repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
kevinjliang/Duke-Tsinghua-MLSS-2017 | 04A_MLP_Optimizer_Sandbox_Assignment.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# Import data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# Helper functions for creating weight variables
def weight_variable(shape):
"""wei... |
openearth/notebooks | netcdf_fortran.ipynb | gpl-3.0 | %%file test.f90
program example
use netcdf
integer, parameter :: n_time = 366
integer, parameter :: n_lon = 720
integer, parameter :: n_lat = 360
real(kind=4), dimension(n_lon, n_lat, n_time) :: outflow
character(len=*), parameter :: unit = 'm3 s-1'
! this is an example dataset from Dai Yamazaki
chara... |
johntanz/ROP | .ipynb_checkpoints/Masimo160127-checkpoint.ipynb | gpl-2.0 | #the usual beginning
import pandas as pd
import numpy as np
from pandas import Series, DataFrame
from datetime import datetime, timedelta
from pandas import concat
#define any string with 'C' as NaN
def readD(val):
if 'C' in val:
return np.nan
return val
"""
Explanation: Masimo Analysis
For Pulse Ox. ... |
nntisapeh/intro_programming | notebooks/if_statements.ipynb | mit | # A list of desserts I like.
desserts = ['ice cream', 'chocolate', 'apple crisp', 'cookies']
favorite_dessert = 'apple crisp'
# Print the desserts out, but let everyone know my favorite dessert.
for dessert in desserts:
if dessert == favorite_dessert:
# This dessert is my favorite, let's let everyone know!... |
Purg/SMQTK | bin/memex/hackathon_2016_07/cp1/data_retreival/notebooks/CP1 Data Curation.ipynb | bsd-3-clause | import json
import os
from collections import defaultdict
DATA_FILE = ''
!md5sum $DATA_FILE
"""
Explanation: The goal of this notebook is to retrieve the relevant images from a set of ads with assigned clusters.
Input
The input is specified by DATA_FILE, which is a JSON lines file containing CDR ad documents that e... |
smorton2/think-stats | code/chap07soln.ipynb | gpl-3.0 | from __future__ import print_function, division
%matplotlib inline
import numpy as np
import brfss
import thinkstats2
import thinkplot
"""
Explanation: Examples and Exercises from Think Stats, 2nd Edition
http://thinkstats2.com
Copyright 2016 Allen B. Downey
MIT License: https://opensource.org/licenses/MIT
End of ... |
probml/pyprobml | notebooks/book1/01/pandas_intro.ipynb | mit | # Standard Python libraries
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import time
import numpy as np
import glob
import matplotlib.pyplot as plt
import PIL
import imageio
from IPython.display import display, HTML
import sklearn
import seaborn as sns
sns.set(style=... |
kubeflow/kfserving-lts | docs/samples/pipelines/kfs-pipeline.ipynb | apache-2.0 | !pip3 install kfp --upgrade
import kfp.compiler as compiler
import kfp.dsl as dsl
import kfp
from kfp import components
# Create kfp client
# Note: Add the KubeFlow Pipeline endpoint below if the client is not running on the same cluster.
# Example: kfp.Client('http://192.168.1.27:31380/pipeline')
client = kfp.Client... |
Milad7m/motion | 07_04.ipynb | mit | import numpy as np
import pandas as pd
from sklearn.svm import SVR
from sklearn.linear_model import Lasso
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split
"""
Explanation: Main imports
End of explanation
"""
# input ... |
keras-team/keras-io | examples/timeseries/ipynb/timeseries_weather_forecasting.ipynb | apache-2.0 | import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
"""
Explanation: Timeseries forecasting for weather prediction
Authors: Prabhanshu Attri, Yashika Sharma, Kristi Takach, Falak Shah<br>
Date created: 2020/06/23<br>
Last modified: 2020/07/20<br>
Description: This n... |
bmeaut/python_nlp_2017_fall | course_material/05_Decorators_Packaging/05_Decorators_packaging.ipynb | mit | def greeter(func):
print("Hello")
func()
def say_something():
print("Let's learn some Python.")
greeter(say_something)
# greeter(12)
"""
Explanation: Introduction to Python and Natural Language Technologies
Lecture 5
Decorators and packaging
March 7, 2018
Let's create a greeter function
takes an... |
gully/PyKE | docs/source/tutorials/ipython_notebooks/psfphotometry/c9-prf-fitting.ipynb | mit | import pyke
pyke.__version__
import oktopus
oktopus.__version__
"""
Explanation: Fitting PRFs in K2 TPFs from Campaign 9.1
In this simple tutorial we will show how to perform PRF photometry in a K2 target pixel file using PyKE and oktopus.
