repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
sserrot/lending_club_analysis | lending_club_notebook.ipynb | mit | df.drop('id', axis=1, inplace=True)
df.drop('member_id', axis=1, inplace=True)
df.drop('url', axis=1, inplace=True)
df.describe()
"""
Explanation: My interest is in the loan status. I want to know if I can predict if a loan will be charged off or not.
Thus there are multiple predictors that can be removed since the... |
madhurilalitha/Python-Projects | AnomaliesTwitterText/anomalies_in_tweets.ipynb | mit | import nltk
import pandas as pd
import numpy as np
data = pd.read_csv("original_train_data.csv", header = None,delimiter = "\t", quoting=3,names = ["Polarity","TextFeed"])
#Data Visualization
data.head()
"""
Explanation: "Detection of anomalous tweets using supervising outlier techniques"
Importing the Dependencies ... |
mssalvador/WorkflowCleaning | notebooks/Semi_supervised_workflow.ipynb | apache-2.0 | %run -i initilization.py
from pyspark.sql import functions as F
from pyspark.ml import clustering
from pyspark.ml import feature
from pyspark.sql import DataFrame
from pyspark.sql import Window
from pyspark.ml import Pipeline
from pyspark.ml import classification
from pyspark.ml.evaluation import BinaryClassification... |
cmorgan/toyplot | docs/markers.ipynb | bsd-3-clause | import numpy
import toyplot
y = numpy.linspace(0, 1, 20) ** 2
toyplot.scatterplot(y, width=300);
"""
Explanation: .. _markers:
Markers
In Toyplot, markers are used to show the individual datums in scatterplots:
End of explanation
"""
canvas = toyplot.Canvas(600, 300)
canvas.axes(grid=(1, 2, 0)).plot(y)
canvas.axes(... |
bhargavvader/gensim | docs/notebooks/Corpora_and_Vector_Spaces.ipynb | lgpl-2.1 | import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
import os
import tempfile
TEMP_FOLDER = tempfile.gettempdir()
print('Folder "{}" will be used to save temporary dictionary and corpus.'.format(TEMP_FOLDER))
"""
Explanation: Tutorial 1: Corpora and Vector Spaces... |
AndreySheka/dl_ekb | hw10/Seminar10-RNN-homework-ru.ipynb | mit |
#тут будет текст
corpora = ""
for fname in os.listdir("codex"):
import sys
if sys.version_info >= (3,0):
with open("codex/"+fname, encoding='cp1251') as fin:
text = fin.read() #If you are using your own corpora, make sure it's read correctly
corpora += text
else:
... |
adriantorrie/adriantorrie.github.io | downloads/notebooks/udacity/deep_learning_foundations_nanodegree/project_1_notes_introduction_to_neural_networks.ipynb | mit | %run ../../../code/version_check.py
"""
Explanation: Summary
Notes taken to help for the first project for the Deep Learning Foundations Nanodegree course dellivered by Udacity.
My Github repo for this project can be found here: adriantorrie/udacity_dlfnd_project_1
Table of Contents
Neural network
Output Formula
In... |
datascience-practice/data-quest | python_introduction/beginner/.ipynb_checkpoints/booleans-and-if-statements-checkpoint.ipynb | mit | cat = True
dog = False
print(type(cat))
"""
Explanation: 1: Booleans
Instructions
Assign the value True to the variable cat and the value False to the variable dog. Then use the print() function and the type() function to display the type for cat.
Answer
End of explanation
"""
from cities import cities
print(citie... |
tensorflow/examples | courses/udacity_intro_to_tensorflow_for_deep_learning/l09c01_nlp_turn_words_into_tokens.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... |
t73liu/crypto-trading | quant/DailyGapFill.ipynb | mit | gap_fill_by_month = candles.groupby(["month", "gap_filled"]).size()
gap_fill_by_month.groupby("month").apply(lambda g: g / g.sum() * 100)
"""
Explanation: Gap fill rates decrease as the gap size increases.
