repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
cbessega/udacity-deep_learning-project1 | DLND-your-first-network/dlnd-your-first-neural-network.ipynb | apache-2.0 | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
kaiping/incubator-singa | doc/en/docs/notebook/cnn.ipynb | apache-2.0 | from __future__ import division
from builtins import zip
from builtins import str
from builtins import range
from past.utils import old_div
from future import standard_library
from __future__ import print_function
from tqdm import tnrange, tqdm_notebook
standard_library.install_aliases()
import pickle, gzip
# Load th... |
Featuretools/featuretools | docs/source/guides/deployment.ipynb | bsd-3-clause | import featuretools as ft
es_train = ft.demo.load_mock_customer(return_entityset=True)
es_test = ft.demo.load_mock_customer(return_entityset=True, random_seed=33)
"""
Explanation: Deployment
Deployment of machine learning models requires repeating feature engineering steps on new data. In some cases, these steps need... |
walkowiak/mips | tests/ours-on-wiki-test.ipynb | mit | cd ..
"""
Explanation: Prerequisites:
python 3.6, we use the f-string notation ( pep 498 ). Alternatively you can modify the code to use python 3.5-compatible formatting
Best way to get python is via Anaconda
You need to install tinydb: pip install tinydb
You need to build our python wrappers: make bind
You... |
JackDi/phys202-2015-work | assignments/assignment09/IntegrationEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy import integrate
"""
Explanation: Integration Exercise 2
Imports
End of explanation
"""
def integrand(x, a):
return 1.0/(x**2 + a**2)
def integral_approx(a):
# Use the args keyword argument to feed extra a... |
maxis42/ML-DA-Coursera-Yandex-MIPT | 1 Mathematics and Python/Lectures notebooks/12 sample distribution evaluation/sample_distribution_evaluation.ipynb | mit | sample = np.random.choice([1,2,3,4,5,6], 100)
"""
Explanation: Дискретное распределение
Сгенерируем выборку объёма 100 из дискретного распределения с шестью равновероятными исходами.
End of explanation
"""
# посчитаем число выпадений каждой из сторон:
from collections import Counter
c = Counter(sample)
print("Числ... |
unpingco/Python-for-Probability-Statistics-and-Machine-Learning | chapters/machine_learning/notebooks/svm.ipynb | mit | from sklearn.datasets import make_blobs
from sklearn.svm import SVC
sv = SVC(kernel='linear')
"""
Explanation: Support Vector Machines
Support Vector Machines (SVM) originated from the statistical learning theory
developed by Vapnik-Chervonenkis. As such, it represents a deep application of
statistical theory that inc... |
hannorein/reboundx | ipython_examples/TidesConstantTimeLag.ipynb | gpl-3.0 | import rebound
import reboundx
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
def getsim():
sim = rebound.Simulation()
sim.units = ('yr', 'AU', 'Msun')
sim.add(m=0.86) # post-MS Sun
sim.add(m=3.e-6, a=1., e=0.03) # Earth
sim.move_to_com()
rebx = reboundx.Extras(si... |
ml4a/ml4a-guides | examples/info_retrieval/image-path.ipynb | gpl-2.0 | %matplotlib inline
import os
import random
import numpy as np
import pickle
import matplotlib.pyplot
from matplotlib.pyplot import imshow
from PIL import Image
from scipy.spatial import distance
from igraph import *
from tqdm import tqdm
"""
Explanation: Shortest path between images
This notebook demonstrates how, giv... |
aldian/tensorflow | tensorflow/compiler/xla/g3doc/tutorials/compile.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... |
kevinjliang/Duke-Tsinghua-MLSS-2017 | 01A_TensorFlow_Basics.ipynb | apache-2.0 | # Beginning a line with "!" allows you to execute a command in your terminal
