repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
AllenDowney/ThinkBayes2 | soln/chap05.ipynb | mit | # If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename... |
mne-tools/mne-tools.github.io | 0.20/_downloads/36ac16a286b47b66f1b51a959c65b5b9/plot_stats_cluster_time_frequency_repeated_measures_anova.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import tfr_morlet
from mne.stats import f_... |
JannesKlaas/MLiFC | Week 4/Ch. 18 - Temporal Order Matters.ipynb | mit | from keras.datasets import imdb
from keras.preprocessing import sequence
max_words = 10000 # Our 'vocabulary of 10K words
max_len = 500 # Cut texts after 500 words
# Get data from Keras
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_words)
print(len(x_train), 'train sequences')
print(len(x_test... |
quantumlib/ReCirq | docs/hfvqe/quickstart.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... |
AhmetHamzaEmra/Deep-Learning-Specialization-Coursera | Convolutional Neural Networks/Keras+-+Tutorial+-+Happy+House+v2.ipynb | mit | import numpy as np
from keras import layers
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from keras.models import Model
from keras.preprocessing import im... |
spacy-io/thinc | examples/03_pos_tagger_basic_cnn.ipynb | mit | !pip install "thinc>=8.0.0a0" "ml_datasets>=0.2.0a0" "tqdm>=4.41"
"""
Explanation: Basic CNN part-of-speech tagger with Thinc
This notebook shows how to implement a basic CNN for part-of-speech tagging model in Thinc (without external dependencies) and train the model on the Universal Dependencies AnCora corpus. The t... |
mne-tools/mne-tools.github.io | dev/_downloads/d7719f60a0c257a5313f06f110154ff3/20_dipole_fit.ipynb | bsd-3-clause | import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.forward import make_forward_dipole
from mne.evoked import combine_evoked
from mne.simulation import simulate_evoked
from nilearn.plotting import plot_anat
from nilearn.datasets import load_mni152_template
data_path = mne.data... |
eyaltrabelsi/my-notebooks | Lectures/Debugging Notebooks.ipynb | mit | import random
def find_max (values):
max = 0
print(f"Initial max is {max}")
for val in values:
if val > max:
max = val
return max
sample = random.sample(range(100), 10)
find_max(sample)
"""
Explanation: Debugging Notebooks
Naive Way - print
End of explanation
"""
import random
de... |
csiu/100daysofcode | datamining/api-reddit.ipynb | mit | import yaml
import praw
import nltk
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import subjectivity
from nltk.sentiment import SentimentAnalyzer
from nltk.sentiment.util import *
from nltk import tokenize
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import pandas as pd
import matp... |
esumitra/minecraft-programming | notebooks/Adventure3.ipynb | mit | import sys
# sys.path.append('/Users/esumitra/workspaces/mc/mcpipy')
# Run this once before starting your tasks
import mcpi.minecraft as minecraft
import mcpi.block as block
import time
mc = minecraft.Minecraft.create()
"""
Explanation: Superpowers for Steve
With our newly learned programming skills let's give Steve ... |
ML4DS/ML4all | TM4.DTUCourse/notebook/DTU02901_student.ipynb | mit | # Common imports
import numpy as np
# import pandas as pd
# import os
from os.path import isfile, join
# import scipy.io as sio
# import scipy
import zipfile as zp
# import shutil
# import difflib
"""
Explanation: Exploring and undertanding documental databases with topic models and graph analysis
Exercise notebook
... |
YaleDHLab/lab-workshops | machine-learning/numerical-optimization.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
np.random.seed(13)
# identify the number of houses to include in the dataset
n_observations = 15
# generate a price and square footage value for each house
prices = np.linspace(200, 400, num=n_observations)
footage = np.linspace(1000, 2000, num=n_... |
shareactorIO/pipeline | source.ml/jupyterhub.ml/notebooks/zz_old/TensorFlow/Word2Vec/3_word2vec_activity.ipynb | apache-2.0 | reset -fs
import collections
import math
import os
from pprint import pprint
import random
import urllib.request
import zipfile
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from sklearn.manifold import TSNE
%matplotlib inline
"""
Explanation: Apply word2vec to dataset
Download some... |
mayankjohri/LetsExplorePython | Section 1 - Core Python/Chapter 02 - Data Types Part - 1/String.ipynb | gpl-3.0 | #### Standard String Examples:
friend = 'Chandu\tNalluri'
print(friend)
manager_details = "# Roshan Musheer:\nExcellent Manager and human being."