End of explanation
"""
gap_fill_by_day_of_week = candles.groupby(["day_of_week", "gap_filled"]).size()
gap_fill... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/migration/UJ8 Vertex SDK AutoML Text Sentiment Analysis.ipynb | apache-2.0 | import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex AI: Vertex AI Migration: AutoML Text Sentiment Analysis
<table align="left">
<td>
... |
empet/geom_modeling | FP-Bezier-Bspline.ipynb | bsd-2-clause | from IPython.display import Image
Image(filename='Imag/Decast4p.png')
"""
Explanation: <center> Interactive generation of Bézier and B-spline curves.<br> Python functional programming implementation of the <br> de Casteljau and Cox-de Boor algorithms </center>
The aim of this IPython notebook is two... |
calroc/joypy | docs/Advent of Code 2017 December 1st.ipynb | gpl-3.0 | from notebook_preamble import J, V, define
"""
Explanation: Advent of Code 2017
December 1st
[Given] a sequence of digits (your puzzle input) and find the sum of all digits that match the next digit in the list. The list is circular, so the digit after the last digit is the first digit in the list.
For example:
1122 ... |
phoebe-project/phoebe2-docs | 2.2/tutorials/pitch_yaw.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Misalignment (Pitch & Yaw)
Setup
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
%matplotlib i... |
tylere/docker-tmpnb-ee | notebooks/1 - IPython Notebook Examples/IPython Project Examples/IPython Kernel/Capturing Output.ipynb | apache-2.0 | from __future__ import print_function
import sys
"""
Explanation: Capturing Output With <tt>%%capture</tt>
IPython has a cell magic, %%capture, which captures the stdout/stderr of a cell. With this magic you can discard these streams or store them in a variable.
End of explanation
"""
%%capture
print('hi, stdout')
p... |
nathanielng/machine-learning | perceptron/digit-recognition.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from itertools import product
from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score, cross_val_predict, ShuffleSplit, KFold
from tqdm import tqdm
from IPython.display import display, Math, Latex, HTML
%matplotlib inline
np.... |
rsterbentz/phys202-2015-work | assignments/assignment05/InteractEx04.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 4
Imports
End of explanation
"""
def random_line(m, b, sigma, size=10):
"""Create a line y = m*x + b + N(0,si... |
mne-tools/mne-tools.github.io | stable/_downloads/26b665b81df8d69d2764306260a9ffa9/cluster_stats_evoked.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.stats import permutation_cluster_test
from mne.datasets import sample
print(__doc__)
"""
Explanation: Permutation F-test on sensor data with 1D cluster level
O... |
AllenDowney/ThinkBayes2 | notebooks/chap11.ipynb | mit | # If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename... |
YAtOff/python0-reloaded | week4/Conditionals.ipynb | mit | a = 2
b = 1
if a > b:
print('a is greater then b')
a = 1
b = 2
if a < b:
print('a is less than b')
a = 1
b = 1
if a == b:
print('a is equal to b')
a = 1
b = 2
if a != b:
print('a is not equal to b')
"""
Explanation: Условен оператор (if)
Условният оператор променя поведението на програмата, на база... |
boffi/boffi.github.io | dati_2018/03/Constant_Acceleration.ipynb | mit | m = 1.00
k = 4*pi*pi
wn = 2*pi
T = 1.0
z = 0.02
wd = wn*sqrt(1-z*z)
c = 2*z*wn*m
"""
Explanation: Constant Acceleration
Define the Dynamical System
End of explanation
"""
NSTEPS = 200 # steps per second
h = 1.0 / NSTEPS
def load(t):
return np.where(t<0, 0, np.where(t<5, sin(0.5*wn*t)**2, 0))
t = np.linspace(-1,... |
IanHawke/Southampton-PV-NumericalMethods-2016 | solutions/05-Partial-Differential-Equations.ipynb | mit | from __future__ import division
import numpy
from matplotlib import pyplot
%matplotlib notebook
dt = 1e-5
dx = 1e-2
x = numpy.arange(0,1+dx,dx)
y = numpy.zeros_like(x)
y = x * (1 - x)
def update_heat(y, dt, dx):
dydt = numpy.zeros_like(y)
dydt[1:-1] = dt/dx**2 * (y[2:] + y[:-2] - 2*y[1:-1])
return dydt
N... |
marko911/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... |
ES-DOC/esdoc-jupyterhub | notebooks/cccr-iitm/cmip6/models/sandbox-3/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccr-iitm', 'sandbox-3', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: CCCR-IITM
Source ID: SANDBOX-3
Sub-Topics: Radiative Forcings.
