2.txt
from Bio.KEGG import Enzyme
records = Enzyme.parse(open("ec_5.4.2.2.txt"))
record = list(records)[0]
record.classname
record.entry
"""
Explanation: KEGG
KEGG (http://www.kegg.jp/) is a database resource for understanding
high-level functions and utilities... |
amcdawes/QMlabs | Simulating measurements.ipynb | mit | import matplotlib.pyplot as plt
from numpy import sqrt,pi,cos,sin,arange,random,real,imag
from qutip import *
%matplotlib inline
"""
Explanation: Measurement simulation
A way to simulate data from measurements of a specific quantum state.
Start with standard imports:
End of explanation
"""
H = Qobj([[1],[0]])
V = Qo... |
pydata/xarray | doc/examples/multidimensional-coords.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import pandas as pd
import xarray as xr
import cartopy.crs as ccrs
from matplotlib import pyplot as plt
"""
Explanation: Working with Multidimensional Coordinates
Author: Ryan Abernathey
Many datasets have physical coordinates which differ from their logical coordinates. Xarray pr... |
YuriyGuts/kaggle-quora-question-pairs | notebooks/feature-lda.ipynb | mit | from pygoose import *
from gensim.corpora import Dictionary
from gensim.models import LdaMulticore
from nltk.stem import SnowballStemmer
from sklearn.metrics.pairwise import cosine_distances, euclidean_distances
"""
Explanation: Feature: LDA Topic Distances
Train a Latent Dirichlet Allocation model with 300 topics ... |
Kaggle/learntools | notebooks/sql_advanced/raw/tut4.ipynb | apache-2.0 | #$HIDE_INPUT$
from google.cloud import bigquery
from time import time
client = bigquery.Client()
def show_amount_of_data_scanned(query):
# dry_run lets us see how much data the query uses without running it
dry_run_config = bigquery.QueryJobConfig(dry_run=True)
query_job = client.query(query, job_config=d... |
brettavedisian/phys202-2015-work | assignments/assignment03/NumpyEx04.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
"""
Explanation: Numpy Exercise 4
Imports
End of explanation
"""
import networkx as nx
K_5=nx.complete_graph(5)
nx.draw(K_5)
"""
Explanation: Complete graph Laplacian
In discrete mathematics a Graph is a set of vertices or n... |
epidataio/epidata-community | ipython/home/tutorials/1. Getting Started Tutorial.ipynb | apache-2.0 | #from epidata.context import ec
from datetime import datetime, timedelta
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: <h1 style="text-align:center;text-decoration: underline">Getting Started Tutorial</h1>
<h1>Overview</h1>
<p>Welcome to the getting started tutorial for EpiData's Jupyter Noteboo... |
kubeflow/fairing | examples/train_job_api/main.ipynb | apache-2.0 | %%writefile train.py
print("hello world!")
job = TrainJob("train.py", backend=KubeflowGKEBackend())
job.submit()
"""
Explanation: Executing a python file
End of explanation
"""
def train():
print("simple train job!")
job = TrainJob(train, backend=KubeflowGKEBackend())
job.submit()
"""
Explanation: Executing a... |
mne-tools/mne-tools.github.io | 0.23/_downloads/a3f6a5e6550d5cc477c48007e697532b/ems_filtering.ipynb | bsd-3-clause | # Author: Denis Engemann <denis.engemann@gmail.com>
# Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io, EvokedArray
from mne.datasets import sample
from mne.decoding import EMS, compute_ems
from sklearn.model_... |
keras-team/keras-io | guides/ipynb/intro_to_keras_for_engineers.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
from tensorflow import keras
"""
Explanation: Introduction to Keras for Engineers
Author: fchollet<br>
Date created: 2020/04/01<br>
Last modified: 2020/04/28<br>
Description: Everything you need to know to use Keras to build real-world machine learning solutions.