print(manager_details)
"""
Explanation: String
Strings are Python builtins datatype for handling text. They are immutable thus you can not add, remove or updated any char... |
lileiting/goatools | notebooks/goea_nbt3102_all_study_genes.ipynb | bsd-2-clause | # Get http://geneontology.org/ontology/go-basic.obo
from goatools.base import download_go_basic_obo
obo_fname = download_go_basic_obo()
"""
Explanation: Run a GOEA. Print study genes as either IDs symbols
We use data from a 2014 Nature paper:
Computational analysis of cell-to-cell heterogeneity
in single-cell RNA-se... |
woters/ds101 | 2-sklearn.ipynb | mit | from IPython.display import Image
Image("images/ml-model.png", width=500)
"""
Explanation: 2 - Intro в Scikit-learn
Цели
Основные понятия:
Модели
Кластеризация, Классификация, Регрессия
Fit, Predict, Evaluate
Accuracy, confusion matrix
Overfitting, training/test data, and crossvalidation
API:
model.fit()
model... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/migration/UJ1 Vertex SDK AutoML Image Classification.ipynb | apache-2.0 | import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex AI: Vertex AI Migration: AutoML Image Classification
<table align="left">
<td>
<a ... |
jskksj/cv2stuff | cv2stuff/notebooks/ConfigParser.ipynb | isc | config = configparser.ConfigParser()
config.sections()
config.read('example.ini')
config.sections()
'bitbucket.org' in config
'bytebong.com' in config
config['bitbucket.org']['User']
config['DEFAULT']['Compression']
topsecret = config['topsecret.server.com']
topsecret['ForwardX11']
topsecret['Port']
for key i... |
ecervera/mindstorms-nb | task/motors.ipynb | mit | from functions import connect, forward, backward, stop, left, right, disconnect, next_notebook
from time import sleep
connect() # Executeu, polsant Majúscules + Enter
"""
Explanation: Moviments bàsics del robot
El robot té dos motors, que controlen cadascuna de les rodes amb uns engranatges. Estan connectats amb cab... |
drphilmarshall/OM10 | examples/LSST/OM10_LSSTDESC.ipynb | mit | import om10
import os, numpy as np
db = om10.DB(catalog=os.path.expandvars("$OM10_DIR/data/qso_mock.fits"))
db.select_random(maglim=22.0,area=18000.0,IQ=0.75)
good = db.sample[np.where(\
(db.sample['IMSEP'] > 1.0) * \
(db.sample['APMAG_I'] < 21.0) * \
(np.max(db.sample['DELAY'],axis=1) > 10.0... |
yihaochen/FLASHtools | xray/xray_APEC_emissivity.ipynb | gpl-2.0 | h5f = h5py.File('apec_emissivity_v2.h5', 'r')
"""
Explanation: Read the hdf5 file using h5py
End of explanation
"""
for k, v in h5f.items():
print(k, v)
"""
Explanation: It works similar to a dictionary in python. We can print the keys and values in this file.
End of explanation
"""
h5f['E'].value
"""
Explan... |
beyondvalence/biof509_wtl | Wk03-OOP/Wk03-Paradigms.ipynb | mit | primes = []
i = 2
while len(primes) < 25:
for p in primes:
if i % p == 0:
break
else:
primes.append(i)
i += 1
print(primes)
"""
Explanation: Week 3 - Programming Paradigms
Learning Objectives
List popular programming paradigms
Demonstrate object oriented programming
Compare pr... |
jc091/deep-learning | embeddings/Skip-Gram_word2vec.ipynb | mit | import time
import numpy as np
import tensorflow as tf
import utils
"""
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language p... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/production_ml/solutions/serving_ml_prediction.ipynb | apache-2.0 | PROJECT = "cloud-training-demos" # Replace with your PROJECT
BUCKET = PROJECT
REGION = "us-central1" # Choose an available region for Cloud MLE
TFVERSION = "2.6" # TF version for CMLE to use
import os
os.environ["BUCKET"] = BUCKET
os.environ["PROJECT"] = PROJECT
os.environ["REGION"] = REGIO... |
sinkap/bart | notebooks/thermal/Thermal.ipynb | apache-2.0 | import trappy
config = {}
# TRAPpy Events
config["THERMAL"] = trappy.thermal.Thermal
config["OUT"] = trappy.cpu_power.CpuOutPower
config["IN"] = trappy.cpu_power.CpuInPower
config["PID"] = trappy.pid_controller.PIDController
config["GOVERNOR"] = trappy.thermal.ThermalGovernor
# Control Temperature
config["CONTROL_T... |
cjam/deep-onc | notebooks/keras-mnist-example.ipynb | mit | # matplotlib is used for...you guessed it: plotting!