Propertie... |
akhilaananthram/nupic.research | sdr_paper/sdr_math_neuron_paper.ipynb | gpl-3.0 | oxp = Symbol("Omega_x'")
b = Symbol("b")
n = Symbol("n")
theta = Symbol("theta")
w = Symbol("w")
s = Symbol("s")
a = Symbol("a")
subsampledOmega = (binomial(s, b) * binomial(n - s, a - b)) / binomial(n, a)
subsampledFpF = Sum(subsampledOmega, (b, theta, s))
subsampledOmegaSlow = (binomial(s, b) * binomial(n - s, a - b... |
pybel/pybel | notebooks/Compiling a BEL Document.ipynb | mit | import os
from urllib.request import urlretrieve
import pybel
import logging
logging.getLogger('pybel').setLevel(logging.DEBUG)
logging.basicConfig(level=logging.DEBUG)
logging.getLogger('urllib3').setLevel(logging.WARNING)
print(pybel.get_version())
DESKTOP_PATH = os.path.join(os.path.expanduser('~'), 'Desktop')
... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session09/Day5/BayesianBlocksDSFP_Problems.ipynb | mit | # execute this cell
np.random.seed(0)
x = np.concatenate([stats.cauchy(-5, 1.8).rvs(500),
stats.cauchy(-4, 0.8).rvs(2000),
stats.cauchy(-1, 0.3).rvs(500),
stats.cauchy(2, 0.8).rvs(1000),
stats.cauchy(4, 1.5).rvs(500)])
# truncate values to... |
tuanavu/python-cookbook-3rd | notebooks/.ipynb_checkpoints/04_finding_the_largest_or_smallest_n_items-checkpoint.ipynb | mit | import heapq
nums = [1, 8, 2, 23, 7, -4, 18, 23, 42, 37, 2]
print(heapq.nlargest(3, nums)) # Prints [42, 37, 23]
print(heapq.nsmallest(3, nums)) # Prints [-4, 1, 2]
"""
Explanation: Keeping the Last N Items
Problem
You want to make a list of the largest or smallest N items in a collection.
Solution
The heapq modu... |
amandersillinois/landlab | notebooks/tutorials/data_record/DataRecord_tutorial.ipynb | mit | import numpy as np
from landlab import RasterModelGrid
from landlab.data_record import DataRecord
from landlab import imshow_grid
import matplotlib.pyplot as plt
from matplotlib.pyplot import plot, subplot, xlabel, ylabel, title, legend, figure
%matplotlib inline
"""
Explanation: <a href="http://landlab.github.io"><i... |
Olsthoorn/TransientGroundwaterFlow | exercises_notebooks/ReversibleStorage.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import pdb
"""
Explanation: Reversible groundwater storage
End of explanation
"""
dg = np.array([0.002, 0.063, 0.2, 0.630, 2.0 ]) * 1e-3 # mm
"""
Explanation: Introduction
In the remainder of this syllabus, we will restrict ourselves to reversible groundwater stora... |
davidthomas5412/PanglossNotebooks | GroupMeeting_11_16.ipynb | mit | %matplotlib inline
from matplotlib import rc
rc("font", family="serif", size=14)
rc("text", usetex=True)
import daft
pgm = daft.PGM([7, 6], origin=[0, 0])
#background nodes
pgm.add_plate(daft.Plate([0.5, 3.0, 5, 2], label=r"foreground galaxy $i$",
shift=-0.1))
pgm.add_node(daft.Node("theta", r"$\theta$", 3.5, 5... |
DavidQiuChao/CS231nHomeWorks | assignment2/BatchNormalization.ipynb | mit | # As usual, a bit of setup
from __future__ import print_function
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solv... |
dougalsutherland/mmd | examples/mmd regression example.ipynb | bsd-3-clause | n = 500
mean = np.random.normal(0, 10, size=n)
var = np.random.gamma(5, size=n)
n_samp = np.random.randint(10, 500, size=n)
samps = [np.random.normal(m, v, size=s)[:, np.newaxis]
for m, v, s in zip(mean, var, n_samp)]
# this gives us a progress bar for MMD computations
from mmd.utils import show_progress
show... |
joferkington/tutorials | 1506_Seismic_petrophysics_2/Seismic_petrophysics_2.ipynb | apache-2.0 | def frm(vp1, vs1, rho1, rho_f1, k_f1, rho_f2, k_f2, k0, phi):
vp1 = vp1 / 1000.
vs1 = vs1 / 1000.
mu1 = rho1 * vs1**2.