This notebook was created with the following versions of PyKE and oktopus:
E... |
jqug/microscopy-object-detection | CNN training & evaluation - plasmodium (phone).ipynb | mit | 29416./261345
N_samples_to_display = 10
pos_indices = np.where(train_y)[0]
pos_indices = pos_indices[np.random.permutation(len(pos_indices))]
for i in range(N_samples_to_display):
plt.subplot(2,N_samples_to_display,i+1)
example_pos = train_X[pos_indices[i],:,:,:]
example_pos = np.swapaxes(example_pos,0,2)
... |
junhwanjang/DataSchool | Lecture/05. 기초 선형 대수 1 - 행렬의 정의와 연산/6) 연립방정식과 역행렬.ipynb | mit | A = np.array([[1, 3, -2], [3, 5, 6], [2, 4, 3]])
A
b = np.array([[5], [7], [8]])
b
Ainv = np.linalg.inv(A)
Ainv
x = np.dot(Ainv, b)
x
np.dot(A, x) - b
x, resid, rank, s = np.linalg.lstsq(A, b)
x
"""
Explanation: 연립방정식과 역행렬
다음과 같이 $x_1, x_2, \cdots, x_n$ 이라는 $n$ 개의 미지수를 가지는 방정식을 연립 방정식(system of equations)이라고 한다.
... |
hannorein/reboundx | ipython_examples/Radiation_Forces_Circumplanetary_Dust.ipynb | gpl-3.0 | import rebound
import reboundx
import numpy as np
sim = rebound.Simulation()
sim.G = 6.674e-11 # SI units
sim.dt = 1.e4 # Initial timestep in sec.
sim.N_active = 2 # Make it so dust particles don't interact with one another gravitationally
sim.add(m=1.99e30, hash="Sun") # add Sun with mass in kg
sim.add(m=5.68e26, a=1.... |
gdhungana/desispec | doc/nb/Bootstrap_tests.ipynb | bsd-3-clause | # import
"""
Explanation: Tests for the Bootstrap code
End of explanation
"""
def pix_sub(infil, outfil, rows=(80,310)):
hdu = fits.open(infil)
# Trim
img = hdu[0].data
sub_img = img[:,rows[0]:rows[1]]
# New
newhdu = fits.PrimaryHDU(sub_img)
# Header
for key in ['CAMERA','VSPECTER','R... |
planetlabs/notebooks | jupyter-notebooks/temporal-analysis/crop-temporal.ipynb | apache-2.0 | import datetime
import json
import os
import shutil
import subprocess
import geojson
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from planet import api
from planet.api import filters, downloader
import rasterio
from shapely.geometry import shape
"""
Explanation: Crop Temporal Analysis
Throu... |
pysal/pysal | notebooks/explore/pointpats/window.ipynb | bsd-3-clause | import pysal.lib as ps
import numpy as np
from pysal.explore.pointpats import PointPattern
f = ps.examples.get_path('vautm17n_points.shp')
fo = ps.io.open(f)
pp_va = PointPattern(np.asarray([pnt for pnt in fo]))
fo.close()
pp_va.summary()
"""
Explanation: Point Pattern Windows
Author: Serge Rey sjs... |
niallrobinson/jade-hack | Dask Intro.ipynb | gpl-3.0 | !pip install castra graphviz # missing dependency for
!apt-get install -y graphviz # logic graph viz
# just so we do plots in the notebook
%matplotlib inline
import dask # for parallel computing
from distributed import Executor, progress # for distributed parallel computing
"""
Explanation: Dask
Dask is a Python lib... |
pyemma/deeplearning | assignment2/ConvolutionalNetworks.ipynb | gpl-3.0 | # As usual, a bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.cnn import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient
from cs231n.layers import *
from cs231n.fast_layers import *
from cs... |
landlab/landlab | notebooks/tutorials/overland_flow/how_to_d4_pitfill_a_dem.ipynb | mit | from landlab import imshow_grid
from landlab.components import FlowAccumulator
from landlab.io import read_esri_ascii
"""
Explanation: <a href="http://landlab.github.io"><img style="float: left" src="../../landlab_header.png"></a>
How to do "D4" pit-filling on a digital elevation model (DEM)
(Greg Tucker, July 2021)
D... |
martinggww/lucasenlights | MachineLearning/DataScience-Python3/MultivariateRegression.ipynb | cc0-1.0 | import pandas as pd
df = pd.read_excel('http://cdn.sundog-soft.com/Udemy/DataScience/cars.xls')
df.head()
"""
Explanation: Multivariate Regression
Let's grab a small little data set of Blue Book car values:
End of explanation
"""
import statsmodels.api as sm