import matplotlib.pyplot as plt
# This next line is a Jupyter directive. It tells Jupyter that we want our plots to show
# up right below the code that creates them.
%matplotlib inline
import tensorflow as tf
from keras import backend,__version__ as keras_version
... |
walkon302/CDIPS_Recommender | notebooks/Dimensionality_Reduction_on_Features.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import seaborn
import pandas as pd
from sklearn.decomposition import PCA
import pickle
%matplotlib inline
# load smaller user behavior dataset
user_profile = pd.read_pickle('../data_user_view_buy/user_profile_items_nonnull_features_20_mins_5_views_v2_sample1000.pkl')
... |
pablosv/memexp | abalysis.ipynb | gpl-3.0 | # General libraries
import os
import pickle
import numpy as np
# Plot, in nb, only when .show() is called
import matplotlib.pyplot as plt
%matplotlib notebook
plt.ioff()
# Personal libraries
import tools.evaluation as ev
import tools.plot as pt
"""
Explanation: Script that analyzes the results from kinetic simulati... |
intel-analytics/analytics-zoo | docs/docs/colab-notebook/orca/quickstart/keras_lenet_mnist.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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distrib... |
planetlabs/notebooks | jupyter-notebooks/analytics/user-guide/03_change_detection.ipynb | apache-2.0 | import os
import requests
# Configure Auth and Base URL
# Planet Analytics API base URL
PAA_BASE_URL = "https://api.planet.com/analytics/"
# API Key Config
API_KEY = os.environ['PL_API_KEY']
# Alternatively, you can just set your API key directly as a string variable:
# API_KEY = "YOUR_PLANET_API_KEY_HERE"
# Setup... |
qutip/qutip-notebooks | examples/trilinear.ipynb | lgpl-3.0 | %pylab inline
from qutip import *
import time
#number of states for each mode
N0=8
N1=8
N2=8
K=1.0
#damping rates
gamma0=0.1
gamma1=0.1
gamma2=0.4
alpha=sqrt(3)#initial coherent state param for mode 0
epsilon=0.5j #sqeezing parameter
tfinal=4.0
dt=0.05
tlist=arange(0.0,tfinal+dt,dt)
taulist=K*tlist #non-dimensional t... |
geratarra/Machine-Learning | Tareas/Logistic Regression/regresion_logistica.ipynb | gpl-2.0 | %matplotlib inline
from scipy.stats import logistic
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import Image # Esto es para desplegar imágenes en la libreta
"""
Explanation: Regresión logística
En esta libreta vamos a desarrollar los algoritmos de regresión logística, y vamos a aplicar los... |
sarahmid/programming-bootcamp-v2 | lab7_exercises.ipynb | mit | import imp
my_utils = imp.load_source('my_utils', '../utilities/my_utils.py') #CHANGE THIS PATH
# test that this worked
print "Test my_utils.gc():", my_utils.gc("ATGGGCCCAATGG")
print "Test my_utils.reverse_compl():", my_utils.reverse_compl("GGGGTCGATGCAAATTCAAA")
print "Test my_utils.read_fasta():", my_utils.read_fas... |
bjshaw/phys202-2015-work | assignments/assignment10/ODEsEx03.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
"""
Explanation: Ordinary Differential Equations Exercise 3
Imports
End of explanation
"""
g = 9.81 # m/s^2
l = 0.5 # length of pendulum... |
Illumina/interop | docs/src/Tutorial_05_Imaging_Table.ipynb | gpl-3.0 | run_folder = r""
"""
Explanation: Using the Illumina InterOp Library in Python: Part 5
Install
If you do not have the Python InterOp library installed, then you can do the following:
$ pip install interop
You can verify that InterOp is properly installed:
$ python -m interop --test
Before you begin
If you plan to us... |
akchinSTC/systemml | samples/jupyter-notebooks/SystemML-PySpark-Recommendation-Demo.ipynb | apache-2.0 | !pip show systemml
%load_ext autoreload
%autoreload 2
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from systemml import MLContext, dml # pip install systeml
plt.rcParams['figure.figsize'] = (10, 6)
"""
Explanation: SystemML PySpark Recommendation Demo
This demonstrates using SystemML for pr... |
kadircet/CENG | 783/HW1/task5_next_char.ipynb | gpl-3.0 | import random
import numpy as np
from metu.data_utils import load_nextchar_dataset, plain_text_file_to_dataset
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
... |
gassantos/ML4Edatics | Relatório de Aprendizado de Máquina - Trabalho 02 (LoadTXT).ipynb | gpl-3.0 | #import os
import pandas as pd
import numpy as np
import matplotlib as plt
from numpy import loadtxt, where, append, zeros, ones, array, linspace, logspace
from pylab import scatter, show, legend, xlabel, ylabel
#%matplotlib inline
# Carregando o arquivo gerado pelo MATLAB
#import scipy.io
#mat = scipy.io.loadmat('... |
mne-tools/mne-tools.github.io | 0.17/_downloads/dc0d85321d22190ec4d4c4394d0057f4/plot_opm_data.ipynb | bsd-3-clause | # sphinx_gallery_thumbnail_number = 4
import os.path as op
import numpy as np
import mne
from mayavi import mlab
data_path = mne.datasets.opm.data_path()
subject = 'OPM_sample'
subjects_dir = op.join(data_path, 'subjects')
raw_fname = op.join(data_path, 'MEG', 'OPM', 'OPM_SEF_raw.fif')
bem_fname = op.join(subjects_d... |
mayankjohri/LetsExplorePython | Section 2 - Advance Python/Chapter S2.01 - Functional Programming/n/0 - Introduction to Programming Paradigm.ipynb | gpl-3.0 | L = [1, 2, 4 , 6, 5, 7, 3]
"""
Explanation: Introduction to Programming Paradigms
Imperative: It uses statements that change a program's state. It focuses on describing how a program operates. It is useful in manipulating data structures and produces elegant & simple code.