# You'll need to install these packages for certain lines of this notebook if you don't already have them.
!conda install -y matplotlib
!pip install -U tqdm
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from tqdm impo... |
CitizensCode/PopulationFlow | PopulationFlow.ipynb | apache-2.0 | flowData = pd.read_csv('../TableD_01110030-eng.csv')
flowData.head()
# Convert place names to unicode
flowData['GEO'] = flowData['GEO'].map(u)
flowData['GEODEST'] = flowData['GEODEST'].map(u)
flowData.head()
# Remove unneeded columns
dropCols = ['Geographical classification',
'Geographical classification.1... |
landlab/landlab | notebooks/tutorials/marine_sediment_transport/simple_submarine_diffuser_tutorial.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from landlab import RasterModelGrid
from landlab.components import SimpleSubmarineDiffuser
grid = RasterModelGrid((3, 51)) # grid has just one row of core nodes
# Close top and bottom boundaries
grid.set_closed_boundaries_at_grid_edges(False, True, False, True)
# W... |
tensorflow/docs-l10n | site/en-snapshot/tfx/tutorials/tfx/penguin_tfdv.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... |
adrn/thejoker | docs/examples/Thompson-black-hole.ipynb | mit | from astropy.io import ascii
from astropy.time import Time
import astropy.units as u
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import pymc3 as pm
import pymc3_ext as pmx
import exoplanet.units as xu
import exoplanet as xo
import corner
import arviz as az
import thejoker as tj
from twobody.... |
turbomanage/training-data-analyst | quests/serverlessml/02_bqml/labs/first_model.ipynb | apache-2.0 | %%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
import os
PROJECT = "your-gcp-project-here" # REPLACE WITH YOUR PROJECT NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# Do not change these
os.environ[... |
mjcollin/2016spr | notebook/Entities_Pandas.ipynb | mit | %matplotlib inline
from __future__ import print_function
import os
from pyspark import SQLContext
from pyspark.sql import Row
import pyspark.sql.functions as sql
#from pyspark.sql.functions import udf, length
import matplotlib.pyplot as plt
import numpy
import math
import matplotlib.pyplot as plt
import seaborn as sns
... |
YuriyGuts/kaggle-quora-question-pairs | notebooks/feature-3rdparty-abhishek.ipynb | mit | from pygoose import *
import os
import warnings
import gensim
from fuzzywuzzy import fuzz
from nltk import word_tokenize
from nltk.corpus import stopwords
from scipy.stats import skew, kurtosis
from scipy.spatial.distance import cosine, cityblock, jaccard, canberra, euclidean, minkowski, braycurtis
"""
Explanatio... |
sorig/shogun | doc/ipython-notebooks/clustering/KMeans.ipynb | bsd-3-clause | from numpy import concatenate, array
from numpy.random import randn
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
num = 200
d1 = concatenate((randn(1,num),10.*randn(1,num)),0)
d2 = concatenate((randn(1,num),10.*randn(1,num)),0)+array([[10.],[0.]])
d3 = concatenate((randn(1,num),10.*randn(1,nu... |
dato-code/tutorials | strata-nyc-2015/feature_engineering/PCA demo.ipynb | apache-2.0 | import matplotlib
matplotlib.use('nbagg')
%matplotlib inline
import graphlab as gl
"""
Explanation: Let's apply PCA to analyze voting patterns
End of explanation
"""
reviews = gl.SFrame.read_csv('../data/yelp/yelp_training_set_review.json', header=False)
reviews = reviews.unpack('X1','')
reviews = reviews.unpack('v... |
jinntrance/MOOC | coursera/ml-foundations/week2/Predicting house prices.ipynb | cc0-1.0 | import graphlab
"""
Explanation: Fire up graphlab create
End of explanation
"""
sales = graphlab.SFrame('home_data.gl/')
sales
"""
Explanation: Load some house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
End of explanation
"""
graphlab.canvas.set_ta... |
espenhgn/LFPy | examples/LFPy-example-03.ipynb | gpl-3.0 | import LFPy
import numpy as np
import os
import sys
from urllib.request import urlopen
import ssl
import zipfile
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
from os.path import join
"""
Explanation: Example plot for LFPy: Single-synapse contribution to the LFP
Copyright (C) 2017 C... |
dnc1994/MachineLearning-UW | ml-regression/lasso.ipynb | mit | import graphlab
"""
Explanation: Regression Week 5: Feature Selection and LASSO (Interpretation)
In this notebook, you will use LASSO to select features, building on a pre-implemented solver for LASSO (using GraphLab Create, though you can use other solvers). You will:
* Run LASSO with different L1 penalties.
* Choose... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_object_raw.ipynb | bsd-3-clause | import mne
import os.path as op
from matplotlib import pyplot as plt
"""
Explanation: The :class:Raw <mne.io.Raw> data structure: continuous data
Continuous data is stored in objects of type :class:Raw <mne.io.Raw>.
