repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
theamazingfedex/ml-project-4 | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
bpgc-cte/python2017 | Week 3/Lecture_5_Introdution_to_Functions.ipynb | mit | #Example_1: return keyword
def straight_line(slope,intercept,x):
"Computes straight line y value"
y = slope*x + intercept
return y
print("y =",straight_line(1,0,5)) #Actual Parameters
print("y =",straight_line(0,3,10))
#By default, arguments have a positional behaviour
#Each of the parameters here is call... |
cirla/tulipy | README.ipynb | lgpl-3.0 | import numpy as np
import tulipy as ti
ti.TI_VERSION
DATA = np.array([81.59, 81.06, 82.87, 83, 83.61,
83.15, 82.84, 83.99, 84.55, 84.36,
85.53, 86.54, 86.89, 87.77, 87.29])
"""
Explanation: tulipy
Python bindings for Tulip Indicators
Tulipy requires numpy as all inputs and output... |
hunterowens/machine-learning-in-edu | EduMLvsRuleBased.ipynb | cc0-1.0 | ## Imports
import pandas as pd
import seaborn as sns
sns.set(color_codes=True)
import matplotlib.pyplot as plt
"""
Explanation: Comparision of Machine Learning Methods vs Rule Based
Traditionally, Educational Institutions use rule based models to generate risk score which then informs resource allocation. For examp... |
albertfxwang/grizli | examples/Fit-with-Photometry.ipynb | mit | %matplotlib inline
import os
import numpy as np
import matplotlib.pyplot as plt
import astropy.io.fits as pyfits
import drizzlepac
import grizli
from grizli.pipeline import photoz
from grizli import utils, prep, multifit, fitting
utils.set_warnings()
print('\n Grizli version: ', grizli.__version__)
# Requires eazy... |
rsterbentz/phys202-2015-work | assignments/assignment11/OptimizationEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
"""
Explanation: Optimization Exercise 1
Imports
End of explanation
"""
def hat(x,a,b):
potential = -a*x**2 + b*x**4
return potential
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(0.0, 1.0, 1.0)==0.0
assert hat(1.... |
tkphd/pycalphad | examples/EquilibriumWithOrdering.ipynb | mit | # Only needed in a Jupyter Notebook
%matplotlib inline
# Optional plot styling
import matplotlib
matplotlib.style.use('bmh')
import matplotlib.pyplot as plt
from pycalphad import equilibrium
from pycalphad import Database, Model
import pycalphad.variables as v
import numpy as np
"""
Explanation: Equilibrium Propertie... |
nholtz/structural-analysis | matrix-methods/frame2d/AA-3203-2019-hw6.ipynb | cc0-1.0 | from Frame2D import Frame2D
theframe = Frame2D('3203/2019/hw-6')
"""
Explanation: CIVE 3203 2019 HW-6
Compare the results here with those given be the slope-deflection method
in the solution of HW-6, CIVE3203, Fall 2019.
End of explanation
"""
%%Table nodes
NODEID,X,Y,Z
a,0,5000
b,8000,5000
c,14000,5000
d,8000,0
"... |
Featuretools/featuretools | docs/source/getting_started/woodwork_types.ipynb | bsd-3-clause | import featuretools as ft
ft.list_logical_types()
"""
Explanation: Woodwork Typing in Featuretools
Featuretools relies on having consistent typing across the creation of EntitySets, Primitives, Features, and feature matrices. Previously, Featuretools used its own type system that contained objects called Variables. N... |
IBMStreams/streamsx.topology | samples/python/topology/notebooks/ViewDemo/ViewDemo.ipynb | apache-2.0 | from streamsx.topology.topology import Topology
from streamsx.topology import context
from some_module import jsonRandomWalk
#from streamsx import rest
import json
import logging
# Define topology & submit
rw = jsonRandomWalk()
top = Topology("myTop")
stock_data = top.source(rw)
# The view object can be used to retri... |
jarrison/trEFM-learn | Examples/.ipynb_checkpoints/demo-checkpoint.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from trEFMlearn import data_sim
%matplotlib inline
"""
Explanation: Welcome!