Setup
End of... |
YeEmrick/learning | cs231/assignment/assignment2/Dropout.ipynb | apache-2.0 | # 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... |
banneker-aztlan/python-week-2 | Part 2/galaxy_phot.ipynb | mit | # only necessary if you're running Python 2.7 or lower
from __future__ import print_function
from __builtin__ import range
"""
Explanation: Simulating Galaxy Observations: Photometry
In this exercise, I want to focus on getting comfortable with a few core packages: matplotlib (the default plotting utility), numpy (use... |
ShubhamDebnath/Coursera-Machine-Learning | Course 2/Gradient Checking v1.ipynb | mit | # Packages
import numpy as np
from testCases import *
from gc_utils import sigmoid, relu, dictionary_to_vector, vector_to_dictionary, gradients_to_vector
"""
Explanation: Gradient Checking
Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking.
You are ... |
mcc-petrinets/formulas | spot/tests/python/_altscc.ipynb | mit | from IPython.display import display
import spot
spot.setup(show_default='.bas')
spot.automaton('''
HOA: v1
States: 2
Start: 0&1
AP: 2 "a" "b"
acc-name: Buchi
Acceptance: 1 Inf(0)
--BODY--
State: 0
[0] 0
[!0] 1
State: 1
[1] 1 {0}
--END--
''')
"""
Explanation: These examples are tests for scc_info on alternating automa... |
leoferres/prograUDD | labs/20.ejercicio_dict1.ipynb | mit | letras = {"A" : (1,12) , "B" : (3,2) , "C" : (3,4), "D" : (2,5), "E" : (1,12), "F" : (4,1),
"G" : (2,2) , "H" : (4,2) , "I" : (1,6), "J" : (8,1), "L" : (1,4), "M" : (3,2),
"N" : (1,5) , "O" : (1,9) , "P" : (3,2), "Q" : (5,1), "R" : (1,5), "S" : (1,6),
"T" : (1,4) , "U" : (1,5) , "V" : (4,1... |
amitkaps/applied-machine-learning | Module-03c-Model-Evaluation.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('fivethirtyeight')
df = pd.read_csv('data/historical_loan.csv')
df.head()
"""
Explanation: Model Evaluation
End of explanation
"""
df.years = df.years.fillna(np.mean(df.years))
#Load the preprocessing module
fr... |
Vvkmnn/books | AutomateTheBoringStuffWithPython/lesson45.ipynb | gpl-3.0 | import docx
"""
Explanation: Lesson 45:
Reading and Editing Word Documents
Python can also be used to create and modify Word documents.
The python-docx module can interact with Word document files, with .docx filetypes. While the module is installed via python-docx, it is imported with docx.
End of explanation
"""
... |
ES-DOC/esdoc-jupyterhub | notebooks/thu/cmip6/models/sandbox-1/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'thu', 'sandbox-1', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: THU
Source ID: SANDBOX-1
Topic: Atmoschem
Sub-Topics: Transport, Emissions Co... |
iannesbitt/ml_bootcamp | Python-for-Data-Analysis/Pandas/Merging, Joining, and Concatenating .ipynb | mit | import pandas as pd
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index=[0, 1, 2, 3])
df2 = pd.DataFrame({'A': ['A4', 'A5',... |
kaushik94/tardis | docs/models/examples/.ipynb_checkpoints/Custom_Density_And_Boundary_Velocities-checkpoint.ipynb | bsd-3-clause | import tardis
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Specifying boundary velocities in addition to a custom density file
This notebook will go through multiple detailed examples of how to properly run TARDIS with a custom ejecta profile specified by a custom density file and a custom abund... |
tkzeng/molecular-design-toolkit | moldesign/_notebooks/Example 3. Simulating a crystal structure.ipynb | apache-2.0 | %matplotlib inline
from matplotlib.pyplot import *
import moldesign as mdt
from moldesign import units as u
"""
Explanation: <span style="float:right">