The core data structure is simply a 2D numpy array (channels × samples)
(in memory or loade... |
ga7g08/ga7g08.github.io | _notebooks/2015-03-19-Income-Distribution-in-the-UK.ipynb | mit | data_dir = "./DataIncomeInvestigation"
if not os.path.isdir(data_dir):
os.makedirs(data_dir)
"""
Explanation: Getting hold of and cleaning up income data for the UK
In this post I will be downloading data from the HMRC on income in the UK. The data provides the mean
and median income before and after tax split by ... |
terapyon/scsk-oss-x-users-meeting-19 | 4-machine-learning.ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
df = pd.read_pickle("df.db")
"""
Explanation: 機械学習をやってみる
この章で扱うもの
前章で作った各種データを再利用
機械学習を行ってみる
学習結果の評価を行う
機械学習の改善方法
この章で取り扱う手順
学習データとテストデータの分割
sklearnを用いて学習
評価
交差検証
グリットサーチ
各種モデルを試す
評価結果の再確認
End of explanation
"""
df.head()
X = df.iloc[:, 3:]
X.head()
y... |
cweinschenk/cweinschenk | Map_Plotting/NIST_Studies/NIST_Study_Map.ipynb | mit | import pandas as pd
from bokeh.models.glyphs import Circle
from bokeh.plotting import show, output_notebook,figure
from bokeh.models import (
GMapPlot, GMapOptions, Range1d, ColumnDataSource, LinearAxis,
PanTool, WheelZoomTool,HoverTool, TapTool, OpenURL)
output_notebook()
"""
Explanation: Fire research at the... |
MarsUniversity/ece387 | website/block_3_vision/lsn15/lsn15.ipynb | mit | %matplotlib inline
from __future__ import print_function
from __future__ import division
import cv2 # opencv itself
import numpy as np # matrix manipulations
from matplotlib import pyplot as plt # this lets you draw inline pictures in the notebooks
import pylab # t... |
shaivaldalal/CS6053_DataScience | HW4_sd3462.ipynb | mit | import pandas as pd
import numpy as np
from collections import Counter
from sklearn.preprocessing import scale
def cleanBosonData(infile_name):
raw=pd.read_csv(infile_name,header=0,index_col=0,na_values=-999)
raw=raw.replace({'Label':{Counter(raw['Label']).most_common()[0][0]:0,Counter(raw['Label']).most_commo... |
ES-DOC/esdoc-jupyterhub | notebooks/mpi-m/cmip6/models/icon-esm-lr/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mpi-m', 'icon-esm-lr', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: MPI-M
Source ID: ICON-ESM-LR
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Energy... |
explosion/thinc | examples/01_intro_model_definition_methods.ipynb | mit | !pip install "thinc>=8.0.0"
"""
Explanation: Intro to Thinc's Model class, model definition and methods
Thinc follows a functional-programming approach to model definition. Its approach is especially effective for complicated network architectures, and use cases where different data types need to be passed through the... |
rdempsey/web-scraping-data-mining-course | week8/2_bi_dashboards_reporting/2 - Build a Shareable Dashboard Using iPython Notebook and Matplotlib.ipynb | mit | # Import the Python libraries we need
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
# Define a variable for the accidents data file
accidents_data_file = '/Users/robert.dempsey/Dropbox/Private/Art of Skill Hacking/Books/' \
'Python Bu... |
mangecoeur/pineapple | data/examples/python3.5/Packages.ipynb | gpl-3.0 | import pineapple
%pip freeze
"""
Explanation: Packages
Pineapple is a self-contained application that includes a copy of Python 3.5. Upon installation, several useful packages are already installed and available for use without further setup. In addition, Pineapple includes commands to help you manage your Python pack... |
nehal96/Deep-Learning-ND-Exercises | Sentiment Analysis/Sentiment Analysis with Andrew Trask/2-creating-input-output-data.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
quantopian/research_public | notebooks/lectures/ARCH_GARCH_and_GMM/notebook.ipynb | apache-2.0 | import cvxopt
from functools import partial
import math
import numpy as np
import scipy
from scipy import stats
import statsmodels.api as sm
from statsmodels.stats.stattools import jarque_bera
import matplotlib.pyplot as plt
"""
Explanation: Generalized Method of Moments with ARCH and GARCH Models
By Delaney Granizo-... |
probml/pyprobml | notebooks/book1/19/finetune_cnn_jax.ipynb | mit | # Install Augmax for Image Augmentation
try:
import augmax
except ModuleNotFoundError:
