repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
tensorflow/docs | site/en/r1/tutorials/images/hub_with_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... |
zzsza/Datascience_School | 19. 문서 전처리/04. 문서 전처리.ipynb | mit | from sklearn.feature_extraction.text import CountVectorizer
corpus = [
'This is the first document.',
'This is the second second document.',
'And the third one.',
'Is this the first document?',
'The last document?',
]
vect = CountVectorizer()
vect.fit(corpus)
vect.vocabulary_
vect.transform(['T... |
UDST/activitysim | activitysim/examples/example_estimation/notebooks/06_cdap.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import larch
import os
os.chdir('test')
"""
Explanation: Estimating Coordinated Daily Activity Patterns
This notebook illustrates how to re-estimate ActivitySim's CDAP model. This process
includes running ActivitySim in estimation mode to read household travel survey files and ... |
LucaCanali/Miscellaneous | Spark_Notes/Spark_SQL_UDF_examples_Mandelbrot/Spark_SQL_UDF_example_notebook_Mandelbrot_Jupyter_Python.ipynb | apache-2.0 | # the function definition
def mandelbrot(cR, cI, maxIterations):
zR = cR
zI = cI
i = 1
# Iterative formula for Mandelbrot set: z => z^2 + c
# Escape point: |z|^2 >= 4. Note: z nd c are complex numbers
while (zR*zR + zI*zI < 4.0 and i < maxIterations):
newzR = zR*zR - zI*zI +cR
... |
jchowk/pyND | pyND/gal/Notebooks/SMHM_Tinker2017_fits.ipynb | gpl-3.0 | ##Set up matplotlib for display in the browser:
%matplotlib inline
import matplotlib as mpl
mpl.rc("savefig", dpi=250)
import matplotlib.pyplot as plt
import numpy as np
from astropy.io import fits, ascii
from astropy.table import Table,Column
"""
Explanation: Tinker+ (2017) SMHM-relations
Here we will fit the Tinke... |
cesarcontre/Simulacion2017 | Modulo3/.ipynb_checkpoints/Clase21_AjusteCurvas-checkpoint.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
P1 = [0, 1]
P2 = [1, 0]
X = np.array([[1, 0], [1, 1]])
y = np.array([1, 0])
b0, b1 = np.linalg.inv(X).dot(y)
b0, b1
x = np.linspace(-0.2, 1.2, 100)
y = b1*x+b0
plt.figure(figsize=(8,6))
plt.plot([P1[0], P2[0]], [P1[1], P2[1]], 'r*', label = 'puntos')
plt.plot(x, y,... |
ES-DOC/esdoc-jupyterhub | notebooks/awi/cmip6/models/awi-cm-1-0-hr/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'awi', 'awi-cm-1-0-hr', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: AWI
Source ID: AWI-CM-1-0-HR
Topic: Ocean
Sub-Topics: Timestepping Framework, Adv... |
4DGenome/Chromosomal-Conformation-Course | Participants/JCarlos/Finding Compartments.ipynb | gpl-3.0 | from pytadbit.parsers.hic_parser import load_hic_data_from_reads
from cPickle import load
%matplotlib inline
reso = 300000
"""
Explanation: Finding compartments and TADs
End of explanation
"""
hic_data1 = load_hic_data_from_reads(
'results/HindIII/03_filtering/valid_reads12_HindIII.tsv',
resolution= reso)
hic_dat... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/gls.ipynb | bsd-3-clause | import numpy as np
import statsmodels.api as sm
"""
Explanation: Generalized Least Squares
End of explanation
"""
data = sm.datasets.longley.load()
data.exog = sm.add_constant(data.exog)
print(data.exog.head())
"""
Explanation: The Longley dataset is a time series dataset:
End of explanation
"""
ols_resid = sm.O... |
Diego-Ibarra/aquamod | tutorials/5_fish_model.ipynb | mit | days = 365 * 2 # Two years
dt = 0.01 # units: days
"""
Explanation: Fish model
Setting up the model backbone
Before we show/code equations... we should start by setting up a the framework... Kinda' a "dummy" empty model...