<a href="http://moldesign.bionano.autodesk.com/" target="_blank" title="About">About</a>
<a href="https://forum.bionano.autodesk.c... |
crowd-course/datascience | 5-classification/5.4.2 - Peeking Inside a Neural Network with MNIST Data.ipynb | mit | import pandas as pd
import numpy as np
from sknn.mlp import Classifier, Layer
from sklearn.datasets import fetch_mldata
from sklearn.utils import shuffle
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib import cm
plt.rcParams['figure.figsize'] = (10, 10)
"""
Explanation: Peeking Inside a Neural Netw... |
seg/2016-ml-contest | MandMs/04_faciesClassification_MandMs_featureEngineering_v2.ipynb | apache-2.0 | # for training data
# import data and filling missing PE values with average
filename = 'facies_vectors.csv'
train_data = pd.read_csv(filename)
train_data['PE'].fillna((train_data['PE'].mean()), inplace=True)
print np.shape(train_data)
train_data['PE'].fillna((train_data['PE'].mean()), inplace=True)
print np.shape(... |
pablormier/yabox | notebooks/yabox-vs-scipy-de.ipynb | apache-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import sys
from time import time
# Load Yabox (from local)
# Comment this line to use the installed version
sys.path.insert(0, '../')
import yabox as yb
import scipy as sp
import numpy as np
# Import the DE implementations
from yabox.algorithms import DE, PDE
from ... |
keylime1/courses_12-752 | projects/avanig_ndirks/Final Project_avanig_ndirks.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import datetime as dt
from operator import itemgetter
import math
%matplotlib inline
"""
Explanation: Avani Goyal, Nathaniel Dirks :
12752 :
Final Project
Due: 12/13/2015
End of explanation
"""
f= open('recs2009_public.csv','r')
datanames = np.genfromtxt(f,delimiter... |
godfreyduke/deep-learning | sentiment-rnn/Sentiment_RNN_Solution.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
GoogleCloudPlatform/training-data-analyst | quests/bq-teradata/02_teradata_bq_sql_translation/solution/teradata_bq_sql_translation.ipynb | apache-2.0 | !bq head -n 5 --selected_fields rental_id,duration,bike_id,end_date,end_station_id,start_date,start_station_id bigquery-public-data:london_bicycles.cycle_hire
"""
Explanation: Teradata to BigQuery SQL Translation
Introduction
Both BigQuery and Teradata Database conform to the ANSI/ISO SQL:2011 standard. In addition, ... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/structured/labs/1b_prepare_data_babyweight.ipynb | apache-2.0 | %%bash
sudo pip freeze | grep google-cloud-bigquery==1.6.1 || \
sudo pip install google-cloud-bigquery==1.6.1
"""
Explanation: LAB 2b: Prepare babyweight dataset.
Learning Objectives
Setup up the environment
Preprocess natality dataset
Augment natality dataset
Create the train and eval tables in BigQuery
Export data... |
StevenPeutz/myDataProjects | PyCon_2018/PyCon 2018/StevenPyCon2018 Exercise(Pandas).ipynb | cc0-1.0 | import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import os
cwd = os.getcwd()
print(cwd)
ls ../
"""
Explanation: PyCon 2018
workshop by Kevin Markham (founder of dataschool.io)
End of explanation
"""
df = pd.read_csv('../police.csv')
"""
Explanation: 'Stanford Open Policing Project' // d... |
jennybrown8/python-notebook-coding-intro | lesson8exercises.ipynb | apache-2.0 | children = ["sally", "jenny", "latoya", "atalia", "yu"]
"""
Explanation: Lesson 8: String Processing