%pip install -qq git+https://github.com/khdlr/augmax.git -q
import augmax
# Install the jax-resnet
try:
import jax_resnet
except ModuleNotFoundError:
%pip install -qq git+https://github.com/n2cholas/jax-resnet.g... |
brain-research/guided-evolutionary-strategies | Guided_Evolutionary_Strategies_Demo_Jax.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... |
Iwan-Zotow/VV | C25_GP3phsp.ipynb | mit | import math
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import BEAMphsf
import beam_loader
import H1Dn
import H1Du
import ListTable
%matplotlib inline
def cm2mm(value):
"""
converts cm to mm
"""
return value*10.0
"""
Explanation: Validation and Verification of the 25mm coll... |
BrownDwarf/ApJdataFrames | notebooks/Patten2006.ipynb | mit | %pylab inline
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
"""
Explanation: ApJdataFrames 007: Patten2006
Title: Spitzer IRAC Photometry of M, L, and T Dwarfs
Authors: Brian M Patten, John R Stauffer, Adam S Burrows, Massimo Marengo, Joseph L Hora, Kevin L Luhman, Sarah... |
ALEXKIRNAS/DataScience | CS231n/assignment3/GANs-PyTorch.ipynb | mit | import torch
import torch.nn as nn
from torch.nn import init
from torch.autograd import Variable
import torchvision
import torchvision.transforms as T
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.data import sampler
import torchvision.datasets as dset
import numpy as np
import ... |
HazyResearch/snorkel | tutorials/crowdsourcing/Crowdsourced_Sentiment_Analysis-pandas.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
import os
import numpy as np
from snorkel import SnorkelSession
session = SnorkelSession()
"""
Explanation: Training a Sentiment Analysis LSTM Using Noisy Crowd Labels
This is a version of the crowdsourcing tutorial that uses PySpark using Pandas instead of SparkSQ... |
metpy/MetPy | v0.12/_downloads/e5685967297554788de3cf5858571b23/Natural_Neighbor_Verification.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import ConvexHull, Delaunay, delaunay_plot_2d, Voronoi, voronoi_plot_2d
from scipy.spatial.distance import euclidean
from metpy.interpolate import geometry
from metpy.interpolate.points import natural_neighbor_point
"""
Explanation: Natural Neighbo... |
mikezawitkowski/fireRiskSF | notebooks/reports/1.0-Weather-vs-dept-call-volume.ipynb | mit | from __future__ import division, print_function
import json
import numpy as np
from datetime import datetime
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
query_url = 'https://data.sfgov.org/resource/wbb6-uh78.json?$order=close_dttm%20DESC&$offset={}&$limit={}'
offset = 0... |
AdityaSoni19031997/Machine-Learning | Classifying_datasets/Convolutional_Neural_Networks/scikit_tutorial.ipynb | mit | import sklearn as sk
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn .datasets import fetch_olivetti_faces
faces = fetch_olivetti_faces()
faces.DESCR
faces.keys()
faces.images.shape
faces.data.shape
faces.target.shape
np.max(faces.data)
np.min(faces.data)
np.median(faces.data)... |
arokem/seaborn | doc/docstrings/set_theme.ipynb | bsd-3-clause | sns.barplot(x=["A", "B", "C"], y=[1, 3, 2])
"""
Explanation: By default, seaborn plots will be made with the current values of the matplotlib rcParams:
End of explanation
"""
sns.set_theme()
sns.barplot(x=["A", "B", "C"], y=[1, 3, 2])
"""
Explanation: Calling this function with no arguments will activate seaborn's ... |
abulbasar/machine-learning | Scikit - 05 Classification.ipynb | apache-2.0 | iris.info()
species = iris.Species.unique()
colors = sns.color_palette("hls", 3)
for i, v in enumerate(species):
df = iris[iris.Species == v]
plt.scatter(df["PetalLength"],df["SepalLength"], color = colors[i], label = v)
plt.legend(loc = "upper left")
plt.xlabel("Petal Length")
plt.ylabel("Sepal Length")
y = ... |
guilgautier/DPPy | notebooks/multivariate_jacobi_ensemble.ipynb | mit | # !pip install dppy
"""
Explanation: On two ways to use determinantal point processes for Monte Carlo integration
Guillaume Gautier, Rémi Bardenet, and Michal Valko
See also
- the documentation on ReadTheDocs
- the NeurIPS'19 paper
- the ICML'19 workshop talk (Workshop on Negative Dependence in ML)
<h1>Table of Conten... |
dmittov/shad_python_hw3 | hw3.ipynb | gpl-2.0 | !ls -l corpus
"""
Explanation: Дмитрий Миттов. Большие данные.