<span style="background-color:#d2d2d2;color:red;font-size:17px">
Copy-paste into Spyder's E... |
thempel/adaptivemd | examples/tutorial/5_example_advanced_generators.ipynb | lgpl-2.1 | from adaptivemd import (
Project, Task, File, PythonTask
)
project = Project('tutorial')
engine = project.generators['openmm']
modeller = project.generators['pyemma']
pdb_file = project.files['initial_pdb']
"""
Explanation: Custom Generator objects
This example should guide you to build your own simple generator... |
zhuanxuhit/deep-learning | intro-to-rnns/Anna_KaRNNa.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
kaiping/incubator-singa | doc/en/docs/notebook/mlp.ipynb | apache-2.0 | from __future__ import print_function
from builtins import range
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Train a multi-layer perceptron (MLP) model
In this notebook, we are going to use PySINGA to train a MLP model for classifying 2-d points into two categories (i.e., po... |
elingg/tensorflow | tensorflow/examples/udacity/5_word2vec.ipynb | apache-2.0 | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
%matplotlib inline
from __future__ import print_function
import collections
import math
import numpy as np
import os
import random
import tensorflow as tf
import zipfile
from matplotlib import pylab
from six.mov... |
nwhidden/ND101-Deep-Learning | intro-to-rnns/Anna_KaRNNa.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
jokedurnez/RequiredEffectSize | Figure2_CorrSimulation/.ipynb_checkpoints/Correlation_simulation-checkpoint.ipynb | mit | import numpy
import nibabel
import os
import nilearn.plotting
import matplotlib.pyplot as plt
from statsmodels.regression.linear_model import OLS
import nipype.interfaces.fsl as fsl
import scipy.stats
if not 'FSLDIR' in os.environ.keys():
raise Exception('This notebook requires that FSL is installed and the FSLDIR... |
PMEAL/OpenPNM | examples/contrib/loading_statoil_finding_permeability.ipynb | mit | import warnings
import scipy as sp
import numpy as np
import openpnm as op
%config InlineBackend.figure_formats = ['svg']
np.set_printoptions(precision=4)
np.random.seed(10)
%matplotlib inline
"""
Explanation: Part 1: Import Networks from Statoil Files
This example explains how to use the OpenPNM.Utilies.IO.Statoil cl... |
quantopian/research_public | notebooks/lectures/Confidence_Intervals/questions/notebook.ipynb | apache-2.0 | def generate_autocorrelated_data(theta, mu, sigma, N):
X = np.zeros((N, 1))
for t in range(1, N):
X[t] = theta * X[t-1] + np.random.normal(mu, sigma)
return X
def newey_west_SE(data):
ind = range(0, len(data))
ind = sm.add_constant(ind)
model = regression.linear_model.OLS(data, ind).fit... |
bashtage/statsmodels | examples/notebooks/gee_nested_simulation.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import statsmodels.api as sm
"""
Explanation: GEE nested covariance structure simulation study
This notebook is a simulation study that illustrates and evaluates the performance of the GEE nested covariance structure.
A nested covariance structure is based on a nested sequence of... |
daniestevez/jupyter_notebooks | BPSK postprocessing.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import scipy.signal
"""
Explanation: BPSK postprocessing
This notebook shows postprocessing of a 9k6 BPSK signal, with the goal of reverse engineering the coding and protocols.
The signals used here were tranmitted by an unknown satellite at 2018-0... |
google-research/google-research | jax_dft/examples/training_neural_xc_functional.ipynb | apache-2.0 | #@title Default title text
# 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, softwar... |
feststelltaste/software-analytics | prototypes/Decomposing classes like a boss new.ipynb | gpl-3.0 | %load_ext cypher
"""
Explanation: Introduction
In this blog post, I want to show you a graph-based way to split up a class into several independent ones. We take a small example class from Michael Feathers' book "Working effectively with legacy code" and use Neo4j's Awesome Procedures On Cypher (APOC).
Hint: To run th... |
williamdjones/protein_binding | notebooks/Full Dataset Nonlinear Dimensionality Reduction (In Progress).ipynb | mit | import os
import pandas as pd
import time
from tqdm import tqdm
import glob
import numpy as np
from utils.input_pipeline import *
#TODO: Try the variational approach instead.
#TODO: instead of solving for a small dimension representation, then traiing a RF on this...make the dims larger and try a linear model instead?
... |
fredhohman/pymks | notebooks/elasticity_2D.ipynb | mit | %matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
n = 21
from pymks.tools import draw_microstructures
from pymks.datasets import make_delta_microstructures
X_delta = make_delta_microstructures(n_phases=2, size=(n, n))
draw_microstructures(X_delta)
"""
Explan... |
ga7g08/ga7g08.github.io | _notebooks/2015-04-07-Some-favourite-recipes-in-matplotlib.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
def f(x):
return x**3 + 3*x**2 - x + 1
fig, ax = plt.subplots()
x = np.linspace(-3, 3.2, 100)
ax.plot(x, f(x))
plt.show()
"""
Explanation: Plotting mathematics in matplotlib
One of the few complaints against matplotlib from Mathematicians and
Physicists alike ... |
me-surrey/dl-gym | math_linear_algebra.ipynb | apache-2.0 | from __future__ import division, print_function, unicode_literals
"""
Explanation: Math - Linear Algebra
Linear Algebra is the branch of mathematics that studies vector spaces and linear transformations between vector spaces, such as rotating a shape, scaling it up or down, translating it (ie. moving it), etc.