The following exercises let you practice on string processing. You'll need the lesson page open for examples since there are too many to list here. I've linked to the python string functions (string methods) just b... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_stats_spatio_temporal_cluster_sensors.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mne.viz import plot_topomap
import mne
from mne.stats import spatio_temporal_cluster_test
from mne.datasets import sample
fro... |
ZhangXinNan/tensorflow | tensorflow/contrib/eager/python/examples/notebooks/automatic_differentiation.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... |
gardenermike/deep-learning | sentiment-rnn/Sentiment RNN.ipynb | mit | import numpy as np
import tensorflow as tf
with open('./reviews.txt', 'r') as f:
reviews = f.read()
with open('./labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural network that performs sentiment analy... |
btq/citi_bike | CitiBike_01_Read_Explore.ipynb | mit | #Station information
station_status_url = 'http://www.citibikenyc.com/stations/json'
resp=requests.get(station_status_url)
resp.json().keys()
resp.json()['stationBeanList'][0]
station_info = pd.DataFrame(resp.json()['stationBeanList'])
station_info.head(2)
filename = 'data/201501-citibike-tripdata.zip'
with zipfile.... |
arcyfelix/Courses | 18-11-22-Deep-Learning-with-PyTorch/Final Lab/Image Classifier Project.ipynb | apache-2.0 | # Imports here
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.optim as optim
from torch import nn
from torch.utils.data.sampler import SubsetRandomSampler
from torch.utils.data.dataloader import DataLoader
import torchvision.transforms as transforms
import torchvision.datasets
import to... |
seg/2016-ml-contest | MandMs/03_Facies_classification_MandMs_feature_engineering_derivatives_moments_glcms.ipynb | apache-2.0 | # import data and filling missing PE values with average
filename = 'facies_vectors.csv'
training_data = pd.read_csv(filename)
training_data['PE'].fillna((training_data['PE'].mean()), inplace=True)
print np.shape(training_data)
training_data['PE'].fillna((training_data['PE'].mean()), inplace=True)
print np.shape(tr... |
lahdo/sentence-suggester | back-end/notebooks/.ipynb_checkpoints/gensim1-checkpoint.ipynb | mit | raw_corpus = ["Human machine interface for lab abc computer applications",
"A survey of user opinion of computer system response time",
"The EPS user interface management system",
"System and human system engineering testing of EPS",
"Relation of user pe... |
tatsuya-ogawa/udacity-deep-learning | image-classification/dlnd_image_classification.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifar10... |
kit-cel/wt | mloc/ch4_Autoencoders/Autoencoder_Compression_Binarizer_simple.ipynb | gpl-2.0 | import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import numpy as np
from matplotlib import pyplot as plt
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print("We are using the following device for learning:",device)
"""
Explanation: Image Compression using Autoencoders with B... |
vicente-gonzalez-ruiz/YAPT | 02-basics/09-OOP.ipynb | cc0-1.0 | class A:
pass
"""
Explanation: Object Oriented Programming
Objects are structures which contain data (attributes) and code (hold by methods). In Python everything is an object, so, objects can contain other objects and methods.
1. Defining a class
End of explanation
"""
class A(object):
pass
"""
Explanation... |
baliga-lab/GGBWeb | docs/ggbweb_usecase_bbb.ipynb | mit | from query.egrin2_query import *
# connect to the egrin 2.0 database
host = "primordial"
port = 27017
db = "eco_db"
client = MongoClient( 'mongodb://'+ host +':'+ str( port )+'/' )
"""
Explanation: Introducing GGBweb
...for Baligans...