Я решил воспользоваться предоставленным корпусом:
End of explanation
"""
import os
import numpy as np
import sys
import nltk
import unicodedata
from collections import Counter, namedtuple
import pickle
import numpy as np
from copy import deepcopy
%matpl... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/ordinal_regression.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import scipy.stats as stats
from statsmodels.miscmodels.ordinal_model import OrderedModel
"""
Explanation: Ordinal Regression
End of explanation
"""
url = "https://stats.idre.ucla.edu/stat/data/ologit.dta"
data_student = pd.read_stata(url)
data_student.head(5)
data_student.d... |
spectralDNS/shenfun | binder/Torus-biharmonic.ipynb | bsd-2-clause | from shenfun import *
from shenfun.la import SolverGeneric2ND
import sympy as sp
from IPython.display import Math
N = 24
R = 3
r, theta, phi = psi = sp.symbols('x,y,z', real=True, positive=True)
rv = ((R + r*sp.cos(theta))*sp.cos(phi), (R + r*sp.cos(theta))*sp.sin(phi), r*sp.sin(theta))
def discourage_powers(expr):
... |
jbwhit/jupyter-best-practices | notebooks/Advanced-Notebook-Tricks.ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
from pandas.tools.plotting import scatter_matrix
from sklearn.datasets import load_boston
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context('poster')
sns.set_style('whitegrid')
plt.rcParams['figure.figsize'] = 12, 8 ... |
willettk/gzhubble | python/notebooks/zeta_models_compared.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
from astropy.table import Table,Column
from astropy.io import fits
from scipy import optimize
from scipy.optimize import minimize
from scipy import stats
from scipy.stats import distributions as dist
import numpy as np
import os
import requests
import warnings
im... |
madlib/madlib-examples | Prediction-metrics-demo-1.ipynb | apache-2.0 | %load_ext sql
# %sql postgresql://gpdbchina@10.194.10.68:55000/madlib
%sql postgresql://fmcquillan@localhost:5432/madlib
%sql select madlib.version();
"""
Explanation: Prediction metrics
This module provides a set of metrics to evaluate the quality of predictions of a model. A typical function will take a set of "pr... |
steinam/teacher | jup_notebooks/data-science-ipython-notebooks-master/deep-learning/theano-tutorial/intro_theano/logistic_regression.ipynb | mit | import os
import requests
import gzip
import six
from six.moves import cPickle
if not os.path.exists('mnist.pkl.gz'):
r = requests.get('http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz')
with open('mnist.pkl.gz', 'wb') as data_file:
data_file.write(r.content)
with gzip.open('mnist.pkl.gz... |
Upward-Spiral-Science/claritycontrol | code/a10_extended_exploratory.ipynb | apache-2.0 | FEATURES_PATH = '../code/data/roi_features/features.csv' # use your own path
import numpy as np
import matplotlib
matplotlib.use('AGG') # avoid some error in matplotlib, delete this line if the following doesn't work
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import jgraph as ig
%matplot... |
CopernicusMarineInsitu/INSTACTraining | PythonNotebooks/IndexFilePlots/IndexFile_Folium_Visalization.ipynb | mit | indexfile = "datafiles/index_latest.txt"
"""
Explanation: Index file visualization
This notebook shows an easy way to represent the In Situ data positions using the index files.<br>
For this visualization of a sample <i>index_latest.txt</i> dataset of the Copernicus Marine Environment Monitoring Service.
End of explan... |
rringham/deep-learning-notebooks | udacity/3_regularization.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 numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
"""
Explanation: Deep Learning
Assignment 3
Previously in 2_fullyconnected.ipynb, you tra... |
kubeflow/code-intelligence | Issue_Embeddings/notebooks/03_Create_Model.ipynb | mit | def pass_through(x):
return x
data_lm = load_data(path, bs=120)
"""
Explanation: 1. Load DataBunch
You have to redefine or import any custom you defined in the last step, because the data bunch is going to look for that.
End of explanation
"""
learn = language_model_learner(data=data_lm,
... |
diogo149/hyperparamter_optimization_from_scratch | Hyperparameter Optimization.ipynb | apache-2.0 | def smbo(generator_fn,
score_fn,
evaluation_fn,
num_dims,
num_initial_points=10,
num_iter=200,
num_generated=10000):
"""
general sequential model based optimization to minimize the result
of some presumably expensive function
generator_fn:
funct... |
survey-methods/samplics | docs/source/tutorial/sample_weights.ipynb | mit | import numpy as np
import pandas as pd
import samplics
from samplics.datasets import PSUSample, SSUSample
from samplics.weighting import SampleWeight
"""
Explanation: Sample weight adjustment
The objective of this tutorial is to familiarize ourselves with SampleWeight the samplics class for adjusting sample weights. ... |
JeffAbrahamson/MLWeek | practicum/03_sklearn/sklearn-intro.ipynb | gpl-3.0 | import numpy as np
import scipy as sp
from sklearn import datasets
iris = datasets.load_iris()
digits = datasets.load_digits()
boston = datasets.load_boston()
"""
Explanation: Scikit Learn et données
Scikit-learn propose quelques ensembles de données, notamment iris et digits (classification) et le boston house price... |
ES-DOC/esdoc-jupyterhub | notebooks/awi/cmip6/models/sandbox-1/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'awi', 'sandbox-1', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: AWI