Let's start by assuming you have downloaded the code, and ran the setup.py . This demonstration will show the user how predict the time constant of their trEFM data using the methods of stati... |
bird-house/birdy | birdy/ipyleafletwfs/examples/ipyleafletwfs_guide.ipynb | apache-2.0 | from birdy import IpyleafletWFS
from ipyleaflet import Map
url = 'http://boreas.ouranos.ca/geoserver/wfs'
version = '2.0.0'
wfs_connection = IpyleafletWFS(url, version)
demo_map = Map(center=(46.42, -64.14), zoom=8)
demo_map
"""
Explanation: How to use the WFSGeojsonLayer class
This class provides WFS layers for ip... |
ctralie/TUMTopoTimeSeries2016 | SlidingWindow2-PersistentHomology.ipynb | apache-2.0 | # Do all of the imports and setup inline plotting
import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
from matplotlib import gridspec
from mpl_toolkits.mplot3d import Axes3D
from sklearn.decomposition import PCA
from scipy.interpolate import InterpolatedUnivariateSpline
import ipywidgets as widg... |
machinelearningdeveloper/lc101-kc | November 28, 2016/Covered in class 11-28-2016.ipynb | unlicense | """Assignment between variables creates aliases."""
animal = "giraffe"
creature = animal
print("Is creature an alias of animal?", creature is animal)
"""Assignment of the same value to different variables does not necessarily create aliases."""
weather_next_5_days = ["Sunny", "Partly sunny", "Cloudy", "Sunny", ... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/introduction_to_tensorflow/solutions/2a_dataset_api.ipynb | apache-2.0 | import json
import math
import os
from pprint import pprint
import numpy as np
import tensorflow as tf
print(tf.version.VERSION)
"""
Explanation: TensorFlow Dataset API
Learning Objectives
1. Learn how use tf.data to read data from memory
1. Learn how to use tf.data in a training loop
1. Learn how use tf.data to rea... |
juancarlosqr/datascience | python/playground/jupyter/keyboard-shortcuts.ipynb | mit | # mode practice
"""
Explanation: Keyboard shortcuts
In this notebook, you'll get some practice using keyboard shortcuts. These are key to becoming proficient at using notebooks and will greatly increase your work speed.
First up, switching between edit mode and command mode. Edit mode allows you to type into cells whi... |
ivergara/science_notebooks | Two levels, two electrons.ipynb | gpl-3.0 | import numpy as np
import itertools
from operator import add
from functools import reduce
#from itertools import combinations, permutations
"""
Explanation: Transitions for a two level system with an electron
End of explanation
"""
initial = [1, 0, 0, 0, 1, 0, 0, 0]
final = [0, 0, 0, 0, 1, 0, 1, 0]
"""
Explanation... |
voyageth/udacity-Deep_Learning_Foundations_Nanodegree | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
Milad7m/motion | DM_05_04.ipynb | mit | %matplotlib inline
import pylab
import numpy as np
import pandas as pd
from sklearn.svm import OneClassSVM
from sklearn.covariance import EllipticEnvelope
pylab.rcParams.update({'font.size': 14})
"""
Explanation: DM_05_04
Import Libraries
End of explanation
"""
df = pd.read_csv("AnomalyData.csv")
df.head()
"""
Exp... |
Kaggle/learntools | notebooks/data_cleaning/raw/ex4.ipynb | apache-2.0 | from learntools.core import binder
binder.bind(globals())
from learntools.data_cleaning.ex4 import *
print("Setup Complete")
"""
Explanation: In this exercise, you'll apply what you learned in the Character encodings tutorial.
Setup
The questions below will give you feedback on your work. Run the following cell to set... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/feature_engineering/labs/3_keras_basic_feat_eng-lab.ipynb | apache-2.0 | # Install Sklearn
!python3 -m pip install --user sklearn
# Ensure the right version of Tensorflow is installed.
!pip3 freeze | grep 'tensorflow==2\|tensorflow-gpu==2' || \
!python3 -m pip install --user tensorflow==2
import os
import tensorflow.keras
import matplotlib.pyplot as plt
import pandas as pd
import tensorf... |
mcleonard/sampyl | examples/Abalone Model.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import sampyl as smp
from sampyl import np
import pandas as pd
plt.style.use('seaborn')
plt.rcParams['font.size'] = 14.
plt.rcParams['legend.fontsize'] = 14.0
plt.rcParams['axes.titlesize'] = 16.0
plt.rcParams['axes.lab... |
prashanti/similarity-experiment | src/Notebooks/MetricComparison.ipynb | mit | import matplotlib.lines as mlines
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import math
import json
%matplotlib inline
"""
Explanation: Parameters used
Query profile size: 10
Number of query profiles: 5
Information Content: Annotation IC
Profile aggregation: Best Pairs
Direction... |
SlipknotTN/udacity-deeplearning-nanodegree | gan_mnist/Intro_to_GANs_Exercises.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
sbenthall/bigbang | examples/experimental_notebooks/Show Interaction Graph.ipynb | agpl-3.0 | %matplotlib inline
"""
Explanation: This notebook shows how BigBang can be used to display a graph of interactions in the mailing list over some period of time.
First we'll make the I Python notebook display computed visualizations inline.