(that means EGRIN 2.0)
GGBweb Highlights
<u>Interact</u> with data on a genome-sc... |
mansweet/GaussianLDA | Data Prep.ipynb | apache-2.0 | from __future__ import division
import numpy as np
from sklearn import datasets
import random
import pprint
from scipy import stats as stat
import nltk
from operator import itemgetter
from gensim.models import Word2Vec
from nltk.tokenize import word_tokenize
from sklearn.cluster import KMeans
import FastGaussianLDA2
... |
Naereen/notebooks | agreg/Algorithme_genetique_pour_generer_des_eclairages_modelisation_agreg.ipynb | mit | graphe1 = [(1,3), (3,2), (2,4), (2,6), (2,7), (4,5), (5,6), (6,7), (6,9), (7,8), (8,9)]
graphe1 = [ (u-1, v-1) for (u,v) in graphe1 ]
def nbsommets(graphe):
n = 0
for (u, v) in graphe:
if u > n or v > n: n = max(u, v)
return n + 1
nbsommets(graphe1)
"""
Explanation: Table of Contents
<p><div clas... |
kaleoyster/nbi-data-science | Bridge Life-Cycle Models/CDF+Probability+Reconstruction+vs+Age+of+Bridges+in+the+Southeast+United+States.ipynb | gpl-2.0 | import pymongo
from pymongo import MongoClient
import time
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib.pyplot import *
import matplotlib.pyplot as plt
import folium
import datetime as dt
import random as rnd
import warnings
import datetime as dt
import csv
%matplotlib inline
"""
Explan... |
ltiao/notebooks | calculating-kl-divergence-in-closed-form-versus-monte-carlo-estimation.ipynb | mit | %matplotlib notebook
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import norm
from keras import backend as K
from keras.layers import (Input, Activation, Dense, Lambda, Layer,
add, multiply)
from keras.models import Model, S... |
SylvainCorlay/ipywidgets | docs/source/examples/Output Widget.ipynb | bsd-3-clause | import ipywidgets as widgets
"""
Explanation: Index - Back - Next
Output widgets: leveraging Jupyter's display system
End of explanation
"""
out = widgets.Output(layout={'border': '1px solid black'})
out
"""
Explanation: The Output widget can capture and display stdout, stderr and rich output generated by IPython. ... |
rjenc29/numerical | tensorflow/genadv1.ipynb | mit | import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
%matplotlib inline
"""
Explanation: Generative Adversarial Nets
Training a generative adversarial network to sample from a Gaussian distribution. This is a toy problem, takes < 3 minutes to run on a modest 1.2GHz CP... |
biolink/ontobio | notebooks/Phenotype_Enrichment.ipynb | bsd-3-clause | ## Parse ids from file
file = open("data/rp-genes.tsv", "r")
gene_ids = [row.split("\t")[0] for row in file]
## show first 10 IDs:
gene_ids[:10]
## Create an ontology factory in order to fetch HPO
from ontobio.ontol_factory import OntologyFactory
ofactory = OntologyFactory()
ont = ofactory.create("hp") ## Load HP. ... |
ES-DOC/esdoc-jupyterhub | notebooks/csiro-bom/cmip6/models/access-1-0/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csiro-bom', 'access-1-0', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: CSIRO-BOM
Source ID: ACCESS-1-0
Topic: Atmos
Sub-Topics: Dynamical Core, Radia... |
jonathanmorgan/msu_phd_work | methods/data_creation/2016.12.10-work_log-prelim_month-single_name_match_error.ipynb | lgpl-3.0 | import datetime
print( "packages imported at " + str( datetime.datetime.now() ) )
%pwd
"""
Explanation: 2016.12.10 - work log - prelim_month - single name match error
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Table-of-Contents" data-toc-modified... |
brettavedisian/Liquid-Crystals-Summer-2015 | Nematic/Annulus_Simple_Matplotlib.ipynb | mit | # commands for plotting, "plot" works with matplotlib
def mesh2triang(mesh):
xy = mesh.coordinates()
return tri.Triangulation(xy[:, 0], xy[:, 1], mesh.cells())
def mplot_cellfunction(cellfn):
C = cellfn.array()
tri = mesh2triang(cellfn.mesh())
return plt.tripcolor(tri, facecolors=C)
def mplot_fun... |
AtmaMani/pyChakras | udemy_ml_bootcamp/Machine Learning Sections/Linear-Regression/Linear Regression - Project Exercise - Solutions.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Linear Regression - Project Exercise
Congratulations! You just got some contract work with an Ecommerce comp... |
phoebe-project/phoebe2-docs | 2.0/tutorials/alternate_backends.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.0,<2.1"
"""
Explanation: Advanced: Alternate Backends
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
import phoe... |
dsacademybr/PythonFundamentos | Cap08/Notebooks/DSA-Python-Cap08-06-Bokeh.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 8</font>
Download: http://github.com/dsacademybr
End of explanation
"""
# Impor... |
jmschrei/pomegranate | benchmarks/pomegranate_vs_sklearn_naive_bayes.ipynb | mit | %pylab inline
import seaborn, time
seaborn.set_style('whitegrid')
from sklearn.naive_bayes import GaussianNB
from pomegranate import *
"""
Explanation: pomegranate / sklearn Naive Bayes comparison
authors: <br>
Nicholas Farn (nicholasfarn@gmail.com) <br>
Jacob Schreiber (jmschreiber91@gmail.com)
<a href="https://gith... |
relopezbriega/mi-python-blog | content/notebooks/pyLinearAlgrebra.ipynb | gpl-2.0 | # <!-- collapse=True -->