In computer science, imperative programmin... |
mayank-johri/LearnSeleniumUsingPython | Section 2 - Advance Python/Chapter S2.01 - Functional Programming/03_map_reduce_and_filter.ipynb | gpl-3.0 | names = [ "Manish", "Aalok", "Mayank","Durga"]
lst = []
for name in names:
lst.append(len(name))
print(lst)
names = ("Manish", "Aalok", "Mayank","Durga")
tmp = map(len, names)
print(tmp)
lst = tuple(tmp)
print(lst)
# This is a map that squares every number in the passed collection:
power = map(lambda x: ... |
tpin3694/tpin3694.github.io | machine-learning/minibatch_k-means_clustering.ipynb | mit | # Load libraries
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import MiniBatchKMeans
"""
Explanation: Title: Mini-Batch k-Means Clustering
Slug: minibatch_k-means_clustering
Summary: How to conduct mini-batch k-means clustering in scikit-learn.
Date: 2017-09-22 12:... |
bocklund/notebooks | atomate/Cu-Mg-prlworkflows-example.ipynb | mit | from fireworks import LaunchPad
# lpad = LaunchPad.auto_load()
lpad = LaunchPad.from_file('/Users/brandon/.fireworks/my_launchpad.yaml')
"""
Explanation: Cu-Mg workflows
Goal: fully describe the Cu-Mg system with DFT calculations
Phases
There are 5 phases in Cu-Mg that will be described with the following models
Phase... |
RaRe-Technologies/gensim | docs/src/auto_examples/tutorials/run_doc2vec_lee.ipynb | lgpl-2.1 | import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
"""
Explanation: Doc2Vec Model
Introduces Gensim's Doc2Vec model and demonstrates its use on the
Lee Corpus <https://hekyll.services.adelaide.edu.au/dspace/bitstream/2440/28910/1/hdl_28910.pdf>__.
End of ex... |
mathcoding/programming | notebooks/Lab1_Introduzione.ipynb | mit | 345
"""
Explanation: Elementi di Programmazione
Un linguaggio di programmazione serve sia per istruire una macchina ad eseguire dei conti, che per organizzare le nostre idee su come quei conti devono essere eseguiti. Per questo, nella scelta di un linguaggio di programmazione, dobbiamo tener presente quali sono gli st... |
rustychris/stomel | examples/refine_existing_grid.ipynb | gpl-2.0 | import paver
import trigrid
import matplotlib.pyplot as plt
import numpy as np
import field
%matplotlib notebook
# Load and display a 25k cell grid of San Francisco Bay
p=paver.Paving(suntans_path='/home/rusty/models/suntans/spinupdated/rundata/original_grid')
fig,ax=plt.subplots()
p.tg_plot() ;
"""
Explanation: Re... |
dnc1994/MachineLearning-UW | ml-clustering-and-retrieval/4_em-with-text-data.ipynb | mit | import graphlab
"""
Explanation: Fitting a diagonal covariance Gaussian mixture model to text data
In a previous assignment, we explored k-means clustering for a high-dimensional Wikipedia dataset. We can also model this data with a mixture of Gaussians, though with increasing dimension we run into two important issue... |
sguthrie/predicting-depression | MPI-LeipzigDataset.ipynb | gpl-3.0 | %%bash
ls MPI-Leipzig/behavioral_data_MPILMBB/phenotype | head
"""
Explanation: Examining the MPI-Leipzig Mind-Brain-Body Dataset
The MRI data are available at https://openfmri.org/dataset/ds000221/. The behavioral data are available via NITRC: https://www.nitrc.org/projects/mpilmbb/. Note I was required to edit one f... |
y2ee201/Deep-Learning-Nanodegree | seq2seq/sequence_to_sequence_implementation.ipynb | mit | import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
"""
Explanation: Character Sequence to Sequence
In this notebook, we'll build a model that takes in a sequence of letters, an... |
kubeflow/kfp-tekton-backend | components/gcp/dataproc/submit_pyspark_job/sample.ipynb | apache-2.0 | %%capture --no-stderr
KFP_PACKAGE = 'https://storage.googleapis.com/ml-pipeline/release/0.1.14/kfp.tar.gz'
!pip3 install $KFP_PACKAGE --upgrade
"""
Explanation: Name
Data preparation using PySpark on Cloud Dataproc
Label
Cloud Dataproc, GCP, Cloud Storage,PySpark, Kubeflow, pipelines, components
Summary
A Kubeflow Pi... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/introduction_to_tensorflow/labs/adv_logistic_reg_TF2.0.ipynb | apache-2.0 | import os
import tempfile
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn
import tensorflow as tf
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Stan... |
LDSSA/learning-units | units/15-classifiers/examples/Unit 15 - Classifiers - Example.ipynb | mit | # Import pandas and numpy
import pandas as pd
import numpy as np
# Import the classifiers we will be using
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