df['Model_ord'] = pd.Categorical(df.Model).codes
X = d... |
metpy/MetPy | v0.11/_downloads/6535033cff935ab2c434cdad6eb5b4f7/Wind_SLP_Interpolation.ipynb | bsd-3-clause | import cartopy.crs as ccrs
import cartopy.feature as cfeature
from matplotlib.colors import BoundaryNorm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from metpy.calc import wind_components
from metpy.cbook import get_test_data
from metpy.interpolate import interpolate_to_grid, remove_nan_obse... |
Bismarrck/deep-learning | tv-script-generation/dlnd_tv_script_generation.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... |
DistrictDataLabs/yellowbrick | examples/rebeccabilbro/pipelines.ipynb | apache-2.0 | %matplotlib inline
import os
import sys
# Modify the path
sys.path.append("/Users/rebeccabilbro/Desktop/waves/stuff/yellowbrick")
import requests
import numpy as np
import pandas as pd
import yellowbrick as yb
import matplotlib.pyplot as plt
"""
Explanation: Chained Visualizations with Yellowbrick Pipelines
In ... |
zhaojijet/UdacityDeepLearningProject | examples/Skip-Grams-Solution.ipynb | apache-2.0 | import time
import numpy as np
import tensorflow as tf
import utils
"""
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language p... |
anhaidgroup/py_entitymatching | notebooks/guides/end_to_end_em_guides/Basic EM Workflow Restaurants - 1.ipynb | bsd-3-clause | import sys
sys.path.append('/Users/pradap/Documents/Research/Python-Package/anhaid/py_entitymatching/')
import py_entitymatching as em
import pandas as pd
import os
# Display the versions
print('python version: ' + sys.version )
print('pandas version: ' + pd.__version__ )
print('magellan version: ' + em.__version__ )... |
mohsinhaider/pythonbootcampacm | Objects and Data Structures/Dictionaries.ipynb | mit | # Initializing a Dictionary
my_dictionary = {"Mike":1, "John":5}
"""
Explanation: Dictionaries
Python has 3 primary types of data: sequences, sets, and mappings. A dictionary is a mapping, or, in other words, a container for multiple mappings of key-value pairs. In specific, mappings are collections of objects organiz... |
matheusportela/indeed-ml-codesprint | indeed.ipynb | mit | import numpy as np
import sklearn
"""
Explanation: Indeed Machine Learning CodeSprint
Load the important packages:
End of explanation
"""
import csv
def load_train_data(filename):
X = []
y = []
with open(filename) as fd:
reader = csv.reader(fd, delimiter='\t')
# ignore header row
... |
giacomov/3ML | docs/examples/joint_BAT_gbm_demo.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
from jupyterthemes import jtplot
jtplot.style(context="talk", fscale=1, ticks=True, grid=False)
plt.style.use("mike")
from threeML import *
from threeML.io.package_data import get_path_of_data_file
import os
import warnings
warnings.simplefilter("ignore")
"""
Ex... |
usantamaria/ipynb_para_docencia | 10_libreria_pycuda/pycuda.ipynb | mit | """
IPython Notebook v4.0 para python 3.0
Librerías adicionales: numpy, scipy, matplotlib. (EDITAR EN FUNCION DEL NOTEBOOK!!!)
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 dinámi... |
tensorflow/docs-l10n | site/ja/tutorials/structured_data/imbalanced_data.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... |
Kkari/bsc_thesis | Diploma_munka_notebook_0.ipynb | apache-2.0 | def accuracy(predictions, labels):
return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))
/ predictions.shape[0])
# Reformat the dataset for the convolutional networks
def reformat(dataset):
dataset = dataset.reshape((-1, image_size, image_size, num_channels)).astype(np.float32)
... |
ueapy/ueapy.github.io | content/notebooks/2017-03-24-climate-model-output.ipynb | mit | URL = 'https://raw.githubusercontent.com/ueapy/ueapy.github.io/src/content/data/run1_U_60N_10hPa.dat'
"""
Explanation: Today one of the group members asked for help with reading climate model output and preparing it for data analysis.