Machine... |
GoogleCloudPlatform/professional-services | examples/cloudml-bank-marketing/bank_marketing_classification_model.ipynb | apache-2.0 | !pip install pandas-profiling
!pip install lime
"""
Explanation: How to build a machine learning marketing model for banking using Google Cloud Platform and Python
This notebook shows you how to build a marketing model for banking using Google Cloud Platform (GCP). Many financial institutions use traditional on-premis... |
MeissnerEffect/hackathon_MIIA_2017 | Bottleneck Features.ipynb | mit | from keras.applications.vgg16 import VGG16
# create the VGG16 model with only the convolutional layers, and using weights from ImageNet
model = VGG16(include_top=False,weights='imagenet')
# show architecture
model.summary()
"""
Explanation: Pothole detector
Bottleneck Features
1. Load the VGG16 model pre-trained on Im... |
jrbourbeau/cr-composition | notebooks/legacy/lightheavy/spectrum-analysis.ipynb | mit | %load_ext watermark
%watermark -u -d -v -p numpy,matplotlib,scipy,pandas,sklearn,mlxtend
"""
Explanation: <a id='top'> </a>
Author: James Bourbeau
End of explanation
"""
%matplotlib inline
from __future__ import division, print_function
from collections import defaultdict
import itertools
import numpy as np
from sci... |
qinwf-nuan/keras-js | notebooks/layers/pooling/AveragePooling3D.ipynb | mit | data_in_shape = (4, 4, 4, 2)
L = AveragePooling3D(pool_size=(2, 2, 2), strides=None, padding='valid', data_format='channels_last')
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(290)
data_in... |
ageron/ml-notebooks | 16_reinforcement_learning.ipynb | apache-2.0 | import numpy as np
import os
import sklearn
import sys
try:
# %tensorflow_version only exists in Colab.
%tensorflow_version 1.x
!apt update && apt install -y libpq-dev libsdl2-dev swig xorg-dev xvfb
!pip install -q -U pyvirtualdisplay gym[atari,box2d]
IS_COLAB = True
except Exception:
IS_COLAB ... |
superbock/parallel2015 | viterbi.ipynb | mit | states = ('Healthy', 'Fever')
observations = ('normal', 'cold', 'dizzy')
start_probability = {'Healthy': 0.6, 'Fever': 0.4}
transition_probability = {
'Healthy' : {'Healthy': 0.7, 'Fever': 0.3},
'Fever' : {'Healthy': 0.4, 'Fever': 0.6}
}
emission_probability = {
'Healthy' : {'normal': 0.5, 'cold': 0... |
sanjanedic/SBARepay | SBARepayML.ipynb | mit | #Import necessary Python packages
#data analysis tools
import numpy as np
import pandas as pd
import datetime
from dateutil.relativedelta import relativedelta
#plotting tools
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import graphviz
#classification
from sklearn.preprocessing import ... |
quantopian/research_public | notebooks/tutorials/2_pipeline_lesson10/notebook.ipynb | apache-2.0 | from quantopian.pipeline import CustomFactor
import numpy
"""
Explanation: Custom Factors
When we first looked at factors, we explored the set of built-in factors. Frequently, a desired computation isn't included as a built-in factor. One of the most powerful features of the Pipeline API is that it allows us to define... |
sassoftware/sas-viya-programming | python/data-mining/CAS Action Documentation.ipynb | apache-2.0 | # Load the swat package and turn off note messages
import swat
swat.options.cas.print_messages = False
# set the connection: host, port, username, password
s = swat.CAS(host, port, username, password)
"""
Explanation: How to Get CAS Action Documentation within Python-swat
There is publically available documentation, ... |
doyljoh/myMachineLearningLibs | notebooks/Notebook_py3_NumerAI_Demo.ipynb | mit | import pandas as pd
import numpy as np
from sklearn import metrics, preprocessing, linear_model
"""
Explanation: Example classifier on Numerai data using a logistic regression classifier.
To get started, install the required packages: pip install pandas, numpy, sklearn
End of explanation
"""
dirRawData = '/home/john... |
martinjrobins/hobo | examples/toy/model-beeler-reuter-ap.ipynb | bsd-3-clause | import pints
import pints.toy
import matplotlib.pyplot as plt
import numpy as np
model = pints.toy.ActionPotentialModel()
"""
Explanation: Beeler-Reuter AP Model
In this example we use the 1977 Beeler-Reuter model of the mammalian ventricular action potential action potential, and try to estimate its maximum conducta... |
csaladenes/csaladenes.github.io | test/plotter.ipynb | mit | import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.font_manager as font_manager
plt.style.use('seaborn-darkgrid')
%matplotlib inline
prop = font_manager.FontProperties('Segoe UI')
from sympy import *
init_printing()
"""
Explanation: Plotting workbook
Plotting workbook to accompany the paper Ca... |
SciTools/cube_browser | doc/write_your_own/components/iplotPicker.ipynb | bsd-3-clause | import ipywidgets
import IPython.display
import iris
import numpy as np
import iris.quickplot as iplt
import matplotlib.pyplot as plt
"""
Explanation: Plot Type Selector
Example showing how to construct a dropdown widget that can be used to select a plot type. Here a dictionary must be used for the plot type options... |
DominikDitoIvosevic/Uni | STRUCE/2018/.ipynb_checkpoints/SU-2018-LAB02-LDM-i-LR-checkpoint.ipynb | mit | # Učitaj osnovne biblioteke...