# Import train/te... |
dmnfarrell/mhcpredict | examples/sarscov2.ipynb | apache-2.0 | import os, math, time, pickle, subprocess
from importlib import reload
from collections import OrderedDict, defaultdict
import numpy as np
import pandas as pd
pd.set_option('display.width', 180)
import epitopepredict as ep
from epitopepredict import base, sequtils, plotting, peptutils, analysis
from IPython.display imp... |
AndreySheka/dl_ekb | hw9/Seminar9-en-Avito.ipynb | mit | low_RAM_mode = True
very_low_RAM = False #If you have <3GB RAM, set BOTH to true
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Deep learning for Natural Language Processing
Simple text representations, bag of words
Word embedding and... not just another w... |
fluxcapacitor/source.ml | jupyterhub.ml/notebooks/train_deploy/zz_under_construction/tensorflow/optimize/01_Explore_Environment.ipynb | apache-2.0 | %%bash
pull_force_overwrite_local
"""
Explanation: Explore Your Environment
Get Latest Code
End of explanation
"""
%%html
<iframe width=800 height=600 src="http://pipeline.io"></iframe>
"""
Explanation: PipelineAI
End of explanation
"""
import requests
url = 'http://169.254.169.254/computeMetadata/v1/instance/... |
probml/pyprobml | notebooks/misc/text_autoencoders_pytorch.ipynb | mit | import torch
from multiprocessing import cpu_count
print(cpu_count())
print(torch.cuda.is_available())
!git clone https://github.com/shentianxiao/text-autoencoders.git
!ls
%cd text-autoencoders
!ls
"""
Explanation: <a href="https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/text_autoen... |
mit-eicu/eicu-code | notebooks/demo/03-plot-timeseries.ipynb | mit | # Import libraries
import pandas as pd
import matplotlib.pyplot as plt
import psycopg2
import os
# Plot settings
%matplotlib inline
plt.style.use('ggplot')
fontsize = 20 # size for x and y ticks
plt.rcParams['legend.fontsize'] = fontsize
plt.rcParams.update({'font.size': fontsize})
# Connect to the database - which i... |
qinwf-nuan/keras-js | notebooks/layers/pooling/MaxPooling1D.ipynb | mit | data_in_shape = (6, 6)
L = MaxPooling1D(pool_size=2, strides=None, padding='valid')
layer_0 = Input(shape=data_in_shape)
layer_1 = L(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
np.random.seed(250)
data_in = 2 * np.random.random(data_in_shape) - 1
resu... |
h-mayorquin/hopfield_sequences | notebooks/2016-12-11(Study of connectivity distribution).ipynb | mit | from __future__ import print_function
import sys
sys.path.append('../')
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import seaborn as sns
from hopfield import Hopfield
%matplotlib inline
sns.set(font_scale=2.0)
prng = np.random.RandomState(seed=100)
normalize = True
T =... |
arongdari/almc | notebooks/Rescal_vs_brescal.ipynb | gpl-2.0 | import numpy as np
import logging
from scipy.io.matlab import loadmat
from scipy.sparse import csr_matrix
import matplotlib
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score
import rescal
from almc.bayesian_rescal import BayesianRescal
%matplotlib inline
#logger = logging.getLogger()
#logger.... |
t-vi/pytorch-tvmisc | hacks/computed_parameters.ipynb | mit | import torch
import numpy
import inspect # this should raise the "we'll do gross things Python internals flag"
"""
Explanation: Computed Parameters - a PyTorch hack
by Thomas Viehmann
If you are anything like me, you like PyTorch and you enjoy an occasional clever hack. So here we go.
The other day we joked online w... |
birdsarah/bokeh-miscellany | old/slider_example/Gapminder homage 0_6 play button - WIP.ipynb | gpl-2.0 | # Links via http://www.gapminder.org/data/
"""
population_url = "http://spreadsheets.google.com/pub?key=phAwcNAVuyj0XOoBL_n5tAQ&output=xls"
fertility_url = "http://spreadsheets.google.com/pub?key=phAwcNAVuyj0TAlJeCEzcGQ&output=xls"
life_expectancy_url = "http://spreadsheets.google.com/pub?key=tiAiXcrneZrUnnJ9dBU-PAw&o... |
vzg100/Post-Translational-Modification-Prediction | .ipynb_checkpoints/Phosphorylation Sequence Tests -XGB -dbptm+ELM -scalesTrain-checkpoint.ipynb | mit | from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
"""
Explanation: Template for test
End of explanation
"""
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
scale = [-1, "standard", "robust", "minmax", "max"]
for i in par:
for j in scale:... |
hannorein/rebound | ipython_examples/CloseEncounters.ipynb | gpl-3.0 | import rebound
import numpy as np
def setupSimulation():
sim = rebound.Simulation()
sim.integrator = "ias15" # IAS15 is the default integrator, so we don't need this line
sim.add(m=1.)
sim.add(m=1e-3,a=1.)