Source ID: SANDBOX-1
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radiat... |
jastarex/DeepLearningCourseCodes | 01_TF_basics_and_linear_regression/tensorflow_basic.ipynb | apache-2.0 | import tensorflow as tf
a = tf.constant(100)
with tf.Session() as sess:
print sess.run(a)
#syntactic sugar
print a.eval()
# or
sess = tf.Session()
print sess.run(a)
# print a.eval() # this will print out an error
"""
Explanation: TensorFlow基础
此处使用python2写法，可以用pyenv安装anaconda2-4.4.0亦即python 2.7对... |
sevo/closure_decorator | Closure a dekorator.ipynb | mit | def f1(a):
print(a)
print(b)
f1(3)
b = 6
f1(3)
"""
Explanation: Obsah dnesnej prednasky
1. Rozsah platnosti premennej
2. Vnorena funkcia
3. Closure
4. Decorator
Closure - Uzaver
Funkcia, ktora pouziva neglobalnu premennu definovanu mimo svojho tela (vysvetlim)
Da sa to pouzit napriklad na asynchronne progra... |
dsacademybr/PythonFundamentos | Cap03/Notebooks/DSA-Python-Cap03-Exercicios-Loops-Condiconais.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 3</font>
Download: http://github.com/dsacademybr
End of explanation
"""
# Exerc... |
ddtm/dl-course | Seminar11/Seminar11_intro.ipynb | mit | with open("./train.csv") as fin:
ids,words,transcripts = zip(*[line.split(',') for line in list(fin)[1:]])
words = [word+"@" for word in words]
transcripts = [["START"]+ts[:-2].split()+["END"] for ts in transcripts]
for word, trans in zip(words[:5],phonemes[:5]):
print word,':',trans
"""
Explanation: ... |
Azaruddinjailor/ml_lab_ecsc_306 | labwork/lab1/TF_Basic_1.ipynb | apache-2.0 | import tensorflow as tf
# creates nodes in a graph
# "construction phase"
x1 = tf.constant(5)
x2 = tf.constant(6)
result = tf.multiply(x1,x2)
print(result)
"""
Explanation: TensorFlow works by first defining and describing our model in abstract, and then, when we are ready, we make it a reality in the session. The des... |
ML4DS/ML4all | U3.PCA/PCA_student.ipynb | mit | # Basic imports
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
"""
Explanation: Principal Component Analysis
The code in this notebook has been taken from a notebook in the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub.
The c... |
karlstroetmann/Artificial-Intelligence | Python/1 Search/N-Queens-Frame.ipynb | gpl-2.0 | start = ()
"""
Explanation: The 8-Queens Puzzle
We represent solutions to the 8-queens puzzle as tuples of the form
$$ (r_0, \cdots, r_7), $$
where $r_i$ is the row of the queen in column $i$. We start counting from $0$ because this is the way it is done in Python.
In general, states are defined as tuples of the form... |
TimothyHelton/k2datascience | notebooks/Linear_Models_Exercises.ipynb | bsd-3-clause | import os
import os.path as osp
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
import seaborn as sns
import statsmodels.formula.api as smf
from statsmodels.graphics.regressionplots import influence_plot
fr... |
jseabold/statsmodels | examples/notebooks/statespace_local_linear_trend.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
from scipy.stats import norm
import statsmodels.api as sm
import matplotlib.pyplot as plt
"""
Explanation: State space modeling: Local Linear Trends
This notebook describes how to extend the statsmodels statespace classes to create and estimate a custom model.... |
nishant009/sdcnd_traffic_sign_classification | Traffic_Sign_Classifier.ipynb | mit | # Load pickled data
import pickle
training_file = '../data/train.p'
validation_file = '../data/valid.p'
testing_file = '../data/test.p'
with open(training_file, mode='rb') as f:
train = pickle.load(f)
with open(validation_file, mode='rb') as f:
valid = pickle.load(f)
with open(testing_file, mode='rb') as f:
... |
NeuroDataDesign/seelviz | Tony/ipynb/april3/s275_Registration.ipynb | apache-2.0 | from ndreg import *
import matplotlib
import ndio.remote.neurodata as neurodata
"""
Explanation: Volumetric Registration and Analysis
In this tutorial we align a partial mouse brain to the Allen Referece Atlas (ARA). Thus the ARA is our reference image, it consists of image data stored in the default "average" channe... |
sot/aca_stats | fit_acq_model-2018-11-dev/fit_acq_model-2018-11-binned-poly-mono.ipynb | bsd-3-clause | import sys
import os
from itertools import count
from pathlib import Path
sys.path.insert(0, str(Path(os.environ['HOME'], 'git', 'skanb', 'pea-test-set')))
import utils as asvt_utils
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table, vstack
from astropy.time import Time
import tables
fr... |
jmhsi/justin_tinker | data_science/courses/learning_dl_packages/models/research/slim/slim_walkthrough.ipynb | apache-2.0 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
import math
import numpy as np
import tensorflow as tf
import time
from datasets import dataset_utils
# Main slim library
from tensorflow.c... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_read_noise_covariance_matrix.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
from os import path as op
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname_cov = op.join(data_path, 'MEG', 'sample', 'sample_audvis-cov.fif')
fname_evo = op.join(data_path,... |
brean/python-pathfinding | pathfinding.ipynb | mit | from pathfinding.core.diagonal_movement import DiagonalMovement
from pathfinding.core.grid import Grid
from pathfinding.finder.a_star import AStarFinder
"""
Explanation: A simple usage example to find a path using A*.