k_s1 = rho1 * vp1**2 - (4./3.)*mu1
# The dry rock bulk modulus
kdry = (k_s1 * ((phi*k0)/k_f1+1-phi)-k0) / ((phi*k0)/k_f1+(k_s1/k0)-1-phi)
# Now we can apply Gassman... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/06_structured/3_keras_wd.ipynb | apache-2.0 | # Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.1
# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = RE... |
tensorflow/docs | site/en/guide/variable.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... |
PyladiesMx/Empezando-con-Python | Pandas second part/Pandas segunda parte.ipynb | mit | #Cargamos los paquetes necesarios
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#Creamos arreglos de datos por un arreglo de numpy
arreglo = np.random.randn(7,4)
columnas = list('ABCD')
df = pd.DataFrame(arreglo, columns=columnas )
df
#Creamos arreglo de datos por diccionario
df2 = pd.Dat... |
BrownDwarf/ApJdataFrames | notebooks/Luhman1999.ipynb | mit | import warnings
warnings.filterwarnings("ignore")
from astropy.io import ascii
import pandas as pd
"""
Explanation: ApJdataFrames Luhman1999
Title: Low-Mass Star Formation and the Initial Mass Function in the ρ Ophiuchi Cloud Core
Authors: K. L. Luhman and G.H. Rieke
Data is from this paper:
http://iopscience.iop.or... |
Naereen/notebooks | Demo_of_RISE_for_slides_with_Jupyter_notebooks__Julia.ipynb | mit | from sys import version
print(version)
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Demo-of-RISE-for-slides-with-Jupyter-notebooks-(Python)" data-toc-modified-id="Demo-of-RISE-for-slides-with-Jupyter-notebooks-(Python)-1"><span class="toc-item-num">1 </span>Demo of RISE for sli... |
ProfessorKazarinoff/staticsite | content/code/matplotlib_plots/yy-plots.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: In this post, you will learn how to create y-y plots with Python and Matplotlib. y-y plots are a type of line plot where one line corresponds to one y-axis and another line on the same plot corresponds to a different y-axis. y-y pl... |
KnHuq/Dynamic-Tensorflow-Tutorial | Vhanilla_RNN/RNN.ipynb | mit | import numpy as np
import tensorflow as tf
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
import pylab as pl
from IPython import display
import sys
%matplotlib inline
"""
Explanation: <span style="color:green"> VANILLA RNN ON 8*8 MNIST DATASET TO PREDICT TEN CLASS
<span... |
empet/Plotly-plots | Europe-Happiness.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
import matplotlib.cm as cm
df = pd.read_excel('Data/Europe-Happiness.xlsx')
df.head()
N = len(df)
score = df['Score'].values
country = list(df.Country)
country[13] = 'U Kingdom'
country[14] = 'Czech Rep'
country[38] = 'Bosn Herzeg'
world_rk = list(df['World-Rank'])
import plot... |
nifannn/MachineLearningNotes | MathNotes/LinearAlgebra.ipynb | mit | import numpy as np
"""
Explanation: Linear Algebra Review Notes
End of explanation
"""
A = np.array([[1,1,1], [3,1,2], [2,3,4]])
b = np.array([6, 11, 20])
A
b
x = np.linalg.solve(A, b)
x
"""
Explanation: Gaussian Elimination
one way to solve $ Ax = b $
For example:
$ \begin{bmatrix}1 & 1 & 1 \ 3 & 1 & 2 \ 2... |
jorgemauricio/INIFAP_Course | ejercicios/Seaborn/6_Color_Estilo.ipynb | mit | import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
tips = sns.load_dataset('tips')
"""
Explanation: Estilos y color
En este ejercicio se demostrara como controlar la estetica de las graficas en seaborn:
End of explanation
"""
sns.countplot(x='sex',data=tips)
sns.set_style('white')
sns.countplo... |
zaqwes8811/micro-apps | self_driving/deps/Kalman_and_Bayesian_Filters_in_Python_master/00-Preface.ipynb | mit | from __future__ import division, print_function
%matplotlib inline
#format the book
import book_format
book_format.set_style()
"""
Explanation: Table of Contents
Preface
End of explanation
"""
import numpy as np
x = np.array([1, 2, 3])
print(type(x))
x
"""
Explanation: Introductory textbook for Kalman filters and ... |
guyk1971/deep-learning | face_generation/dlnd_face_generation_orig.ipynb | mit | data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
"""
Explanation: Face Generation
In this project, you'll use generative adv... |
mgalardini/2017_python_course | notebooks/1-Basic_python_and_native_python_data_structures.ipynb | gpl-2.0 | import this
"""
Explanation: Basic Python and native data structures
In a nutshell
Scripting language
Multi-platform (OsX, Linux, Windows)
Battery-included
Lots of third-party library (catching up with R for computational biology)
Lots of help available online (e.g. stackoverflow)
"Scripting language" means:
no typ... |
pklfz/fold | tensorflow_fold/g3doc/sentiment.ipynb | apache-2.0 | # boilerplate
import codecs
import functools
import os
import tempfile
import zipfile
from nltk.tokenize import sexpr
import numpy as np
from six.moves import urllib
import tensorflow as tf
sess = tf.InteractiveSession()
import tensorflow_fold as td
"""
Explanation: Sentiment Analysis with TreeLSTMs in TensorFlow Fol... |
fastai/fastai | nbs/17_callback.tracker.ipynb | apache-2.0 | #|export
class TerminateOnNaNCallback(Callback):
"A `Callback` that terminates training if loss is NaN."