End of explanation
"""
from bigbang.archive import Archive
import bigbang.par... |
mne-tools/mne-tools.github.io | 0.23/_downloads/efd09079125b2bd222e2dd62aaaccfa4/source_space_snr.ipynb | bsd-3-clause | # Author: Padma Sundaram <tottochan@gmail.com>
# Kaisu Lankinen <klankinen@mgh.harvard.edu>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
import numpy as np
import matplotlib.pyplot as plt
print(__doc__)
data_path = s... |
franzpl/StableGrid | jupyter_notebooks/clock_frequency_accuracy.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: On the influence of temperature in 16 MHz clock frequency measurement
This notebook discusses the behaviour of accuracy and stability of 2 types of clock generators (ceramic resonator & quartz) under influence of temperature for highly precision frequ... |
hpparvi/ldtk | notebooks/A2_redifining_stellar_edge.ipynb | gpl-2.0 | %pylab inline
from scipy.interpolate import interp1d
from os.path import join
import seaborn as sb
sb.set_style('white')
from ldtk.core import SIS, ldtk_root
sis = SIS(join(ldtk_root,'cache_lowres','Z-0.0','lte05800-5.50-0.0.PHOENIX-ACES-AGSS-COND-SPECINT-2011.fits'))
mu_o, z_o, ip_o = sis.mu, sis.z, sis.intensity_p... |
mikelseverson/Udacity-Deep_Learning-Nanodegree | tv-script-generation/.ipynb_checkpoints/dlnd_tv_script_generation-checkpoint.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
usantamaria/ipynb_para_docencia | 05_python_errores/errores.ipynb | mit | """
IPython Notebook v4.0 para python 3.0
Librerías adicionales: IPython, pdb
Contenido bajo licencia CC-BY 4.0. Código bajo licencia MIT.
(c) Sebastian Flores, Christopher Cooper, Alberto Rubio, Pablo Bunout.
"""
# Configuración para recargar módulos y librerías dinámicamente
%reload_ext autoreload
%autoreload 2
# C... |
mliu49/RMG-stuff | Thermo/sdata134k_small_polycyclic.ipynb | mit | from rmgpy.data.rmg import RMGDatabase
from rmgpy import settings
from rmgpy.species import Species
from rmgpy.molecule import Group
from rmgpy.rmg.main import RMG
from IPython.display import display
import numpy as np
import os
import pandas as pd
from pymongo import MongoClient
import logging
logging.disable(logging.... |
kmunve/APS | aps/notebooks/aps_terrain_analysis.ipynb | mit | # -*- coding: utf-8 -*-
%matplotlib inline
from __future__ import print_function
import pylab as plt
import datetime
import netCDF4
import numpy as np
import numpy.ma as ma
from linecache import getline
plt.rcParams['figure.figsize'] = (14, 6)
"""
Explanation: APS terrain analysis
Imports
End of explanation
"""
##... |
NifTK/NiftyNet | demos/Learning_Rate_Decay/Demo_for_learning_rate_decay_application.ipynb | apache-2.0 | import os,sys
import matplotlib.pyplot as plt
import numpy as np
niftynet_path='your/niftynet/path' # Set your NiftyNet root path here
os.environ['niftynet_config_home'] = niftynet_path
os.chdir(niftynet_path)
"""
Explanation: Demo for Learning Rate Decay Application
This demo will address how to make use of the lear... |
tensorflow/docs-l10n | site/zh-cn/hub/tutorials/retrieval_with_tf_hub_universal_encoder_qa.ipynb | apache-2.0 | # Copyright 2019 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... |
mne-tools/mne-tools.github.io | 0.22/_downloads/2e7ef25ccf0fd2af7902f12debe11fc1/plot_stats_cluster_1samp_test_time_frequency.ipynb | bsd-3-clause | # Authors: 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 permutation_cluster_1samp_test
from mne.datasets import sample
print(__doc__)
"""
Explanation: Non-par... |
amueller/scipy-2017-sklearn | notebooks/04.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: Training and Testing Data
To evaluate how well our supervised models generalize, we can split our data into a training and a ... |
RainFool/Udacity_Anwser_RainFool | Project1/boston_housing.ipynb | mit | # 载入此项目所需要的库
import numpy as np
import pandas as pd
import visuals as vs # Supplementary code
# 检查你的Python版本
from sys import version_info
if version_info.major != 2 and version_info.minor != 7:
raise Exception('请使用Python 2.7来完成此项目')
# 让结果在notebook中显示
%matplotlib inline
# 载入波士顿房屋的数据集
data = pd.read_csv('housi... |
oscarmore2/deep-learning-study | tflearn-digit-recognition/TFLearn_Digit_Recognition.ipynb | mit | # Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
"""
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This... |
junhwanjang/DataSchool | Lecture/09. 기초 확률론 3 - 확률모형/3) 가우시안 정규 분포.ipynb | mit | mu = 0
std = 1
rv = sp.stats.norm(mu, std)
rv
"""
Explanation: 가우시안 정규 분포
가우시안 정규 분포(Gaussian normal distribution), 혹은 그냥 간단히 정규 분포라고 부르는 분포는 자연 현상에서 나타나는 숫자를 확률 모형으로 모형화할 때 가장 많이 사용되는 확률 모형이다.