This notebook shows a couple of ways of doing that with the help of numpy and iris P... |
hvanwyk/quadmesh | experiments/multiscale_gmrf/optimal_upscaling_01.ipynb | mit | # Add src folder to path
import os
import sys
sys.path.insert(0,'../../src/')
"""
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Introduction" data-toc-modified-id="Introduction-1"><span class="toc-item-num">1 </span>Introducti... |
jcbozonier/research | notebooks/PuLP Shopping with a Data Scientist.ipynb | mit | model_a = p.LpProblem("Albon Shopping Problem", p.LpMinimize)
"""
Explanation: Shopping with a Data Scientist
End of explanation
"""
lightning_1 = p.LpVariable('lightning_1', lowBound=0, cat='Integer')
lightning_3 = p.LpVariable('lightning_3', lowBound=0, cat='Integer')
lightning_6 = p.LpVariable('lightning_6', lowB... |
luofan18/deep-learning | batch-norm/Batch_Normalization_Lesson.ipynb | mit | # Import necessary packages
import tensorflow as tf
import tqdm
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Import MNIST data so we have something for our experiments
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
"... |
irockafe/revo_healthcare | notebooks/Effects_of_retention_time_on_classification/retention_time_regions_and_classifiiability.ipynb | mit | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.colors as colors
%matplotlib inline
"""
Explanation: <h2>Goal:</h2>
Write functions to subdivide an m/z : rt space into rt bins. See how this affects classification performance
End of explanation
"""
# Get the data
### Subdiv... |
ZoranPandovski/al-go-rithms | image_processing/Augmentation/augmentor.ipynb | cc0-1.0 | !pip install Augmentor -q
%matplotlib inline
"""
Explanation: Image Augmentation using Augmentor
Augmentor is an image augmentation library in Python for machine learning. It aims to be a standalone library that is platform and framework independent, which is more convenient, allows for finer grained control over aug... |
ivannz/study_notes | year_15_16/machine_learning_course/ensemble_practicum/ensemble_methods_scikit.ipynb | mit | import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.utils import check_random_state
"""
Explanation: A half-baked tutorial on ensemble methods
<center>by Ivan Nazarov<center/>
This tutorial covers both introductiory level theory underpinning
each ensemble method, as ... |
skdaccess/skdaccess | skdaccess/examples/Demo_TESS_Data_Alerts.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.dpi'] = 150
"""
Explanation: The MIT License (MIT)<br>
Copyright (c) 2018 Massachusetts Institute of Technology<br>
Authors: Cody Rude<br>
This software has been created in projects supported by the US National<br>
Science Foundation and NASA (PI... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/gapic/automl/showcase_automl_tabular_binary_classification_batch.ipynb | apache-2.0 | import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex client library: AutoML tabular binary classification model for batch prediction
<tab... |
daviddesancho/PREFUR | examples/free_energy_model_local.ipynb | gpl-3.0 | from prefur import thermo
"""
Explanation: Splitting stabilization energy
We start by importing the thermo module from the prefur package.
End of explanation
"""
fig, ax = plt.subplots(2,2, figsize=(7,5), sharex=True)
ax = ax.flatten()
FES = thermo.FES(40)
FES.gen_enthalpy_global(DHloc=1.31, DHnonloc=5.5)
FES.gen_f... |
JamesRunnalls/HealthEconomics_Analysis | Health_Economics.ipynb | gpl-3.0 | %matplotlib notebook
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import difflib
import re
#import seaborn as sns
"""
Explanation: Health and Economic Analysis
End of explanation
"""
key = pd.read_excel('key.xlsx',sheetname='UK', usecols=['NUTS3_13','LAU1_NAT_CODE_NEW'])
key = key.drop_dup... |
mathcoding/Programmazione2 | Appunti vari.ipynb | mit | 0.1+0.1+0.1-0.3
"""
Explanation: Precisione dei numeri floats
A seguito di un paio di domande fatte a lezione, vediamo la precisione dei numeri "reali" in Python.
I float in python corrispondono ai double in C e quindi sono numeri in doppio precisione, e occupano in memoria 64 bits. Questo comporta un errore di preci... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/production_ml/labs/samples/core/dataflow/dataflow.ipynb | apache-2.0 | project = 'Input your PROJECT ID'
region = 'Input GCP region' # For example, 'us-central1'
output = 'Input your GCS bucket name' # No ending slash
"""
Explanation: GCP Dataflow Component Sample
A Kubeflow Pipeline component that prepares data by submitting an Apache Beam job (authored in Python) to Cloud Dataflow for... |
sz-workshop-2017/virtual-machine | notebooks/6.2 - Loading a pre-trained model.ipynb | apache-2.0 | import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.callbacks import ModelCheckpoint
from keras.utils import np_utils
import sys
import re
import pickle
"""
Explanation: 6.2 - Using a pre-trained model with Ker... |
sriharshams/mlnd | boston_housing/boston_housing.ipynb | apache-2.0 | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from sklearn.cross_validation import ShuffleSplit
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the Boston housing dataset