# importando modulos necesarios
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.sparse as sp
import scipy.sparse.linalg
import scipy.linalg as la
import sympy
# imprimir con notación matemática.
sympy.init_printing(use_latex='mathjax')
# <!-- collapse=True ... |
matmodlab/matmodlab2 | notebooks/PoroplasticFitting.ipynb | bsd-3-clause | import numpy as np
from numpy import *
from bokeh import *
from bokeh.plotting import *
output_notebook()
from matmodlab2 import *
from pandas import read_excel
from scipy.optimize import leastsq
diff = lambda x: np.ediff1d(x, to_begin=0.)
trace = lambda x, s='SIG': x[s+'11'] + x[s+'22'] + x[s+'33']
RTJ2 = lambda x: sq... |
JoaoFelipe/ipython-unittest | presentations/SciPy 2017.ipynb | mit | %load_ext ipython_unittest.dojo
def add(x, y):
return x + y
%%unittest -p 1
assert add(1, 1) == 2
assert add(1, 2) == 3
assert add(2, 2) == 4
import unittest
import sys
class JupyterTest(unittest.TestCase):
def test_add_1_1_returns_2(self):
self.assertEqual(add(1, 1), 2)
def test_add_1_2_retu... |
bsafdi/NPTFit | examples/Example7_Manual_nonPoissonian_Likelihood.ipynb | mit | # Import relevant modules
%matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import healpy as hp
import matplotlib.pyplot as plt
from NPTFit import nptfit # module for performing scan
from NPTFit import create_mask as cm # module for creating the mask
from NPTFit import psf_correction as pc # m... |
rob-dalton/amazon-product-recommender | src/scriptDev-addPosTags.ipynb | mit | import pyspark as ps
from sentimentAnalysis import dataProcessing as dp
# create spark session
spark = ps.sql.SparkSession(sc)
# get dataframes
# specify s3 as sourc with s3a://
#df = spark.read.json("s3a://amazon-review-data/user_dedup.json.gz")
#df_meta = spark.read.json("s3a://amazon-review-data/metadata.json.gz")... |
PublicHealthEngland/pygom | notebooks/PyGOM_SEIRsetup.ipynb | gpl-2.0 | # import required packages
from pygom import DeterministicOde, Transition, SimulateOde, TransitionType
import os
from sympy import symbols, init_printing
import numpy as np
import matplotlib.pyplot as mpl
import sympy
import itertools
# Add graphvis path (N.B. set to your local circumstances)
graphvis_path = 'h:\\Pr... |
jeanbaptistepriez/predicsis-ai-faq-tuto | 24.how_to_produce_scores_from_trained_model/Predictive Scoring.ipynb | gpl-3.0 | # Load PredicSis.ai SDK
from predicsis import PredicSis
import predicsis.config as config, os, sys
os.environ['PREDICSIS_URL'] = 'your_instance'
if sys.version_info[0] >= 3:
from importlib import reload
reload(config)
"""
Explanation: Goal
From a predictive model, score a new dataset using the Python SDK
Prerequi... |
infilect/ml-course1 | keras-notebooks/FCNN/3.1 Hidden Layer Representation and Embeddings.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Fully Connected Feed-Forward Network
In this notebook we will play with Feed-Forward FC-NN (Fully Connected Neural Network) for a classification task:
Image Classification on MNIST Dataset
RECALL
In the FC-NN, the output of each l... |
Aggieyixin/cjc2016 | code/03.python_intro.ipynb | mit | import random, datetime
import numpy as np
import pylab as plt
import statsmodels.api as sm
from scipy.stats import norm
from scipy.stats.stats import pearsonr
"""
Explanation: Python使用简介
王成军
wangchengjun@nju.edu.cn
计算传播网 http://computational-communication.com
人生苦短，我用Python。
Python（/ˈpaɪθən/）是一种面向对象、解释型计算机程序设计语言
- 由... |
tensorflow/docs-l10n | site/en-snapshot/hub/tutorials/action_recognition_with_tf_hub.ipynb | apache-2.0 | # Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