정규 분포는 평균 $\mu$와 분산 $\sigma^2$ 이라는 두 개의 모수만으로 정의되며 확률 밀도 함수(pdf: probability density function)는 다음과 같은 수식을 가진다.
$$ \mathcal{N... |
wgong/open_source_learning | projects/Open_Food/open-food-100k.ipynb | apache-2.0 | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: Data Incubator Fellowship Semifinalist Challenge
<a href="mailto:wen.g.gong@gmail.com">Wen Gong</a>
Motivation
<br>
<font color=red size=+3>Know what you eat, </font>
<font color=green size=+3> Gain insight into food.</font>
<a href=https... |
daniel-koehn/Theory-of-seismic-waves-II | 07_SH_waves_in_moons_and_planets/4_2D_SHaxi_FD_modelling_ganymede.ipynb | gpl-3.0 | # Execute this cell to load the notebook's style sheet, then ignore it
from IPython.core.display import HTML
css_file = '../style/custom.css'
HTML(open(css_file, "r").read())
"""
Explanation: Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2018 by D. Koehn, notebook styl... |
hamnonlineng/hamnonlineng | examples/Example_7th_order_Hamiltonian-no_solutions.ipynb | bsd-3-clause | import hamnonlineng as hnle
letters = 'abcde'
resonant = [hnle.Monomial(1, 'aabbEEC'), hnle.Monomial(1,'abddEEC')]
op_sum = hnle.operator_sum(letters)
sine_exp = (
hnle.sin_terms(op_sum, 3)
+hnle.sin_terms(op_sum, 5)
+hnle.sin_terms(op_sum, 7)
)
off_resonant = hnle.drop_sin... |
sympy/scipy-2017-codegen-tutorial | notebooks/23-lambdify-Tc99m.ipynb | bsd-3-clause | import sympy as sym
sym.init_printing()
symbs = t, l1, l2, x0, y0, z0 = sym.symbols('t lambda_1 lambda_2 x0 y0 z0', real=True, nonnegative=True)
funcs = x, y, z = [sym.Function(s)(t) for s in 'xyz']
inits = [f.subs(t, 0) for f in funcs]
diffs = [f.diff(t) for f in funcs]
exprs = -l1*x, l1*x - l2*y, l2*y
eqs = [sym.Eq(... |
owenjhwilliams/ASIIT | FindSwirlLocs.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import h5py
from importlib import reload
import PIVutils
#PIVutils = reload(PIVutils)
X, Y, Swirl, Cond, Prof = PIVutils.importMatlabPIVdata2D('/Users/Owen/Dropbox/Data/ABL/SBL PIV data/RNV45-RI2.mat',['X','Y','Swirl'],['Cond','Prof'])
NanLocs = np... |
ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/sandbox-3/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'sandbox-3', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: SANDBOX-3
Topic: Aerosol
Sub-Topics: Transport, ... |
diging/methods | 2.1. Co-citation networks/2.1. Co-citation analysis.ipynb | gpl-3.0 | metadata = wos.read('../data/Baldwin/PlantPhysiology',
streaming=True, index_fields=['date', 'abstract'], index_features=['citations'])
len(metadata)
"""
Explanation: 2.1. Co-citation Analysis
In this workbook we will conduct a co-citation analysis using the approach outlined in Chen (2009). If y... |
ecervera/mindstorms-nb | task/light_teacher.ipynb | mit | from functions import connect, light, forward, stop
connect()
"""
Explanation: Sensor de llum
<img src="http://www.nxtprograms.com/line_follower/DCP_2945.JPG" align="right">
Aquest sensor té dos parts:
un diode emissor de llum roja
un transistor detector de llum, que dóna un valor proporcional a la intensitat de llu... |
tensorflow/probability | discussion/examples/cross_gpu_logprob.ipynb | apache-2.0 | %tensorflow_version 2.x
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp
tfb, tfd = tfp.bijectors, tfp.distributions
physical_gpus = tf.config.experimental.list_physical_devices('GPU')
print(physical_gpus)
tf.config.experimental.set_virtual_device_configuration(
physical_gpus[0],
... |
mjones01/NEON-Data-Skills | code/Python/remote-sensing/hyperspectral-data/NEON_AOP_Hyperspectral_Functions_Tiles_py.ipynb | agpl-3.0 | import matplotlib.pyplot as plt
import numpy as np
import h5py, os, osr, copy
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
"""
Explanation: syncID: e046a83d83f2042d8b40dea1b20fd6779
title: "Band Stacking, RGB & False Color Images, and Interactive Widgets in Python - Tiled Data"
description: "Le... |
Bowenislandsong/Distributivecom | Archive/Hyperparameter Optimization.ipynb | gpl-3.0 | import numpy as np
import ray
@ray.remote
def train_cnn_and_compute_accuracy(hyperparameters,
train_images,
train_labels,
validation_images,
validation_labels):
# Construct a deep... |
SylvainCorlay/bqplot | examples/Marks/Pyplot/Bins.ipynb | apache-2.0 | # Create a sample of Gaussian draws
np.random.seed(0)
x_data = np.random.randn(1000)
"""
Explanation: Bins Mark
This Mark is essentially the same as the Hist Mark from a user point of view, but is actually a Bars instance that bins sample data.