data = pd.read_csv('hou... |
chseifert/tutorials | visualizations/Anscombe-Data-Set.ipynb | apache-2.0 | import matplotlib.pyplot as plt
import numpy as np
import pandas
import statistics
from statistics import variance
from pylab import *
from collections import OrderedDict
"""
Explanation: THE ANSCOMBE QUARTET
Authors
Ndèye Gagnessiry Ndiaye and Christin Seifert
License
This work is licensed under the Creative Common... |
quantopian/research_public | notebooks/tutorials/4_futures_getting_started_lesson5/notebook.ipynb | apache-2.0 | from quantopian.research.experimental import continuous_future, history
cl_future = continuous_future('CL')
xb_future = continuous_future('XB')
cl_price = history(
cl_future,
fields='price',
frequency='daily',
start='2014-01-01',
end='2015-01-01'
)
xb_price = history(
xb_future,
fields='... |
mihaic/brainiak | examples/funcalign/FastSRM_encoding_experiment.ipynb | apache-2.0 | import wget
from time import time
from glob import glob
from os.path import join
import nibabel
from nilearn.image import new_img_like
from nilearn.input_data import NiftiMasker, MultiNiftiMasker
import numpy as np
from joblib import Parallel, delayed
from nilearn.plotting import plot_stat_map
import matplotlib.pyplot ... |
landlab/landlab | notebooks/tutorials/flow_direction_and_accumulation/the_Flow_Director_Accumulator_PriorityFlood.ipynb | mit | # import plotting tools
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib as mpl
# import numpy
import numpy as np
# import necessary landlab components
from landlab import RasterModelGrid... |
emjotde/UMZ | Wyklady/05/Wieloklasyfikacja.ipynb | cc0-1.0 | import pandas
data = pandas.read_csv("iris.csv", header=None,
names=["lod.dl.", "lod.sz.", "pl.dl.", "pl.sz.", "Gatunek"])
data[:8]
"""
Explanation: Wieloklasyfikacja
Regresja logistyczna i liniowa
Przypomnienie: <br/>Zgadnienie klasyfikacji wieloklasowej
$$\textrm{Zbiór klas: } \qquad C = { c_1, c_2, \cdots, c_k } ... |
KJE2001/seminars | 04_operators_and_commutators.ipynb | mit | from sympy import *
# Define symbols
x, y, z = symbols('x y z')
# We want results to be printed to screen
init_printing(use_unicode=True)
# Calculate the derivative with respect to x
diff(exp(x**2), x)
"""
Explanation: <figure>
<IMG SRC="gfx/Logo_norsk_pos.png" WIDTH=100 ALIGN="right">
</figure>
Operators and commu... |
snowicecat/umich-eecs445-f16 | handsOn_lecture11_info-theory-decision-trees/handsOn11.ipynb | mit | from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression, Perceptron
import numpy as np
import matplotlib.pyplot as plt
from mlxtend.evaluate import plot_decision_regions
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
"""
Explanation: EECS 445: Machine Le... |
bjodah/PubChemPy | examples/CAS registry numbers.ipynb | mit | import re
import pubchempy as pcp
"""
Explanation: Retrieving CAS registry numbers
End of explanation
"""
import logging
logging.getLogger('pubchempy').setLevel(logging.DEBUG)
"""
Explanation: Enable debug logging to make it easier to see what is going on:
End of explanation
"""
def get_substructure_cas(smiles):... |
brainsqueeze/Open_Ag_examples | open_ag_tutorials.ipynb | mit | from sklearn.datasets import fetch_20newsgroups
import numpy as np
"""
Explanation: Scikit-learn API examples
End of explanation
"""
newsgroups_train = fetch_20newsgroups(subset='train')
newsgroups_test = fetch_20newsgroups(subset='test')
print newsgroups_train.keys(), '\n'
print newsgroups_train['data'][:2], '\n'... |
peterdalle/mij | 3 News robot/Earthquake news robot.ipynb | gpl-3.0 | # Import datetime to use dates.
from datetime import *
# The data comes in a dictionary (key-value pairs). Note that it looks just like JSON!
data = {
"Richter": 7.5,
"Latitud": 12,
"Longitud": 12,
"City": "Gothenburg",
"Country": "Sweden",
"Datetime": "2017-02-01 22:15:... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/migration/UJ2,12 Vertex SDK Custom Image Classification with pre-built training container.ipynb | apache-2.0 | import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex AI: Vertex AI Migration: Custom Image Classification w/pre-built training container
<tab... |
napsternxg/DataMiningPython | Lecture Notebooks/Redoing Weka stuff.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
from scipy.io import arff
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import patsy
import statsmodels.api as sm
from sklearn import tree, linear_model, metrics, dummy, naive_bayes, neighbors
from IPython.display import Image
import pydotplus
sns.s... |
nudomarinero/mltier1 | Match_LOFAR_combined_final.ipynb | gpl-3.0 | import numpy as np
from astropy.table import Table, join
from astropy import units as u
from astropy.coordinates import SkyCoord, search_around_sky
from IPython.display import clear_output
import pickle
import os
from mltier1 import (get_center, Field, MultiMLEstimator, parallel_process, get_sigma_all, describe)
%loa... |
astroumd/GradMap | notebooks/Lectures2019/Lecture1/GradMap_L1_Student.ipynb | gpl-3.0 | ## You can use Python as a calculator:
5*7 #This is a comment and does not affect your code.
#You can have as many as you want.
#Comments help explain your code to others and yourself
#No worries.