import sklearn
import mlutils
import matplotlib.pyplot as plt
%pylab inline
"""
Explanation: Sveučilište u Zagrebu
Fakultet elektrotehnike i računarstva
Strojno učenje 2018/2019
http://www.fer.unizg.hr/predmet/su
Laboratorijska vježba 2: Linearni diskriminativni modeli
Verzija: 1.2
Za... |
darkomen/TFG | ipython_notebooks/06_regulador_experto/ensayo2.ipynb | cc0-1.0 | #Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
#Mostramos las versiones usadas de cada librerías
print ("Numpy v{}".format(np.__version__))
print ("Pandas v{}".format(pd.__version__))
print ("Seaborn v{}".format(sns.__version__))
#Abrimos el fichero csv con los datos... |
mathcoding/Programmazione2 | Lab 4 - Matrici, Immagini e Frattali.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
def DrawChessBoard(n=8):
# L'operatore '%' calcola il resto della divisione
ChessBoard = np.matrix([[(i+(j%2))%2 for i in range(n)] for j in range(n)])
print(ChessBoard)
# Crea una figura, disegna l'immagine data dalla matrice, aggiungi la colorbar sul... |
waltervh/BornAgain-tutorial | talks/day_2/python_api_G/pyapi06_lmfit_decaying_wave.ipynb | gpl-3.0 | def decaying_sin(params, x):
amp = params['amp']
phaseshift = params['phase']
freq = params['frequency']
decay = params['decay']
return amp * np.sin(x*freq + phaseshift) * np.exp(-x*x*decay)
"""
Explanation: Defining decaying sin wave
Function accepts dictionary of parameters and array of x-points... |
markriedl/easygen | Easygen.ipynb | mit | !git clone https://github.com/markriedl/easygen.git
!cp easygen/*.js /usr/local/share/jupyter/nbextensions/google.colab/
!cp easygen/images/*.png /usr/local/share/jupyter/nbextensions/google.colab/
"""
Explanation: EasyGen
EasyGen is a visual user interface to help set up simple neural network generation tasks.
There ... |
ES-DOC/esdoc-jupyterhub | notebooks/bnu/cmip6/models/sandbox-2/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bnu', 'sandbox-2', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: BNU
Source ID: SANDBOX-2
Topic: Aerosol
Sub-Topics: Transport, Emissions, Concent... |
jllanfranchi/playground | test_hashses_and_container_classes.ipynb | mit | print hash_obj([0, 1, 2])
bits = 64*2
n_elements = 200
np.log10(2*2**bits/(n_elements*(n_elements-1)))
"""
Explanation: Old version that was developed here (now probably out of sync with official version in PISA...):
python
def hash_obj(obj):
hash_val, = struct.unpack('<q', hashlib.md5(
pickle.dump... |
GoogleCloudPlatform/training-data-analyst | courses/dialogflow-chatbot/notebooks/IntentHealthCheck.ipynb | apache-2.0 | !pip install --quiet --upgrade tensorflow dialogflow scipy tensorflow-hub
import dialogflow_v2 as dialogflow
import tensorflow as tf
import tensorflow_hub as hub
import numpy as np
import pandas as pd
from collections import defaultdict
import os
from google.colab import auth
import pickle
print(tf.__version__)
"""
E... |
AtmaMani/pyChakras | python_crash_course/pandas_cheat_sheet_2.ipynb | mit | import pandas as pd
import numpy as np
# Index Levels
outside = ['G1','G1','G1','G2','G2','G2']
inside = [1,2,3,1,2,3]
hier_index = list(zip(outside,inside)) #create a list of tuples
hier_index
#create a multiindex
hier_index = pd.MultiIndex.from_tuples(hier_index)
hier_index
# Create a dataframe (6,2) with multi le... |
sz2472/foundations-homework | homework 8/homework 8.ipynb | mit | df['Age'].describe()
"""
Explanation: 1. the youngest programmer vs. the oldest programmer?
End of explanation
"""
df.groupby('Gender')['Income'].describe()
"""
Explanation: 2. What are the maximum income for female programmers?