The difference with Hist is that the binning is done in the backend, so it... |
cathywu/flow | tutorials/tutorial04_rllab.ipynb | mit | # ring road scenario class
from flow.scenarios.loop import LoopScenario
# input parameter classes to the scenario class
from flow.core.params import NetParams, InitialConfig
# name of the scenario
name = "training_example"
# network-specific parameters
from flow.scenarios.loop import ADDITIONAL_NET_PARAMS
net_params... |
rsterbentz/phys202-2015-work | assignments/assignment06/DisplayEx01.ipynb | mit | from IPython.display import Image, HTML, display
assert True # leave this to grade the import statements
"""
Explanation: Display Exercise 1
Imports
Put any needed imports needed to display rich output the following cell:
End of explanation
"""
Image(url='http://www.elevationnetworks.org/wp-content/uploads/2013/05/... |
dacr26/CompPhys | 00_02_numbers_in_python.ipynb | mit | import sys
sys.float_info
"""
Explanation: Manipulating numbers in Python
Disclaimer: Much of this section has been transcribed from <a href="https://pymotw.com/2/math/">https://pymotw.com/2/math/</a>
Every computer represents numbers using the <a href="https://en.wikipedia.org/wiki/IEEE_floating_point">IEEE floati... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/06_structured/3_keras_dnn.ipynb | apache-2.0 | # change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-east1' #'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
%%bash
if ! gsutil ls | grep -q gs://${BUCKET}/; then
gsutil mb -l ${RE... |
rkastilani/PowerOutagePredictor | PowerOutagePredictor/Linear/Ridge.ipynb | mit | import numpy as np
import pandas as pd
from sklearn import linear_model
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
data = pd.read_csv("../../Data/2014outagesJerry.csv")
data.head()
"""
Explanation: Ridge Regression:
Ridge regression performs ‘L2 regularization‘, i.e. it adds a f... |
radhikapc/foundation-homework | homework05/Homework05_Spotify_radhika.ipynb | mit | #With "Lil Wayne" and "Lil Kim" there are a lot of "Lil" musicians. Do a search and print a list of 50
#that are playable in the USA (or the country of your choice), along with their popularity score.
count =0
for artist in Lil_artists:
count += 1
print(count,".", artist['name'],"has the popularity of", artis... |
michaelaye/iuvs | notebooks/L1A_darks_mean_value_dataframe_analysis.ipynb | isc | %matplotlib inline
plt.rcParams['figure.figsize'] = (10,10)
from matplotlib.pyplot import subplots
"""
Explanation: Inital setup
End of explanation
"""
import pandas as pd
df = pd.read_hdf('/home/klay6683/l1a_dark_stats.h5','df')
"""
Explanation: loading summary file that I previously created by scanning through L1... |
gprakhar/sifar | document_clustering.ipynb | gpl-3.0 | # Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Lars Buitinck
# License: BSD 3 clause
from __future__ import print_function
from sklearn.datasets import fetch_20newsgroups
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feat... |
softEcon/talks | intro_scientific_python/talk.ipynb | mit | import this
"""
Explanation: Python for Scientific Computing in Economics
<font size="3"> ... background material available at <a href="https://github.com/softecon/talks">https://github.com/softecon/talks</a> </font>
Why Python?