5+7
5-7
5/7
"""
Explanation: Introduction to "Doing Science" in Python for REAL Beginners
Python is one of many lang... |
mclaughlin6464/pearce | notebooks/Make MCMC Cfgs for Aemulus.ipynb | mit | import yaml
import copy
from os import path
import numpy as np
orig_cfg_fname = '/u/ki/swmclau2/Git/pearce/bin/mcmc/nh_gg_sham_hsab_mcmc_config.yaml'
with open(orig_cfg_fname, 'r') as yamlfile:
orig_cfg = yaml.load(yamlfile)
bsub_template="""#BSUB -q long
#BSUB -W 72:00
#BSUB -J {jobname}
#BSUB -oo /u/ki/swmclau... |
alephcero/adsProject | 3. Model Evaluation and Selection.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import os
import sys
import simpledbf
%pylab inline
import matplotlib.pyplot as plt
import statsmodels.api as sm
from sklearn.model_selection import train_test_split
from sklearn import linear_model
"""
Explanation: New York University
Applied Data Science 2016 Final Project
Mea... |
GoogleCloudPlatform/training-data-analyst | self-paced-labs/ai-platform-qwikstart/ai_platform_qwik_start.ipynb | apache-2.0 | import os
"""
Explanation: AI Platform: Qwik Start
This lab gives you an introductory, end-to-end experience of training and prediction on AI Platform. The lab will use a census dataset to:
Create a TensorFlow 2.x training application and validate it locally.
Run your training job on a single worker instance in the c... |
cliburn/sta-663-2017 | notebook/06_Graphics.ipynb | mit | import warnings
warnings.filterwarnings("ignore")
"""
Explanation: Graphics in Python
The foundational package for most graphics in Python is matplotlib, and the seaborn package builds on this to provide more statistical graphing options. We will focus on these two packages, but there are many others if these don't me... |
bassio/omicexperiment | omicexperiment/docs/01_experiment_basics.ipynb | bsd-3-clause | %load_ext autoreload
%autoreload 2
from omicexperiment.experiment.microbiome import MicrobiomeExperiment
mapping = "example_map.tsv"
biom = "example_fungal.biom"
tax = "blast_tax_assignments.txt"
#the MicrobiomeExperiment constructor currently needs three parameters
exp = MicrobiomeExperiment(biom, mapping,tax)
#the... |
cmry/cmry.github.io | sources/serialize_sk2.ipynb | mit | import serialize_sk as sr
def deserialize(class_init, attr):
for k, v in attr.items():
setattr(class_init, k, sr.json_to_data(v))
return class_init
"""
Explanation: Scikit-learn Pipeline Persistence and JSON Serialization Part II
By Chris Emmery, 14-04-2016, 5 minute read
This is a follow-up to this... |
csc-training/python-introduction | notebooks/answers/4 - Functions and exceptions.ipynb | mit | def celsius_to_kelvin(c):
return c + 273.15
celsius_to_kelvin(0)
"""
Explanation: Functions and exceptions
Functions
Write a function that converts from Celsius to Kelvin.
To convert from Centigrade to Kelvin you add 273.15 to the value.
Try your solution for a few values.
End of explanation
"""
def fahrenheit_... |
ageron/ml-notebooks | extra_capsnets-cn.ipynb | apache-2.0 | from IPython.display import IFrame
IFrame(src="https://www.youtube.com/embed/pPN8d0E3900", width=560, height=315, frameborder=0, allowfullscreen=True)
"""
Explanation: 胶囊网络(CapsNets)
基于论文：Dynamic Routing Between Capsules，作者：Sara Sabour, Nicholas Frosst and Geoffrey E. Hinton (NIPS 2017)。
部分启发来自于Huadong Liao的实现CapsNet-... |
blua/deep-learning | weight-initialization/.ipynb_checkpoints/weight_initialization-checkpoint.ipynb | mit | %matplotlib inline
import tensorflow as tf
import helper
from tensorflow.examples.tutorials.mnist import input_data
print('Getting MNIST Dataset...')
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print('Data Extracted.')
"""
Explanation: Weight Initialization
In this lesson, you'll learn how to fin... |
ppham27/MLaPP-solutions | chap07/7.ipynb | mit | %matplotlib inline
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd
from linreg import *
np.random.seed(2016)
def make_data(N):
X = np.linspace(0, 20, N)
Y = stats.norm.rvs(size=N, loc=-1.5*X + X*X/9, scale=2)
return X, Y
X, Y = make_data(21)
print(np.column... |
jseabold/statsmodels | examples/notebooks/pca_fertility_factors.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.multivariate.pca import PCA
plt.rc("figure", figsize=(16,8))
plt.rc("font", size=14)
"""
Explanation: statsmodels Principal Component Analysis
Key ideas: Principal component analysis, world bank data, fertility
In this n... |
derrowap/MA490-MachineLearning-FinalProject | project.ipynb | mit | data_inorder = pd.read_csv('Data\\adder_inorder_data.csv')
data_inorder = data_inorder[['Steps', 'MSE']]
data_inorder = data_inorder.sort_values(['Steps'])
data_inorder.head(9)
data_rnd_0 = pd.read_csv('Data\\adder_random_0_data.csv')
data_rnd_0 = data_rnd_0[['Steps', 'MSE']]
data_rnd_0 = data_rnd_0.sort_values(['Step... |
GoogleCloudPlatform/bigquery-notebooks | notebooks/community/analytics-componetized-patterns/retail/recommendation-system/bqml-scann/tfx01_interactive.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
!pip install -U -q tfx
"""
Explanation: Create an interactive TFX pipeline
This notebook is the first of two notebooks that guide you through automating the Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN solution with a pipeline.