sim.add(m=5e-3,a=1.25)
sim.move_to_com()
return sim
"""
Explanation: Catching close e... |
saideepchandg/twitter-relationship-using-neo4j | Twitter analysis using Neo4j v2.ipynb | mit | ### checking rate limit - friends list
limit = api.rate_limit_status()
limit['resources']['friends']['/friends/list']['remaining']
limit['resources']['friends']['/friends/list']
"""
Explanation: Twitter API rate limits
End of explanation
"""
import datetime as dt
given_date =dt.datetime.fromtimestamp(
int(li... |
mne-tools/mne-tools.github.io | dev/_downloads/84d68dbced84793d122fec3a2cf0cde5/source_power_spectrum.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, compute_source_psd
print(__doc__)
"""
Explanation: Compute source power spectral den... |
carnby/matta | examples/Let's Make a Map Too.ipynb | bsd-3-clause | from __future__ import print_function, unicode_literals
import matta
import json
import unicodedata
# we do this to load the required libraries when viewing on NBViewer
matta.init_javascript(path='https://rawgit.com/carnby/matta/master/matta/libs')
"""
Explanation: matta - view and scaffold d3.js visualizations in I... |
stefanbuenten/nanodegree | p3/L1_Data_Wrangling.ipynb | mit | # set up environment
import numpy as np
import pandas as pd
"""
Explanation: Lesson 1
Data Wrangling
End of explanation
"""
# read data from local file system
data = pd.read_excel("2013_ERCOT_Hourly_Load_Data.xls")
data.head()
data.dtypes
data["COAST"].describe()
print(data["COAST"].max(), data["COAST"].min(), np... |
alexandratutino/rna-analysis-notebooks | Bar Graph for Multiple Genes.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import csv
aortaData = []
aortaDataNumbers = []
cerebellumData = []
cerebellumDataNumbers= []
arteryData = []
arteryDataNumbers = []
with open ("genomicdata.csv") as csvfile:
readCSV = csv.reader(csvfile, delimiter= '\t') #gives access to the CSV file
for col... |
albahnsen/PracticalMachineLearningClass | notebooks/22-RecurrentNeuralNetworks_LSTM.ipynb | mit | import pandas as pd
data = pd.read_csv('https://raw.githubusercontent.com/albahnsen/PracticalMachineLearningClass/master/datasets/phishing.csv')
data.head()
data.tail()
"""
Explanation: 22 - Recurren Neural Netwoks and LSTM
by Alejandro Correa Bahnsen and Jesus Solano
version 1.4, May 2019
Part of the class Practica... |
shuiruge/little_mcmc | tests/simulated_annealing.ipynb | mit | import sys
sys.path.append('../sample/')
from simulated_annealing import Temperature, SimulatedAnnealing
from random import uniform, gauss
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Description
This is a test of SimulatedAnnealing.
Basics
End of explanation
"""
def temperature_of_time(t, re... |
intel-analytics/BigDL | apps/sentiment-analysis/sentiment.ipynb | apache-2.0 | from bigdl.dllib.feature.dataset import base
import numpy as np
def download_imdb(dest_dir):
"""Download pre-processed IMDB movie review data
:argument
dest_dir: destination directory to store the data
:return
The absolute path of the stored data
"""
file_name = "imdb.npz"
fil... |
texib/deeplearning_homework | theano-scan.ipynb | mit | import theano
import theano.tensor as T
"""
Explanation: 練習 Theano 的 Scan Function
End of explanation
"""
k = T.iscalar('K')
a = T.vector('A')
i = T.vector('A')
result, updates = theano.scan(fn=lambda pre , k : pre*a ,
outputs_info = i,
non_sequences=a,
n... |
ebellm/ztf_summerschool_2015 | notebooks/Introduction_to_Python_and_Astropy.ipynb | bsd-3-clause | # and "code" cells for computation and output, like this one! Press Shift-Enter to execute it.