<style>
table,td,tr,th {border:none!important}
</style>
<table style="width:90%">
<tbod... |
jinzishuai/learn2deeplearn | deeplearning.ai/C1.NN_DL/week4/Building your Deep Neural Network - Step by Step/Building+your+Deep+Neural+Network+-+Step+by+Step+v5.ipynb | gpl-3.0 | import numpy as np
import h5py
import matplotlib.pyplot as plt
from testCases_v3 import *
from dnn_utils_v2 import sigmoid, sigmoid_backward, relu, relu_backward
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['imag... |
Erhil/PythonNpCourse | materials/week 2/numpy.ipynb | mit | %pylab inline
import this
"""
Explanation: Программирование на Python
Дзен Python
End of explanation
"""
import numpy as np
np.array([1,2,3])
a = np.array([[1,2,3], [4,5,6]])
a = np.array([1,2,3])
b = np.array([4,5,6])
a+b
a*b
a/b
a**b
"""
Explanation: Красивое лучше, чем уродливое.<br>
Явное лучше, чем нея... |
3DGenomes/tadbit | doc/notebooks/tutorial_1-Retrieve_published_HiC_datasets.ipynb | gpl-3.0 | %%bash
mkdir -p FASTQs
fastq-dump SRR5344921 --defline-seq '@$ac.$si' -X 100000000 --split-files --outdir FASTQs/
mv FASTQs/SRR5344921_1.fastq FASTQs/mouse_B_rep1_1.fastq
mv FASTQs/SRR5344921_2.fastq FASTQs/mouse_B_rep1_2.fastq
fastq-dump SRR5344925 --defline-seq '@$ac.$si' -X 100000000 --split-files --outdir FASTQs... |
Xilinx/PYNQ | boards/Pynq-Z1/logictools/notebooks/fsm_generator.ipynb | bsd-3-clause | from pynq.overlays.logictools import LogicToolsOverlay
logictools_olay = LogicToolsOverlay('logictools.bit')
"""
Explanation: Finite State Machine Generator
This notebook will show how to use the Finite State Machine (FSM) Generator to generate a state machine. The FSM we will build is a Gray code counter. The count... |
ernestyalumni/servetheloop | servetheloop.ipynb | mit | import sympy
from sympy import Eq, solve, Symbol, symbols, pi
from sympy import Rational as Rat
from sympy.abc import tau,l,F
"""
Explanation: Start with the Final Design Report - SpaceX Hyperloop Competition II for high level view.
SpaceX Hyperloop Track Specification
End of explanation
"""
eta_1 = Symbol('eta... |
molpopgen/fwdpy | docs/examples/BGS.ipynb | gpl-3.0 | #Use Python 3's print a a function.
#This future-proofs the code in the notebook
from __future__ import print_function
#Import fwdpy. Give it a shorter name
import fwdpy as fp
##Other libs we need
import numpy as np
import pandas as pd
import math
"""
Explanation: Example: background selection
Setting up the simulati... |
pligor/predicting-future-product-prices | 04_time_series_prediction/20_price_history_seq2seq-L2reg.ipynb | agpl-3.0 | from __future__ import division
import tensorflow as tf
from os import path, remove
import numpy as np
import pandas as pd
import csv
from sklearn.model_selection import StratifiedShuffleSplit
from time import time
from matplotlib import pyplot as plt
import seaborn as sns
from mylibs.jupyter_notebook_helper import sho... |
ogaway/Econometrics | MultiCollinearity.ipynb | gpl-3.0 | %matplotlib inline
# -*- coding:utf-8 -*-
from __future__ import print_function
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
# データ読み込み
data = pd.read_csv('example/k0801.csv')
data
# 説明変数設定
X = data[['X', 'Z']]
X ... |
zzsza/Datascience_School | 10. 기초 확률론3 - 확률 분포 모형/02. 베르누이 확률 분포 (파이썬 버전).ipynb | mit | theta = 0.6
rv = sp.stats.bernoulli(theta)
rv
"""
Explanation: 베르누이 확률 분포
베르누이 시도
결과가 성공(Success) 혹은 실패(Fail) 두 가지 중 하나로만 나오는 것을 베르누이 시도(Bernoulli trial)라고 한다. 예를 들어 동전을 한 번 던져 앞면(H:Head)이 나오거나 뒷면(T:Tail)이 나오게 하는 것은 베르누이 시도의 일종이다.
베르누이 시도의 결과를 확률 변수(random variable) $X$ 로 나타낼 때는 일반적으로 성공을 정수 1 ($X=1$), 실패를 정수 0 ($X=0$... |
mne-tools/mne-tools.github.io | 0.23/_downloads/5b9edf9c05aec2b9bb1f128f174ca0f3/40_cluster_1samp_time_freq.ipynb | bsd-3-clause | # Authors: 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 permutation_cluster_1samp_test
from mne.datasets import sample
print(__doc__)
"""
Explanation: Non-par... |
mne-tools/mne-tools.github.io | 0.19/_downloads/c569084177bc9cce4e0419ab10cfd45d/plot_dipole_fit.ipynb | bsd-3-clause | from os import 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... |
rubensfernando/mba-analytics-big-data | Python/2016-08-08/aula7-parte2-web-scraping.ipynb | mit | import requests
req = requests.get("http://pythonscraping.com/pages/page1.html")
print(req.text)
from bs4 import BeautifulSoup
bs = BeautifulSoup(req.text, "html.parser")
print(bs)
type(bs.h1)
bs.h1
bs.h1.string
req = requests.get("http://pythonscraping.com/pages/page1.html")
req.status_code
"""
Explanation:... |
ES-DOC/esdoc-jupyterhub | notebooks/nerc/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', 'nerc', 'sandbox-1', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: NERC
Source ID: SANDBOX-1
Topic: Atmoschem
Sub-Topics: Transport, Emissions ... |
shikhar413/openmc | examples/jupyter/mgxs-part-ii.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn-dark')
import openmoc
import openmc
import openmc.mgxs as mgxs
import openmc.data
from openmc.openmoc_compatible import get_openmoc_geometry
%matplotlib inline
"""
Explanation: Multigroup Cross Section Generation Part II: Advanced Features
Th... |
bkoz37/labutil | samples/lab5_samples/Lab_5_Handout.ipynb | mit | from ase.calculators.eam import EAM
"""