georgetown-analytics/envirohealth | CapstoneSEER/SEER Data Analysis Phase 1- Ingestion.ipynb | mit | import time
import os
import glob
import pandas as pd
from pandas.io import sql
from MasterSeer import MasterSeer
"""
Explanation: SEER Data Analysis
Phase 1: Data Ingestion
End of explanation
"""
class LoadSeerData(MasterSeer):
def __init__(self, path=r'./data', reload=True, testMode=False, verbose=True, batch... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/migration/UJ4 AutoML for structured data with Vertex AI Regression.ipynb | apache-2.0 | ! pip3 install -U google-cloud-aiplatform --user
"""
Explanation: Vertex AI AutoML tables regression
Installation
Install the latest (preview) version of Vertex SDK.
End of explanation
"""
! pip3 install google-cloud-storage
"""
Explanation: Install the Google cloud-storage library as well.
End of explanation
"""
... |
cloudera/ibis | docs/source/tutorial/07-Advanced-Topics-Analytics-Tools.ipynb | apache-2.0 | import os
import ibis
ibis.options.interactive = True
connection = ibis.sqlite.connect(os.path.join('data', 'geography.db'))
"""
Explanation: Advanced Topics: Analytics Tools
Setup
End of explanation
"""
countries = connection.table('countries')
countries.continent.value_counts()
"""
Explanation: Frequency tables... |
Luke035/dlnd-lessons | sentiment-rnn/.ipynb_checkpoints/Sentiment_RNN-checkpoint.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
sairamprasad999/udacity-deep-learning | projects/notMNIST/1_notmnist.ipynb | mit | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy import ndimage
from sklearn.line... |
PyLCARS/PythonUberHDL | myHDL_ComputerFundamentals/Memorys/Memory.ipynb | bsd-3-clause | from myhdl import *
from myhdlpeek import Peeker
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from sympy import *
init_printing()
import random
#https://github.com/jrjohansson/version_information
%load_ext version_information
%version_information myhdl, myhdlpeek, numpy, ... |
nholtz/structural-analysis | matrix-methods/frame2d/70-Generate-large-frames.ipynb | cc0-1.0 | from Frame2D import Frame2D
from Tables import Table, DataSource
import numpy as np
import pandas as pd
## NOTE: all units are kN and m
FD = {'storey_heights': [6.5] + [5.5]*20 + [7.0], # m
'bay_widths': [10.5,10,10,10,10,10.5], # m
'frame_spacing':8, # m, used only for... |
mne-tools/mne-tools.github.io | 0.17/_downloads/81258b1255ee7242b3b6c9f251dcfbd8/plot_xdawn_denoising.ipynb | bsd-3-clause | # Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
#
# License: BSD (3-clause)
from mne import (io, compute_raw_covariance, read_events, pick_types, Epochs)
from mne.datasets import sample
from mne.preprocessing import Xdawn
from mne.viz import plot_epochs_image
print(__doc__)
data_path = sample.data_pa... |
jfemiani/srp-boxes | nb/get_sample_locations.ipynb | mit | import logging
import os
import numpy as np
import rasterio as rio
import lmdb
from caffe.proto.caffe_pb2 import Datum
import caffe.io
from rasterio._io import RasterReader
from glob import glob
sources =glob('/home/shared/srp/try2/*.tif')
print len(sources)
pos_regions = rasterio.open(r'/home/liux13/Desktop/tmp/pos... |
csieber/alpha-dataset | notebooks/results.ipynb | mit | df = pd.read_csv("../data/results.csv.gz")
df.loc[:,'sw_p_m'] = df.loc[:,'nr_of_switches'] / (df.loc[:,'video_lengh'] / 60)
"""
Explanation: Result Database
In this example we show how to read and evaluate the result database.