Use this noteb... |
davidthomas5412/PanglossNotebooks | MassLuminosityProject/DataAndMassPrior_2017_02_08.ipynb | mit | from IPython.display import Image
Image(filename='pgm_mock_data.png')
"""
Explanation: Generate Mock Data
End of explanation
"""
from scipy.stats import norm
import numpy as np
np.random.seed(1)
alpha1 = norm(10.709, 0.022).rvs()
alpha2 = norm(0.359, 0.009).rvs()
alpha3 = 2.35e14
alpha4 = norm(1.10, 0.06).rvs()
S ... |
tjwei/HackNTU_Data_2017 | Week03/00-Download-M06A.ipynb | mit | from urllib.request import urlopen, urlretrieve
import tqdm
"""
Explanation: 下載 ETC M06A 資料
<a href="http://www.freeway.gov.tw/UserFiles/File/TIMCCC/TDCS%E4%BD%BF%E7%94%A8%E6%89%8B%E5%86%8A(tanfb)v3.0-1.pdf">國道高速公路電子收費交通資料蒐集支援系統(Traffic Data Collection System,TDCS)使用手冊</a>
End of explanation
"""
# 歷史資料網址
data_baseur... |
Echelle/AO_bonding_paper | notebooks/SiGaps_11_etalon_trans.ipynb | mit | %pylab inline
import pandas as pd
import seaborn as sns
sns.set_context("paper", font_scale=2.0, rc={"lines.linewidth": 2.5})
sns.set(style="ticks")
"""
Explanation: This IPython Notebook is for showing the family of solutions to the Fabry-Perot etalon transmission.
The filename of the figure is etalon_trans.pdf.
Aut... |
therealAJ/python-sandbox | data-science/learning/ud1/DataScience/NaiveBayes.ipynb | gpl-3.0 | import os
import io
import numpy
from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
def readFiles(path):
for root, dirnames, filenames in os.walk(path):
for filename in filenames:
path = os.path.join(root, filen... |
bzamecnik/ml-playground | snippets/keras/keras_hello_world.ipynb | mit | %pylab inline
from keras.layers.core import Dense, Activation
from keras.models import Sequential
from keras.utils import np_utils
from sklearn.cross_validation import train_test_split
from sklearn.datasets.samples_generator import make_blobs
from sklearn.metrics import classification_report, confusion_matrix
"""
Ex... |
DJCordhose/ai | notebooks/tensorflow/tf_low_level_advanced.ipynb | mit | # import and check version
import tensorflow as tf
# tf can be really verbose
tf.logging.set_verbosity(tf.logging.ERROR)
print(tf.__version__)
# a small sanity check, does tf seem to work ok?
hello = tf.constant('Hello TF!')
sess = tf.Session()
print(sess.run(hello))
sess.close()
"""
Explanation: <a href="https://co... |
elastic/examples | Machine Learning/Data Frames/pivot_review_data_pandas.ipynb | apache-2.0 | import bz2
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pandas.plotting import scatter_matrix
"""
Explanation: Pivot review data in pandas
This notebook shows how data can be pivoted by python pandas to reveal insights into the behaviour of reviewers. The use case and data is from Mark H... |
GoogleCloudPlatform/nvidia-merlin-on-vertex-ai | 03-model-inference-hugectr.ipynb | apache-2.0 | import json
import os
import shutil
import time
from pathlib import Path
from src.serving import export
from google.cloud import aiplatform as vertex_ai
"""
Explanation: Serving models using NVIDIA Triton Inference Server and Vertex AI Prediction
This notebook demonstrates how to serve NVIDIA Merlin HugeCTR deep lea... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/migration/UJ2,12 legacy Custom Training Prebuilt Container TF Keras.ipynb | apache-2.0 | ! pip3 install google-cloud-storage
"""
Explanation: Vertex SDK: Train & deploy a TensorFlow model with hosted runtimes (aka pre-built containers)
Installation
Install the Google cloud-storage library as well.
End of explanation
"""
import os
if not os.getenv("AUTORUN"):
# Automatically restart kernel after ins... |
brettavedisian/phys202-2015-work | assignments/assignment06/ProjectEuler17.ipynb | mit | def number_to_words(n):
"""Given a number n between 1-1000 inclusive return a list of words for the number."""