order=-9
def after_batch(self):
"Test if `last_loss` is NaN and interrupts training."
if torch.isinf(self.loss) or torch.isnan(self.loss): raise CancelFitException
learn = synth_learner(... |
google-aai/tf-serving-k8s-tutorial | jupyter/keras_training_to_serving.ipynb | apache-2.0 | # Import Keras libraries
import keras.applications.resnet50 as resnet50
from keras.preprocessing import image
from keras import backend as K
import numpy as np
import os
# Import TensorFlow saved model libraries
import tensorflow as tf
from tensorflow.python.saved_model import builder as saved_model_builder
from tenso... |
Kaggle/learntools | notebooks/feature_engineering_new/raw/ex3.ipynb | apache-2.0 | # Setup feedback system
from learntools.core import binder
binder.bind(globals())
from learntools.feature_engineering_new.ex3 import *
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score
from xgboost import XGBRegressor
def score_dataset(X, y, model=XGBRegressor()):
# Label... |
prk327/CoAca | 3_Position_and_Label_Based_Indexing.ipynb | gpl-3.0 | # loading libraries and reading the data
import numpy as np
import pandas as pd
market_df = pd.read_csv("../global_sales_data/market_fact.csv")
market_df.head()
"""
Explanation: Position and Label Based Indexing: df.iloc and df.loc
You have seen some ways of selecting rows and columns from dataframes. Let's now see s... |
ComputoCienciasUniandes/MetodosComputacionalesLaboratorio | 2016-1/w04/.ipynb_checkpoints/sistemas_lineales-checkpoint.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Sistemas de ecuaciones lineales
En este notebook vamos a ver conceptos básicos para resolver sistemas de ecuaciones lineales.
La estructura de esta presentación está basada en http://nbviewer.ipython.org/github/mbakker7/exploratory_... |
janusnic/21v-python | unit_20/parallel_ml/notebooks/04 - Pandas and Heterogeneous Data Modeling.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import warnings
warnings.simplefilter('ignore', DeprecationWarning)
"""
Explanation: Predictive Modeling with heterogeneous data
End of explanation
"""
#!curl -s https://dl.dropboxusercontent.com/u/5743203/data/titanic/titanic... |
ContinualAI/avalanche | notebooks/from-zero-to-hero-tutorial/04_training.ipynb | mit | !pip install avalanche-lib=0.2.0
"""
Explanation: description: Continual Learning Algorithms Prototyping Made Easy
Training
Welcome to the "Training" tutorial of the "From Zero to Hero" series. In this part we will present the functionalities offered by the training module.