Explanation: NEB using ASE
1. Setting up an EAM calculator.
Suppose we want to calculate the minimum energy path of adatom diffusion on a (100) surface. We first need to choose an energy model, and in ASE, this is done by defining a "calculator". Let's choose our calculator to b... |
oditorium/blog | iPython/Error-Estimation-for-Survey-Data.ipynb | agpl-3.0 | N_people = 500
ratio_female = 0.30
proba = 0.40
"""
Explanation: Error Estimation for Survey Data
the issue we have is the following: we are drawing indendent random numbers from a binary distribution of probability $p$ (think: the probability of a certain person liking the color blue) and we have two groups (think: m... |
karlnapf/shogun | doc/ipython-notebooks/clustering/GMM.ipynb | bsd-3-clause | %pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
# import all Shogun classes
from shogun import *
from matplotlib.patches import Ellipse
# a tool for visualisation
def get_gaussian_ellipse_artist(mean, cov, nstd=1.96, color="red", linewidth=3):
"""
Retur... |
clazaro/elastica | 2DRodFF_1.ipynb | gpl-3.0 | import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
%matplotlib inline
"""
Explanation: © C. Lázaro, Universidad Politécnica de Valencia, 2015
Form finding of planar flexible rods (1)
1 Motivation
Schek, 1973 & Linkwitz - Force denisty method
1. Define net... |
martinjrobins/hobo | examples/toy/model-simple-harmonic-oscillator.ipynb | bsd-3-clause | import pints
import pints.toy
import matplotlib.pyplot as plt
import numpy as np
model = pints.toy.SimpleHarmonicOscillatorModel()
"""
Explanation: Simple Harmonic Oscillator model
This example shows how the Simple Harmonic Oscillator model can be used.
A model for a particle undergoing Newtonian dynamics that experi... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/.ipynb_checkpoints/n09_dyna_10000_states_full_training-checkpoint.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
%matplotlib inline
%pylab inline
pylab.rcPar... |
Featuretools/featuretools | docs/source/getting_started/afe.ipynb | bsd-3-clause | import featuretools as ft
es = ft.demo.load_mock_customer(return_entityset=True)
es
"""
Explanation: Deep Feature Synthesis
Deep Feature Synthesis (DFS) is an automated method for performing feature engineering on relational and temporal data.
Input Data
Deep Feature Synthesis requires structured datasets in order to ... |
caromedellin/Python-notes | exploratory-data-analysis/state example.ipynb | mit | import pandas as pd
import numpy as np
my_data = pd.DataFrame([1,2,3])
"""
Explanation: Example for dimensionnality reduction
End of explanation
"""
def to_binary(value):
return "{0:b}".format(value)
to_binary(5)
unique_values = my_data.thrid.unique()
"""
Explanation: convert integer in to binary string
End... |
gfeiden/Notebook | Projects/senap/real_variables.ipynb | mit | import fileinput as fi
"""
Explanation: Identifying Hard Coded Single Precision Variables
In looking to merge MARCS with DSEP, it is immediately clear that the two codes are incompatible when it comes to passing variables. MARCS is written with single precision declarations for real variables and DSEP is written with ... |
aaronmckinstry706/twitter-crime-prediction | notebooks/New Pipeline.ipynb | gpl-3.0 | import pyspark.sql as sql
ss = sql.SparkSession.builder.appName("TwitterTokenizing")\
.getOrCreate()
"""
Explanation: The New Pipeline
This is a rough draft of our new code. We're using PySpark's DataFrame and Pipeline API (for the most part) to re-implement what we've already done, and t... |
simonsfoundation/CaImAn | demos/notebooks/demo_pipeline_cnmfE.ipynb | gpl-2.0 | try:
get_ipython().magic(u'load_ext autoreload')
get_ipython().magic(u'autoreload 2')
get_ipython().magic(u'matplotlib qt')
except:
pass
import logging
import matplotlib.pyplot as plt
import numpy as np
logging.basicConfig(format=
"%(relativeCreated)12d [%(filename)s:%(funcNa... |
IST256/learn-python | content/lessons/09-Dictionaries/HW-Dictionaries.ipynb | mit | import requests
import json
file='US-Senators.json'
senators = requests.get('https://www.govtrack.us/api/v2/role?current=true&role_type=senator').json()['objects']
with open(file,'w') as f:
f.write(json.dumps(senators))
print(f"Saved: {file}")
"""
Explanation: Homework: US Senator Lookup
The Problem
Let's wri... |
alexandrnikitin/algorithm-sandbox | courses/DAT256x/Module03/03-03-Matrices.ipynb | mit | import numpy as np
A = np.array([[1,2,3],
[4,5,6]])
print (A)
"""
Explanation: Introduction to Matrices
In general terms, a matrix is an array of numbers that are arranged into rows and columns.