End of explanation
"""
matrix = [
[1, 1, 1],
[1, 0, 1],
[1, 1, 1]
]
"""
Explanation: Create ... |
cosmolejo/Fisica-Experimental-3 | Pre-Labs/Espectroscopia/Basics.ipynb | gpl-3.0 | import sys
print ('maximo float: ',sys.float_info.max)
print ('minimo float: ',sys.float_info.min)
print ('int: numero de bits por digito ',sys.int_info.bits_per_digit, 'esto es: ', 2**sys.int_info.bits_per_digit)
import matplotlib.pyplot as plt
import numpy as np
plt.close("all")
x=np.linspace(-10,10)
cuadrado=pl... |
ES-DOC/esdoc-jupyterhub | notebooks/pcmdi/cmip6/models/sandbox-3/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'pcmdi', 'sandbox-3', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: PCMDI
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topics: Tracers.
Propertie... |
whitead/numerical_stats | unit_5/hw_2016/homework_5_key.ipynb | gpl-3.0 | import numpy as np
ints = np.arange(1,21)
pows = 2**ints
print(pows)
print(pows[9], pows[19])
"""
Explanation: Homework 5
CHE 116: Numerical Methods and Statistics
Prof. Andrew White
Version 1.1 (2/9/2016)
1. Python Practice (20 Points)
Answer the following problems in Python
[4 points] Using Numpy, create the first... |
tensorflow/docs-l10n | site/en-snapshot/guide/migrate/tensorboard.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... |
rbiswas4/AnalyzeSN | examples/LightCurve.ipynb | mit | import sncosmo
import analyzeSN as ans
import numpy as np
from analyzeSN import LightCurve
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
"""
Explanation: The LightCurve class
Background
What kind of physical data are we representing and what do these quantities mean?
Astrophysica... |
bjshaw/phys202-2015-work | assignments/assignment07/AlgorithmsEx02.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
"""
Explanation: Algorithms Exercise 2
Imports
End of explanation
"""
def find_peaks(a):
"""Find the indices of the local maxima in a sequence."""
c = []
for n in range(0,len(a)):
if n >= 1 and n <= l... |
jnarhan/Breast_Cancer | src/models/JN_BC_Diff_ Diagnosis.ipynb | mit | import os
import sys
import time
import numpy as np
from tqdm import tqdm
import sklearn.metrics as skm
from sklearn import metrics
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from skimage import color
import keras.callbacks as cb
import keras.utils.np_utils as np_utils
from ker... |
robertoalotufo/ia898 | src/phasecorr.ipynb | mit | import numpy as np
def phasecorr(f,h):
F = np.fft.fftn(f)
H = np.fft.fftn(h)
T = F * np.conjugate(H)
R = T/np.abs(T)
g = np.fft.ifftn(R)
return g.real
"""
Explanation: Function phasecorr
Synopse
Computes the phase correlation of two images.
g = phasecorr(f,h)
OUTPUT
g: Image. Phase correlation... |
angelmtenor/deep-learning | batch-norm/Batch_Normalization_Exercises.ipynb | mit | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
"""
Explanation: Batch Normalization – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a con... |
InsightLab/data-science-cookbook | 2019/09-clustering/cl_PedroMotta.ipynb | mit | # import libraries
import math
# linear algebra
import numpy as np
# data processing
import pandas as pd
# data visualization
from matplotlib import pyplot as plt
from sklearn import cluster
# load the data with pandas
dataset = pd.read_csv('dataset.csv', header=None)
dataset = np.array(dataset)
plt.scatter(datas... |
dtamayo/rebound | ipython_examples/Starman.ipynb | gpl-3.0 | import rebound
import numpy as np
%matplotlib inline
"""
Explanation: Starman
This notebook integrates the orbit of Elon Musk's Tesla and Starman. The orbital parameters are taken from Bill Gray's website: https://www.projectpluto.com/temp/j95.htm#elements.