2+2
"""
Explanation: Hands-on Exercise 0: Introduction to Python & Astropy
by Leo Singer (2014) and Eric Bellm (2015-2016)
Introduction
Our hands-on exercises will use the Python programming language. No previous experien... |
smattis/BET-1 | examples/compare/comparison.ipynb | gpl-3.0 | num_samples_left = 50
num_samples_right = 50
delta = 0.5 # width of measure's support per dimension
L = unit_center_set(2, num_samples_left, delta)
R = unit_center_set(2, num_samples_right, delta)
plt.scatter(L._values[:,0], L._values[:,1], c=L._probabilities)
plt.xlim([0,1])
plt.ylim([0,1])
plt.show()
plt.scatter(R.... |
Olsthoorn/IHE-python-course-2017 | exercises/Feb28/tuplesListsSets.ipynb | gpl-2.0 | from pprint import pprint
import numpy as np
"""
Explanation: <figure>
<IMG SRC="../../logo/logo.png" WIDTH=250 ALIGN="right">
</figure>
IHE Python course, 2017
Tuples, lists and sets
T.N.Olsthoorn, Feb 2017
End of explanation
"""
myTuple = ('This', 'is', 'our', 'tuple', 'number', 1)
print("This tuple contains {}... |
ZoeyYiZhou/141BProject | zJupyterNB_Script/ScriptPrecipitationAPI_Kai.ipynb | cc0-1.0 | for i in range(9):
print county_name[i]
zipcode=[93210,93263,93202,93638,93620,95641,95242,95326,93201]
ZipcodeList=[{ "County_N":county_name[i], "zipcode":zipcode[i] } for i in range(len(zipcode))]
COUNTYZIP=pd.DataFrame(ZipcodeList, columns=["County_N", "zipcode"])
COUNTYZIP
"""
Explanation: Lets extract the z... |
trungdong/datasets-provanalytics-dmkd | Application 3 - RRG Messages.ipynb | mit | import pandas as pd
filepath = lambda k: "rrg/depgraphs-%d.csv" % k
# An example of reading the data file
df = pd.read_csv(filepath(5), index_col=0)
df.head()
"""
Explanation: Application 3: RRG Chat Messages
Identifying instructions from chat messages in the Radiation Response Game
Goal: To determine if the proven... |
avloss/serving | example_jupyter/tf_serving_rest_example.ipynb | apache-2.0 | import tensorflow as tf
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))
y = tf.matmul(x,W) + b
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
t... |
solgaardlab/dphox | doc/source/00_photonics.ipynb | mit | import dphox as dp
import numpy as np
import holoviews as hv
from trimesh.transformations import rotation_matrix
hv.extension('bokeh')
import warnings
warnings.filterwarnings('ignore') # ignore shapely warnings
"""
Explanation: Photonic design in dphox
At a glance
In this tutorial, the goal is to demonstrate how pra... |
hbjornoy/DataAnalysis | Homework01/Homework-1-final.ipynb | apache-2.0 | # Imports
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import glob
import csv
import calendar
import webbrowser
from datetime import datetime
# Constants
DATA_FOLDER = 'Data/'
"""
Explanation: Table of Contents
<p><div class="lev1"><a href="#Task-1.-Compiling-Ebola-Data">... |
ktaneishi/deepchem | examples/notebooks/Uncertainty.ipynb | mit | import deepchem as dc
import numpy as np
import matplotlib.pyplot as plot
tasks, datasets, transformers = dc.molnet.load_sampl()
train_dataset, valid_dataset, test_dataset = datasets
model = dc.models.MultitaskRegressor(len(tasks), 1024, uncertainty=True)
model.fit(train_dataset, nb_epoch=200)
y_pred, y_std = model.p... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session03/Day1/ReIntroToDatabasesSolutions.ipynb | mit | import matplotlib.pyplot as plt
%matplotlib notebook
"""
Explanation: Re-Introduction to Databases:
Selecting Sources from the Sloan Digital Sky Survey
Version 0.1
By AA Miller 2017 Apr 19
During the first session of the DSFP we learned about the basics of database operation and writing queries/code in SQL. Here, we ... |
kanhua/pypvcell | demos/Color_of_surface_tmm_2.ipynb | apache-2.0 | from __future__ import division, print_function, absolute_import
%load_ext autoreload
%autoreload 2
from pypvcell.tmm_core import (coh_tmm, unpolarized_RT, ellips, absorp_in_each_layer,
position_resolved, find_in_structure_with_inf)
from numpy import pi, linspace, inf, array
import numpy as n... |
tien-le/uranus | 05_model_evaluation.ipynb | mit | # read in the iris data
from sklearn.datasets import load_iris
iris = load_iris()
# create X (features) and y (response)
X = iris.data
y = iris.target
"""
Explanation: Comparing machine learning models in scikit-learn
From the video series: Introduction to machine learning with scikit-learn
Agenda
How do I choose wh... |
Python4AstronomersAndParticlePhysicists/PythonWorkshop-ICE | notebooks/03_02_TipsAndTricks.ipynb | mit | N_SQUARES = 10