num_to_word={'1':'one','2':'two','3':'three','4':'four','5':'five','6':'six','7':'seven','8':'eight','9':'nine','10':'ten',
'11':'eleven','12':'twelve','13':'thirteen','14':'fourteen',... |
spencerchan/ctabus | notebooks/Toward Neighborhood-Level Analysis - Bus Service in Logan Square.ipynb | gpl-3.0 | commareas = gpd.read_file("../data/raw/geofences/Boundaries - Community Areas (current).geojson")
commareas.plot()
commareas.head()
"""
Explanation: Introduction <a name="introduction"></a>
With the large volume of CTA bus location data I have collected so far in 2019, I want to develop a process for analyzing the dat... |
mcneela/Retina | demos/mcculloch-pitts/McCulloch-Pitts Neurons.ipynb | bsd-3-clause | class MPNeuron(object):
def __init__(self, threshold, inputs):
self.threshold = threshold
self.inputs = inputs
def activate(self):
excitations = 0
for trigger in self.inputs:
if trigger.excitatory:
excitations += trigger.value
else:
... |
martinjrobins/hobo | examples/stats/custom-logpdf.ipynb | bsd-3-clause | import numpy as np
import pints
class Rosenbrock(pints.LogPDF):
def __init__(self, a=1, b=100):
self._a = a
self._b = b
def __call__(self, x):
return - np.log((self._a - x[0])**2 + self._b * (x[1] - x[0]**2)**2)
def n_parameters(self):
return 2
"""
Explanation: Writing a ... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/hadgem3-gc31-hh/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-hh', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: MOHC
Source ID: HADGEM3-GC31-HH
Topic: Seaice
Sub-Topics: Dynamics, Thermody... |
Vvkmnn/books | AutomateTheBoringStuffWithPython/lesson44.ipynb | gpl-3.0 | import PyPDF2
"""
Explanation: Lesson 44:
Reading and Editing PDFs
PDF files are binary files, which are more complex than text files, since they contain formatting information, images, and other assets.
PDF is great for printing, but not great for software, which work by typically parsing plain text.
The PyPDF2 modu... |
littlewine/USelections2016 | LDA extract topics.ipynb | mit | from pymongo import MongoClient
import json
client = MongoClient()
db = client.Twitter
import pandas as pd
import time
import re
from nltk.tokenize import RegexpTokenizer
import HTMLParser # In Python 3.4+ import html
import nltk
from nltk.corpus import stopwords
"""
Explanation: In this notebook, we will train an ... |
atlury/deep-opencl | DL0110EN/3.2.1.logistic_regression_with_mean_square_error_v2.ipynb | lgpl-3.0 | # Import the libraries we need for this lab
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
import torch
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
"""
Explanation: <a href="http://cocl.us/pytorch_link_top">
<img src="https://cocl.us/Pytorch_top" wi... |
kriete/cie5703_notebooks | week_7_spatial_students.ipynb | mit | from rpy2.robjects.packages import importr
from rpy2.robjects import r
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: This is a python / R implementation for spatial analysis of radar rainfall fields. All courtesy for the R code implementation goes to Marc ... |
Vettejeep/Data-Analysis-and-Data-Science-Projects | Principal Components Analysis on the UCI Image Segmentation Data Set.ipynb | gpl-3.0 | %matplotlib inline
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn import decomposition
from sklearn import metrics
"""
Explanation: Principal Com... |
zzsza/TIL | AutoGIS/02-Geometric-Objects-Spatial-Data-Model.ipynb | mit | from shapely.geometry import Point, LineString, Polygon
# Create Point geometric object(s) with coordinates
point1 = Point(2.2, 4.2)
point2 = Point(7.2, -25.1)
point3 = Point(9.26, -2.456)
point3D = Point(9.26, -2.456, 0.57)
# What is the type of the point?
point_type = type(point1)
print(point1)
print(point3D)
prin... |
colour-science/colour-ipython | notebooks/colour.ipynb | bsd-3-clause | from IPython.core.display import Image
Image(filename="resources/images/Colour_Logo_Medium_001.png")
"""
Explanation: Colour - Colour Science for Python
End of explanation
"""
%matplotlib inline
import colour
from colour.plotting import *
colour.filter_warnings(True, False)
colour_plotting_defaults()
visible_sp... |
Santara/ML-MOOC-NPTEL | lecture4/ML-Anirban_Tutorial4.ipynb | gpl-3.0 | iris = datasets.load_iris()
X = iris.data[:,:2]
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
"""
Explanation: 1. Support Vector Classification
1.1 Load the Iris dataset
End of explanation
"""
def evaluate_on_test_data(model=None):
predictions = model... |
ES-DOC/esdoc-jupyterhub | notebooks/mri/cmip6/models/sandbox-3/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mri', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: MRI
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbulen... |
nbelaid/nbelaid.github.io | dev/titanic/titanic.ipynb | mit | # Import numerical and data processing libraries
import numpy as np
import pandas as pd
# Import helpers that make it easy to do cross-validation
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
# Import machine learning models
from sklearn.linear_model import LinearRegres... |
mne-tools/mne-tools.github.io | 0.21/_downloads/7cf7296709bf473b6e7fed6bc98287be/plot_ems_filtering.ipynb | bsd-3-clause | # Author: Denis Engemann <denis.engemann@gmail.com>
# Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io, EvokedArray
from mne.datasets import sample
from mne.decoding import EMS, compute_ems
from sklearn.model_... |
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