End of explanation
"""
df = df[(df.video_id == "CRZbG73SX3s") & (df.pattern_type=="medium"... |
kaleoyster/nbi-data-science | Deterioration Curves/(Northeast) Deterioration+Curves+and+Classification+of+Bridges+in+the+Northeast+United+States.ipynb | gpl-2.0 | import pymongo
from pymongo import MongoClient
import time
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import csv
"""
Explanation: Libraries and Packages
End of explanation
"""
Client = MongoClient("mongodb://bridges:readonly@nbi-mongo.admin/bridge")
db = Client.bridg... |
justinsowhat/scikit-learn-nlp-tutorial | sklearn_tutorial_1.ipynb | mit | import pandas as pd
dataset = pd.read_csv('20news-18828.csv', header=None, delimiter=',', names=['label', 'text'])
"""
Explanation: scikit-learn for NLP -- Part 1 Introductory Tutorial
scikit-learning is an open-sourced simple and efficient tools for data mining, data analysis and machine learning in Python. It is bu... |
chengsoonong/didbits | Accuracy/choose_threshold.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Choosing a threshold for classification
There are three types of output that could come out of a classifier: the score, the probability of positive, and the classification.
This notebook illustrates how to select a threshold on the... |
samstav/scipy_2015_sklearn_tutorial | notebooks/01.4 Training and Testing Data.ipynb | cc0-1.0 | from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
iris = load_iris()
X, y = iris.data, iris.target
classifier = KNeighborsClassifier()
"""
Explanation: Cross-Validation and scoring methods
To evaluate how well our supervised models generalize, we can split our data into a trai... |
ARM-software/lisa | ipynb/deprecated/examples/trace_analysis/TraceAnalysis_IdleStates.ipynb | apache-2.0 | import logging
from conf import LisaLogging
LisaLogging.setup()
%matplotlib inline
import os
# Support to access the remote target
from env import TestEnv
# Support to access cpuidle information from the target
from devlib import *
# Support to configure and run RTApp based workloads
from wlgen import RTA, Ramp
#... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/ml_ops/stage6/get_started_with_matching_engine.ipynb | apache-2.0 | import os
# The Vertex AI Workbench Notebook product has specific requirements
IS_WORKBENCH_NOTEBOOK = os.getenv("DL_ANACONDA_HOME")
IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(
"/opt/deeplearning/metadata/env_version"
)
# Vertex AI Notebook requires dependencies to be installed with '--user'
USER_FLAG = ... |
openfisca/openfisca-france-indirect-taxation | openfisca_france_indirect_taxation/examples/notebooks/compute_cas_type_ticpe.ipynb | agpl-3.0 | import datetime
import pandas as pd
import seaborn
"""
Explanation: Nous simulons les montants de TICPE payés par un ménage selon le type de véhicules dont il dispose. Nous prenons un ménage dont les dépenses annuelles en carburants s'élèveraient à 1000 euros. C'est en dessous de la moyenne de nos samples (plutôt aut... |
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