End of explanation
"""
sim = rebound.Simulation()
sim.add([... |
michaelaye/planet4 | notebooks/clustering development.ipynb | isc | from planet4.dbscan import DBScanner
from planet4 import io, clustering, plotting, markings, dbscan
import seaborn as sns
sns.set_context('notebook')
blotchcols = markings.Blotch.to_average
fancols = markings.Fan.to_average
# not automatically initialized
%matplotlib ipympl
import socket
if socket.gethostname().star... |
cpcloud/ibis | docs/tutorial/05-IO-Create-Insert-External-Data.ipynb | apache-2.0 | !curl -LsS -o $TEMPDIR/geography.db 'https://storage.googleapis.com/ibis-tutorial-data/geography.db'
import os
import tempfile
import ibis
ibis.options.interactive = True
connection = ibis.sqlite.connect(
os.path.join(tempfile.gettempdir(), 'geography.db')
)
"""
Explanation: Creating and Inserting Data
Setup
E... |
zifeo/Food-habits | 1-preprocessing/restaurants/lafourchette.ipynb | apache-2.0 | restaurant_ids = []
restaurant_names = []
websites = []
for link in df.index.values:
link_split = link.split("/")
websites.append(link_split[2])
restaurant_names.append(link_split[-2])
restaurant_ids.append(link_split[-1])
len(np.unique(restaurant_ids)) == len(restaurant_ids)
df = df.set_index([resta... |
neurokernel/vision | notebooks/vision.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as p
"""
Explanation: Vision Model Demo
This notebook illustrates how to run a Neurokernel-based model of portions of the fly's vision system.
Background
In addition to the retina where the photo-transduction takes place, the optic
lobe of the Drosop... |
andmax/gpufilter | python/alg5epe.ipynb | mit | import math
import cmath
import numpy as np
from scipy import ndimage, linalg
from skimage.color import rgb2gray
from skimage.measure import structural_similarity as ssim
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
%matplotlib inline
plt.gray() # to plot gray images using gray scale
... |
InsightLab/data-science-cookbook | 2020/trabalho-01/Exemplo de relatório.ipynb | mit | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: (Micro)Relatório do catálogo da Netflix
Este (micro)relatório apresenta algumas informações extraídas de um dataset com o catálogo da Netflix, disponível no Kaggle.
As bibliotecas utilizadas são as seguintes:
End of explanation
"""... |
mne-tools/mne-tools.github.io | 0.18/_downloads/7b57fca16e7c12b88411529a749018d6/plot_sensors_decoding.ipynb | bsd-3-clause | # sphinx_gallery_thumbnail_number = 6
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
import mne
from mne.datasets import sample
from mne.decoding import (SlidingEstimato... |
tensorflow/examples | courses/udacity_intro_to_tensorflow_for_deep_learning/l09c05_nlp_tweaking_the_model.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... |
whitead/numerical_stats | unit_4/hw_2018/Homework_4_Key.ipynb | gpl-3.0 | #1.1
import math
print('{:.4}'.format(math.pi))
#1.2 - no for loop
print('{:5.4}'.format(math.sqrt(1)))
print('{:5.4}'.format(math.sqrt(2)))
print('{:5.4}'.format(math.sqrt(3)))
print('{:5.4}'.format(math.sqrt(4)))
print('{:5.4}'.format(math.sqrt(5)))
#1.2 - for loop
for i in range(1, 6):
print('{:5.4}'.format(ma... |
whitead/numerical_stats | unit_9/hw_2019/homework_9_key.ipynb | gpl-3.0 | import numpy as np
"""
Explanation: Homework 9 Key
CHE 116: Numerical Methods and Statistics
2/21/2019
End of explanation
"""
import numpy as np
m = np.zeros((6, 12))
m[:,1] = 2**np.arange(6)
m[:,2] = 4
print(m)
"""
Explanation: 1. Creating Matrices (12 Points)
Create the following matrices using the given constra... |
zraxon/Oslo-Model | Oslo Model.ipynb | gpl-3.0 | import analysis
import matplotlib.pyplot as plt
import numpy as np
import pickle
from collections import OrderedDict
from pile import Pile
from scipy.optimize import curve_fit
"""
Explanation: Zixu Zack Xiang, 01/02/17
The following notebook was ran in a Python 3 environment. The code was written to maximise clarity... |
xiongzhenggang/xiongzhenggang.github.io | AI/ML/专利.ipynb | gpl-3.0 | from scipy.stats import pearsonr
def pearsonrs(x,y):
pears = []
# 循环列处理
for arr in x.T:
pears.append(pearsonr(arr,Y)[0])
return np.array(pears)
pears = np.abs(pearsonrs(X1,Y))
index = np.argsort(-pears)
pears,index
# 选取相关系数大于0.1的，即前9个特征
X2 = X1[:,index[0:9]]
X2.shape
# 1，分别将数据分为训练集和测试集,随机分配... |
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