Matrices and Matrix Notation
A matrix arranges numbers into rows and columns, like this:
\begin{equation}A = ... |
gunan/tensorflow | tensorflow/lite/micro/examples/hello_world/create_sine_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... |
ml4a/ml4a-guides | examples/fundamentals/math_review_numpy.ipynb | gpl-2.0 | import numpy as np
"""
Explanation: Review of numpy and basic mathematics
written by Gene Kogan
Before learning about what regression and classification are, we will do a review of key mathematical concepts from linear algebra and calculus, as well as an introduction to the numpy package. These fundamentals will be h... |
tensorflow/docs-l10n | site/ko/r1/tutorials/eager/custom_training.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... |
tensorflow/docs-l10n | site/ja/tensorboard/scalars_and_keras.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... |
nntisapeh/intro_programming | notebooks/while_input.ipynb | mit | # Set an initial condition.
game_active = True
# Set up the while loop.
while game_active:
# Run the game.
# At some point, the game ends and game_active will be set to False.
# When that happens, the loop will stop executing.
# Do anything else you want done after the loop runs.
"""
Explanation: W... |
ireapps/cfj-2017 | completed/08. Working with APIs (Part 1).ipynb | mit | from os import environ
slack_hook = environ.get('IRE_CFJ_2017_SLACK_HOOK', None)
"""
Explanation: Let's post a message to Slack
In this session, we're going to use Python to post a message to Slack. I set up a team for us so we can mess around with the Slack API.
We're going to use a simple incoming webhook to accomp... |
necromuralist/necromuralist.github.io | posts/baysian-spam-detector.ipynb | mit | # python standard library
from fractions import Fraction
import sys
# it turns out 'reduce' is no longer a built-in function in python 3
if sys.version_info.major >= 3:
from functools import reduce
spam = 'offer is secret, click secret link, secret sports link'.split(',')
print(len(spam))
ham = 'play sports toda... |
ledrui/cat-vs-dogs-deeplearning | vgg16/lesson1.ipynb | mit | %matplotlib inline
"""
Explanation: Using Convolutional Neural Networks
Welcome to the first week of the first deep learning certificate! We're going to use convolutional neural networks (CNNs) to allow our computer to see - something that is only possible thanks to deep learning.
Introduction to this week's task: 'Do... |
tensorflow/docs-l10n | site/ko/guide/keras/sequential_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... |
mehmetcanbudak/JupyterWorkflow | JupyterWorkflow4.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
plt.style.use("seaborn")
from jupyterworkflow.data import get_fremont_data
data = get_fremont_data()
data.head()
data.resample("W").sum().plot()
data.groupby(data.index.time).mean().plot()
pivoted = data.pivot_table("Total", index=data.index.time, columns=data.ind... |
cranmer/look-elsewhere-2d | examples_from_paper.ipynb | mit | %pylab inline --no-import-all
from lee2d import *
"""
Explanation: Look Elsewhere Effect in 2-d
Kyle Cranmer, Nov 19, 2015
Based on
Estimating the significance of a signal in a multi-dimensional search by Ofer Vitells and Eilam Gross http://arxiv.org/pdf/1105.4355v1.pdf
This is for the special case of a likelihood ... |
thempel/adaptivemd | examples/tutorial/3_example_adaptive.ipynb | lgpl-2.1 | import sys, os
from adaptivemd import (
Project,
Event, FunctionalEvent,
File
)
# We need this to be part of the imports. You can only restore known objects
# Once these are imported you can load these objects.
from adaptivemd.engine.openmm import OpenMMEngine
from adaptivemd.analysis.pyemma import PyEMMA... |
oroszl/szamprob | notebooks/Package04/3D.ipynb | gpl-3.0 | %pylab inline
from mpl_toolkits.mplot3d import * #3D-s ábrák alcsomagja
from ipywidgets import * #interaktivitáshoz szükséges függvények
"""
Explanation: 3D ábrák
A matplotlib csomag elsősorban 2D ábrák gyártására lett kitalálva. Ennek ellenére rendelkezik néhány 3D-s ábrakészítési függvénnyel is. Vizsgáljunk me... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.