End of explanation
"""
df['Income'].describe()
"""
Explanation: 3. how much does a p... |
brettavedisian/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... |
kgrodzicki/machine-learning-specialization | course-2-regression/notebooks/numpy-tutorial.ipynb | mit | import numpy as np # importing this way allows us to refer to numpy as np
"""
Explanation: Numpy Tutorial
Numpy is a computational library for Python that is optimized for operations on multi-dimensional arrays. In this notebook we will use numpy to work with 1-d arrays (often called vectors) and 2-d arrays (often cal... |
idekerlab/ci-service-template | sample-notebooks/Heat_Diffusion_Example.ipynb | mit | IP = '192.168.99.100'
BASE = 'http://' + IP + '/v1/' # You need to change this to your service server
import requests
import json
def jprint(data):
print(json.dumps(data, indent=4))
# Change this to your Docker container's IP
HEADERS = {'Content-Type': 'application/json'}
# Check API Status
res = requests.get... |
BrahmsPotato/PlayWithPrettyFat | kaggle/titanic/feature_engineering.ipynb | mit | %matplotlib inline
import numpy as np
import pandas as pd
import re as re
train = pd.read_csv('../input/train.csv', header = 0, dtype={'Age': np.float64})
test = pd.read_csv('../input/test.csv' , header = 0, dtype={'Age': np.float64})
full_data = [train, test]
print (train.info())
"""
Explanation: Introduction
This... |
uber/pyro | tutorial/source/epi_intro.ipynb | apache-2.0 | import os
import matplotlib.pyplot as plt
import seaborn as sns
import torch
import pyro
import pyro.distributions as dist
from pyro.contrib.epidemiology import CompartmentalModel, binomial_dist, infection_dist
%matplotlib inline
assert pyro.__version__.startswith('1.7.0')
torch.set_default_dtype(torch.double) # Requ... |
scikit-optimize/scikit-optimize.github.io | dev/notebooks/auto_examples/bayesian-optimization.ipynb | bsd-3-clause | print(__doc__)
import numpy as np
np.random.seed(237)
import matplotlib.pyplot as plt
from skopt.plots import plot_gaussian_process
"""
Explanation: Bayesian optimization with skopt
Gilles Louppe, Manoj Kumar July 2016.
Reformatted by Holger Nahrstaedt 2020
.. currentmodule:: skopt
Problem statement
We are interested... |
mne-tools/mne-tools.github.io | 0.17/_downloads/c0c13bba79416f07138e867fac14148e/plot_configuration.ipynb | bsd-3-clause | import os.path as op
import mne
from mne.datasets.sample import data_path
fname = op.join(data_path(), 'MEG', 'sample', 'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(fname).crop(0, 10)
original_level = mne.get_config('MNE_LOGGING_LEVEL', 'INFO')
"""
Explanation: Configuring MNE python
This tutorial gives a shor... |
laurentmih/fastai_homework3 | notebooks/state farm attempt.ipynb | mit | import os
import zipfile
import shutil
import csv
import bcolz
os.environ["KERAS_BACKEND"] = "theano"
import keras
import numpy as np
from keras.utils.data_utils import get_file
from keras.models import load_model
from keras.layers.normalization import BatchNormalization
from keras.layers import Dense, Dropout, Flatten... |
jdhp-docs/python-notebooks | python_matplotlib_geo_fr.ipynb | mit | %matplotlib inline
"""
Explanation: Cartographie avec Python et Matplotlib
End of explanation
"""
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
lllat = 41.0 # latitude of lower left hand corner of the desired map domain (degrees).
urlat = 52.0 # latitude of upper... |
berlemontkevin/Jupyter_Notebook | Algorithm_Physic/M2_PTSC_17/.ipynb_checkpoints/Homework2-checkpoint.ipynb | apache-2.0 | # Some useful package
import random
import numpy
import math
import time
import operator
import pylab
import matplotlib.pyplot as plt
%matplotlib inline
n_item = 5
#First of all : generate the random distribut... |
GoogleCloudPlatform/python-docs-samples | data-science-onramp/feature-engineering/feature_engineering.ipynb | apache-2.0 | import os
import pandas as pd
from google.cloud import storage
"""
Explanation: Feature engineering with pandas and scikit-learn
This notebook demonstrates how to use Notebooks to perform feature engineering on a dataset using Pandas.
For each dataset, you will load the data into a Pandas DataFrame, clean and transfo... |
anjsimmo/simple-ml-pipeline | jupyter_notebooks/data_bt.ipynb | mit | import pandas as pd
f = pd.read_csv('../data/BLUETH_20150819.filtered.BT', header=None, names=['Site', 'Unix Time', 'Anonymized Bluetooth ID'])
f.head()
"""
Explanation: Wrangle Bluetooth Data
We wrangle the bluetooth data into a workable format, and extract the travel time between the sites we are interested in.