# Don't do this!!!
ugly_list = []
for i in range(N_SQUARES):
ugly_list.append(i**2)
print('ugly list = {}'.format(ugly_list))
# You can do the same in one line
wonderful_list = [ i**2 for i in range(N_SQUARES) ]
print('wonderful list = {}'.format(wonderful_list))
"""
Explanation: Tips and tric... |
mit-crpg/openmc | examples/jupyter/mdgxs-part-i.ipynb | mit | from IPython.display import Image
Image(filename='images/mdgxs.png', width=350)
"""
Explanation: Multigroup (Delayed) Cross Section Generation Part I: Introduction
This IPython Notebook introduces the use of the openmc.mgxs module to calculate multi-energy-group and multi-delayed-group cross sections for an infinite h... |
telescopeuser/workshop_blog | wechat_tool_py3_local/terminal-script-py/lesson_1_terminal_py3.ipynb | mit | # from __future__ import unicode_literals, division
# import time, datetime, requests
import itchat
from itchat.content import *
"""
Explanation: 如何使用和开发微信聊天机器人的系列教程
A workshop to develop & use an intelligent and interactive chat-bot in WeChat
WeChat is a popular social media app, which has more than 800 million month... |
tequa/ammisoft | ammimain/WinPython-64bit-2.7.13.1Zero/notebooks/docs/dplyr_pandas.ipynb | bsd-3-clause | #%load_ext rpy2.ipython
#%R install.packages("nycflights13", repos='http://cran.us.r-project.org')
#%R library(nycflights13)
#%R write.csv(flights, "flights.csv")
"""
Explanation: Tom Augspurger Dplyr/Pandas comparison (copy of 2016-01-01)
See result there
http://nbviewer.ipython.org/urls/gist.githubusercontent.com/To... |
jskDr/jamespy_py3 | poodle/01-001 Poodle Tutorial V02.ipynb | mit | from importlib import reload
import sklearn.linear_model
import pandas as pd
import numpy as np
"""
Explanation: Poodle: Pandas + Sklearn, Tutorial V02
Sung-Jin Kim, Apr 11, 2016
Pandas is a wonderful framework for data management. Also, Sklearn is a powerful tool for machine learning. However, there is no one which m... |
mdeff/ntds_2016 | toolkit/01_ex_acquisition_exploration.ipynb | mit | # Number of posts / tweets to retrieve.
# Small value for development, then increase to collect final data.
n = 20 # 4000
"""
Explanation: A Python Tour of Data Science: Data Acquisition & Exploration
Michaël Defferrard, PhD student, EPFL LTS2
1 Exercise: problem definition
Theme of the exercise: understand the impac... |
Hash--/documents | notebooks/TP Master Fusion/LH-Hands-on-multijunction.ipynb | mit | %pylab
%matplotlib inline
from scipy.constants import c
"""
Explanation: Hands-on LH2: the multijunction launcher
A tokamak a intrinsequally a pulsed machine. In order to perform long plasma discharges, it is necessary to drive a part of the plasma current, in order to limit (or ideally cancel) the magnetic flux consu... |
wuafeing/Python3-Tutorial | 01 data structures and algorithms/01.11 naming slice.ipynb | gpl-3.0 | ###### 0123456789012345678901234567890123456789012345678901234567890'
record = '....................100 .......513.25 ..........'
cost = int(record[20:23]) * float(record[31:37])
"""
Explanation: Previous
1.11 命名切片
问题
你的程序已经出现一大堆已无法直视的硬编码切片下标，然后你想清理下代码。
解决方案
假定你有一段代码要从一个记录字符串中几个固定位置提取出特定的数据字段(比如文件或类似格式)：
End of explan... |
probml/pyprobml | notebooks/misc/splines_numpyro.ipynb | mit | !pip install -q numpyro@git+https://github.com/pyro-ppl/numpyro
!pip install -q arviz
import numpy as np
np.set_printoptions(precision=3)
import matplotlib.pyplot as plt
import math
import os
import warnings
import pandas as pd
from scipy.interpolate import BSpline
from scipy.stats import gaussian_kde
import jax
p... |
AllenDowney/ModSimPy | soln/interest.ipynb | mit | # Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *
from pandas import read_html
"""
Expl... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/n13_sensitivity_analysis.ipynb | mit | # Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
from multiprocessing import Pool
import pickle
%matplotlib inline
%pylab inli... |
openclimatedata/pymagicc | notebooks/Example.ipynb | agpl-3.0 | # NBVAL_IGNORE_OUTPUT
from pprint import pprint
import pymagicc
from pymagicc import MAGICC6
from pymagicc.io import MAGICCData
from pymagicc.scenarios import rcp26, rcp45, rcps
%matplotlib inline
from matplotlib import pyplot as plt
plt.style.use("ggplot")
plt.rcParams["figure.figsize"] = 16, 9
"""
Explanation: Py... |
rileyrustad/pdxapartmentfinder | analysis/Munge.ipynb | mit | with open('../pipeline/data/Day90ApartmentData.json') as f:
my_dict1 = json.load(f)
def listing_cleaner(entry):
print entry
listing_cleaner(my_dict['5465197037'])
type(dframe['bath']['5399866740'])
"""
Explanation: Load the data from our JSON file.
The data is stored as a dictionary of dictionaries in... |
tpin3694/tpin3694.github.io | machine-learning/getting_the_diagonal_of_a_matrix.ipynb | mit | # Load library
import numpy as np
"""
Explanation: Title: Getting The Diagonal Of A Matrix
Slug: getting_the_diagonal_of_a_matrix
Summary: How to get the diagonal of a matrix in Python.
Date: 2017-09-02 12:00
Category: Machine Learning
Tags: Vectors Matrices Arrays
Authors: Chris Albon
Preliminaries
End of exp... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/gapic/automl/showcase_automl_image_object_detection_batch.ipynb | apache-2.0 | import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex client library: AutoML image object detection model for batch prediction
<table alig... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.