Impo... |
samuelsinayoko/kaggle-housing-prices | data_exploration.ipynb | mit | df.columns
"""
Explanation: Exploration
The dataset is massive, so let's pick a few columns to get started. We look for features that explain the the sell prices, so these features should be correlated. One way to find them is to plot the correlation matrix and look for correlated variables.
End of explanation
"""
d... |
rpreen/xcsf | python/notebooks/example_regression.ipynb | gpl-3.0 | from __future__ import annotations
from typing import Final
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.datasets import fetch_openml
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_s... |
colour-science/colour-science.org | posts/about-rendering-engines-colourspace-agnosticism.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pylab
import colour
from colour.utilities.verbose import message_box
name, data, illuminant = colour.COLOURCHECKERS['ColorChecker 2005']
sRGB_w = colour.sRGB_COLOURSPACE.whitepoint
sRGB_XYZ_to_RGB = colour.sRGB_COLOURSPACE.XYZ_to_RGB_matrix
sRGB_RGB_to_XYZ = colour.sRGB_... |
tensorflow/docs-l10n | site/pt-br/tutorials/images/transfer_learning.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... |
hpleva/pyemto | example_projects/fcc_elastic_constants/CoCrFeMnNi_paramagnetic_fcc.ipynb | mit | from IPython.display import display, HTML
HTML("<style>.container { width:98% !important; }</style>")
%pylab inline
import numpy as np
from pyemto.EOS import EOS
from pyemto.emto_parser import EMTOPARSER
"""
Explanation: Make the cell width as wide as possible
End of explanation
"""
lat = 'fcc'
ry2J = 2.1798741e-1... |
quantopian/research_public | notebooks/data/eventvestor.earnings_guidance/notebook.ipynb | apache-2.0 | # import the dataset
from quantopian.interactive.data.eventvestor import earnings_guidance
# or if you want to import the free dataset, use:
# from quantopian.interactive.data.eventvestor import earnings_guidance_free
# import data operations
from odo import odo
# import other libraries we will use
import pandas as pd... |
dualphase90/Learning-Neural-Networks | TF more basic tutuorials.ipynb | mit | import tensorflow as tf
x = tf.constant(35, name='x')
y = tf.Variable(x + 5, name='y')
print(y)
x = tf.constant(35, name='x')
y = tf.Variable(x + 5, name='y')
model = tf.initialize_all_variables()
with tf.Session() as session:
session.run(model)
print(session.run(y))
"""
Explanation: What does Tensor... |
Evfro/polara | examples/Hyper-parameter tuning and cross-validation tutorial.ipynb | mit | import urllib
from polara import (get_movielens_data, # returns data in the pandas dataframe format
RecommenderData) # provides common interface to access data
url = 'http://files.grouplens.org/datasets/movielens/ml-10m.zip'
tmp_file, _ = urllib.request.urlretrieve(url) # this may take some time d... |
neXyon/suvicoco | notebooks/Adaptive_Controllers.ipynb | agpl-3.0 | B_len = np.random.randint(5, 10)
A_len = np.random.randint(5, 10)
B = np.random.randn(B_len)
A = np.random.randn(A_len)
def stable_poly(length):
# all roots inside unit circle and real valued polynomial
roots = np.random.rand((length - 1) // 2) * np.exp(np.random.rand((length - 1) // 2) * 2j * np.pi)
... |
ecervera/mindstorms-nb | task/navigation.ipynb | mit | from functions import connect, touch, forward, backward, left, right, stop, disconnect, next_notebook
from time import sleep
connect()
"""
Explanation: Exercici de navegació
<span title="Roomba navigating around furniture"><img src="img/roomba.jpg" align="right" width=200></span>
Un robot mòbil com el Roomba de la ima... |
VectorBlox/PYNQ | docs/source/8_base_overlay_iop.ipynb | bsd-3-clause | from pynq import Overlay
from pynq.iop import Pmod_OLED
from pynq.iop import PMODB
ol = Overlay("base.bit")
ol.download()
oled = Pmod_OLED(PMODB)
"""
Explanation: Using Peripherals with the Base overlay
Base overlay
The PYNQ-Z1 has 2 Pmod connectors. PMODA and PMODB as indicated below are connected to the FPGA fabric... |
vrbala/vrbala.github.io | machine-learning-nanodegree/student_intervention/student_intervention/new/stu_intv_solution/student_intervention.ipynb | mit | # Import libraries
import numpy as np
import pandas as pd
# Read student data
student_data = pd.read_csv("student-data.csv")
print "Student data read successfully!"
# Note: The last column 'passed' is the target/label, all other are feature columns
"""
Explanation: Project 2: Supervised Learning
Building a Student In... |
ankurankan/pgmpy | examples/Monty Hall Problem.ipynb | mit | from IPython.display import Image
Image("images/monty.png")
"""
Explanation: Monty Hall Problem
Problem Description:
The Monty Hall Problem is a very famous problem in Probability Theory. The question goes like:
Suppose you're on a game show, and you're given the choice of three doors: Behind one door is a car; behin... |
sameera2004/scikit-xray-examples | demos/Generate_ROI_labeled_arrays.ipynb | bsd-3-clause | import skbeam.core.roi as roi
import skbeam.core.correlation as corr
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib.ticker import MaxNLocator
from matplotlib.colors import LogNorm
import xray_vision.mpl_plotting as mpl_plot
"""
Explanation: Generate Region of Interests (ROI) l... |
Luke035/dlnd-lessons | transfer-learning/Transfer_Learning.ipynb | mit | from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_s... |
vkuznet/rep | howto/03-howto-gridsearch.ipynb | apache-2.0 | %pylab inline
"""
Explanation: Grid Search in REP
This notebook demonstrates tools to optimize classification model provided by Reproducible experiment platform (REP) package:
grid search for the best classifier hyperparameters
different optimization algorithms
different scoring models (optimization of arbirtar... |
google/jax | docs/notebooks/quickstart.ipynb | apache-2.0 | import jax.numpy as jnp
from jax import grad, jit, vmap
from jax import random
"""
Explanation: JAX Quickstart
JAX is NumPy on the CPU, GPU, and TPU, with great automatic differentiation for high-performance machine learning research.
With its updated version of Autograd, JAX
can automatically differentiate native Py... |
AllenDowney/ModSimPy | soln/chap23soln.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 *
"""
Explanation: Modeling and Simulati... |
texib/deeplearning_homework | mnist-logistic_regression.ipynb | mit | x = tf.placeholder(tf.float32,shape=[None,28*28])
y = tf.placeholder(tf.float32,shape=[None,10])
# Create model
# Set model weights
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
xw = tf.matmul(x, W)
r = xw + b
a = tf.nn.softmax(r)
"""
Explanation: 定義 Input 及 Output 暫存變數
Input 為 28x28 的點陣圖素
Ou... |
folivetti/PIPYTHON | ErrosExcecoes.ipynb | mit | print(4/0) # division by zero
float("quatro ponto seis") # ValueError: could not convert string to float
"""
Explanation: Erros e Exceções
Em diversas situações, podem ocorrer erros ou exceções no Python. Veja alguns exemplos a seguir:
End of explanation
"""
x = float(input("Digite um número:"))
print(1/x)
"""
Exp... |
tensorflow/docs-l10n | site/ko/io/tutorials/audio.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... |
jaalonso/AFV | Programacion_imperativa_en_Python.ipynb | gpl-3.0 | 2 * 5 + 6 - (100 + 3)
8/2, 8/3
7 // 2, 7 % 2
2 ** 10
"""
Explanation: <a href="https://colab.research.google.com/github/jaalonso/AFV/blob/master/Programacion_imperativa_en_Python.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Programación impera... |
renecnielsen/twitter-diy | ipynb/03b Descriptive Statistics - TSV.ipynb | mit | from IPython.core.display import HTML
styles = open("../css/custom.css", "r").read()
HTML(styles)
import pandas as pd
import numpy as np
"""
Explanation: Descriptive Statistics
Descriptive Statistics and Graphs
Number of Tweets (Total)
Number of Tweets (Time Series)
Gender Distribution
Language Distribution
Follower... |
KristianHolsheimer/tensorflow_training | control_flow_in_tensorflow.ipynb | gpl-3.0 | import tensorflow as tf
import numpy as np
import pandas as pd
%matplotlib inline
"""
Explanation: Control flow in Tensorflow
In this tutorial, we're going to use control flow operations, i.e. if statements and while loops. So before we tackle the bigger problem, let's learn how these control flow operations are imple... |
philmui/datascience2016fall | lecture02.ingestion/lecture02.web.scaping.ipynb | mit | import requests
from lxml import html
"""
Explanation: Import needed libraries
End of explanation
"""
response = requests.get('http://news.ycombinator.com/')
response
response.content
"""
Explanation: We used the library "request" last time in getting Twitter data (REST-ful). We are introducing the new "lxml" lib... |
hyzhak/nltk-experiments | main.ipynb | mit | import nltk
from nltk import tokenize
# TODO: we don't relly want to download packages each time when we lauch this script
# so it'll better to check somehow whether we have packages or not - or Download on demand
# nltk.download()
"""
Explanation: NLTK experiments
based on NLTK with Python 3 for Natural Language Pr... |
dkirkby/bossdata | examples/nb/MakingSkyPlots.ipynb | mit | %pylab inline
from mpl_toolkits.basemap import Basemap
from matplotlib.collections import PolyCollection
import astropy.units as u
from astropy.coordinates import SkyCoord
import healpy as hp
print(hp.version.__version__)
import bossdata.meta
print(bossdata.__version__)
"""
Explanation: Examples of Making Sky Plot... |
Eomys/MoSQITo | tutorials/tuto_signal_basic_operations.ipynb | apache-2.0 | # Add MOSQITO to the Python path
import sys
sys.path.append('..')
# To get inline plots (specific to Jupyter notebook)
%matplotlib notebook
# Import numpy
import numpy as np
# Import plot function
import matplotlib.pyplot as plt
# Import load function
from mosqito.utils import load
# Import MOSQITO color sheme [Opti... |
random-forests/tensorflow-workshop | archive/extras/time-series-estimator.ipynb | apache-2.0 | # tensorflow
import tensorflow as tf
# rnn common functions
from tensorflow.contrib.learn.python.learn.estimators import rnn_common
# visualization
import seaborn as sns
import matplotlib.pyplot as plt
# helpers
import numpy as np
import pandas as pd
import csv
# enable tensorflow logs
tf.logging.set_verbosity(tf.l... |
mne-tools/mne-tools.github.io | 0.20/_downloads/89050e30106bf5c25f0fafb6d50732da/plot_phantom_4DBTi.ipynb | bsd-3-clause | # Authors: Alex Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
from mne.datasets import phantom_4dbti
import mne
"""
Explanation: 4D Neuroimaging/BTi phantom dataset tutorial
Here we read 4DBTi epochs data obtained with a spherical phantom
using four differe... |
tensorflow/fairness-indicators | g3doc/tutorials/Fairness_Indicators_on_TF_Hub_Text_Embeddings.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
jss367/assemble | exploratory_notebooks/tweets_nlp_experiment.ipynb | mit | # some nlp tools
import spacy
import gensim
import pyLDAvis.gensim as gensimvis
import pyLDAvis
# tools to get data from s3
import boto3
from io import StringIO
import json
import requests
# would probably need these
import numpy as np
import pandas as pd
import itertools
nlp = spacy.load('en')
"""
Explanation: <h2... |
GoogleCloudPlatform/bigquery-notebooks | notebooks/community/analytics-componetized-patterns/retail/recommendation-system/bqml-scann/05_deploy_lookup_and_scann_caip.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
"""
Explanation: Part 5: Deploy the solution to AI Platform Prediction
This notebook is the fifth of five notebooks that guide you through running the Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN solution.
Use this notebook to complete... |
HackyHour/Wuerzburg | 56_nalu/nalu_problem.ipynb | cc0-1.0 | import torch
from torch import Tensor, nn, optim
import torch.nn.functional as F
import numpy as np
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
"""
Explanation: Learning the identity with neural networks
We try to reproduce the problem of learning neural networks that learn the identity function an... |
google/eng-edu | ml/pc/exercises/image_classification_part3.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... |
icrtiou/coursera-ML | ex7-kmeans and PCA/3- kmeans for image compression.ipynb | mit | from skimage import io
# cast to float, you need to do this otherwise the color would be weird after clustring
pic = io.imread('data/bird_small.png') / 255.
io.imshow(pic)
pic.shape
# serialize data
data = pic.reshape(128*128, 3)
"""
Explanation: load image as ndarray
http://scikit-image.org/
End of explanation
"""... |
lamastex/scalable-data-science | _360-in-525/2018/02/SimonLindgren/MeTooInJupyterIpythonNBAction/Simon_MetooStep4.ipynb | unlicense | from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
import pandas as pd
pd.set_option('display.max_colwidth', -1)
"""
Explanation: Simon #metoo step 4
For sentiment analysis, we will use the VADER library.
End of explanation
"""
df = pd.DataFrame.from_csv("me... |
molpopgen/fwdpy | docs/pages/regions.ipynb | gpl-3.0 | recRegions = [fwdpy.Region(1,1e5,0,False),fwdpy.Region(1e5,2e5,1e-8,False),fwdpy.Region(2e5,3e5,1e-7,False)]
for i in recRegions:
print (i)
neutralRegions = [fwdpy.Region(0,1,1)]
"""
Explanation: For this hypothetical example, the region lengths are all identical, and thus an equivalent specification would be thi... |
mohsinhaider/pythonbootcampacm | Control Flow/The 'if', 'elif', and 'else' Statements.ipynb | mit | you = "ready"
if you == "ready":
print("Vamanos!")
else:
print("What's wrong?")
"""
Explanation: If, Elif, Else Statements
Programming starts and ends at control flow. Decisions need to be made to carry out the functions you write. We make decisions use the if, elif, and else statement. Like any other program... |
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