First, let's install Avalanche. You can skip... |
mccormd1/LCandR | LC_python/LC_MLtrain.ipynb | gpl-3.0 | import joblib
features=joblib.load('clean_LCfeatures.p')
labels=joblib.load('clean_LClabels.p')
clabels=joblib.load('clean_LCclassifierlabel.p')
import numpy as np
import pandas as pd
from time import time
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualiz... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_read_events.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Chris Holdgraf <choldgraf@berkeley.edu>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvi... |
minesh1291/Learning-Python | TensorFlow_Beginner/try2-LR-TFB.ipynb | apache-2.0 | import tensorflow as tf
"""
Explanation: https://www.youtube.com/watch?v=oxf3o8IbCk4 [23:33]
http://ischlag.github.io/2016/06/04/how-to-use-tensorboard/ outdated
https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard
Linear Regression
Import
End of explanation
"""
W = tf.Variable([0.3])
b = tf.Variab... |
netodeolino/TCC | TCC 02/Resultados/Geral/Análise geral sobre os dados criminais.ipynb | mit | import plotly.plotly as py
import plotly.graph_objs as go
import pandas
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import matplotlib.ticker as ticker
from scipy.stats import gaussian_kde
import matplotlib.image as mpimg
files = [
'Cluster-Crime-Janeiro', 'Cluster-Crime-Fevereiro'... |
johnpfay/environ859 | 07_DataWrangling/notebooks/03-Using-NumPy-With-Rasters.ipynb | gpl-3.0 | # Import the modules
import arcpy
import numpy as np
#Set the name of the file we'll import
demFilename = '../Data/DEM.tif'
#Import the DEM as a NumPy array, using only a 200 x 200 pixel subset of it
demRaster = arcpy.RasterToNumPyArray(demFilename)
#What is the shape of the raster (i.e. the # of rows and columns)? ... |
dwhswenson/contact_map | examples/concurrences.ipynb | lgpl-2.1 | from __future__ import print_function
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from contact_map import ContactFrequency, ResidueContactConcurrence, plot_concurrence
import mdtraj as md
traj = md.load("data/gsk3b_example.h5")
print(traj) # to see number of frames; size of system
"""
Expla... |
schiob/MusGen | Genetic_Chords.ipynb | mit | from IPython import display
display.Image('img/simple.jpg', width=400)
"""
Explanation: Creating a chord progression with a genetic algorithm
This work is the result of an experiment done some months ago. I used a simple genetic algorithm to find a solution to a classic exercise of harmony: given a certain voice (nor... |
ES-DOC/esdoc-jupyterhub | notebooks/messy-consortium/cmip6/models/sandbox-3/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'sandbox-3', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: SANDBOX-3
Topic: Ocean
Sub-Topics: Timestepp... |
HaoMood/cs231n | assignment3 (copy)/assignment3/ImageGradients.ipynb | gpl-3.0 | # As usual, a bit of setup
import time, os, json
import numpy as np
import skimage.io
import matplotlib.pyplot as plt
from cs231n.classifiers.pretrained_cnn import PretrainedCNN
from cs231n.data_utils import load_tiny_imagenet
from cs231n.image_utils import blur_image, deprocess_image
%matplotlib inline
plt.rcParams... |
kerimlcr/ab2017-dpyo | ornek/osmnx/osmnx-0.3/examples/05-example-save-load-networks-shapes.ipynb | gpl-3.0 | import osmnx as ox
%matplotlib inline
ox.config(log_file=True, log_console=True, use_cache=True)
place = 'Piedmont, California, USA'
"""
Explanation: Use OSMnx to construct place boundaries and street networks, and save as various file formats for working with later
Overview of OSMnx
GitHub repo
Examples, demos, tut... |
rrbb014/data_science | fastcampus_dss/2016_05_24/0524_01__베르누이 확률 분포.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 보통 성공을 정수 1 ($X=1$), 실패를 정수 0 ($X=0$)으로... |
Aniruddha-Tapas/Applied-Machine-Learning | Machine Learning using GraphLab/Analyzing Product Sentiment using GraphLab Create.ipynb | mit | import graphlab
"""
Explanation: Predicting sentiment from product reviews
<hr>
Import GraphLab Create
End of explanation
"""
products = graphlab.SFrame('amazon_baby.gl/')
"""
Explanation: Read some product review data
Loading reviews for a set of baby products.
End of explanation
"""
products.head()
"""
Explan... |
ioggstream/python-course | connexion-101/notebooks/02-openapi-3.ipynb | agpl-3.0 | remote_yaml = 'https://raw.githubusercontent.com/teamdigitale/api-starter-kit/master/openapi/simple.yaml.src'
render_markdown(f'''
[Swagger Editor]({oas_editor_url(remote_yaml)}) is a simple webapp
for editing OpenAPI 3 language specs.
''')
"""
Explanation: OpenAPI & Modeling
OpenAPI is a specification language
Ope... |
catalyst-cooperative/pudl | test/validate/notebooks/validate_fuel_ferc1.ipynb | mit | %load_ext autoreload
%autoreload 2
import sys
import pandas as pd
import sqlalchemy as sa
import pudl
import pudl.validate as pv
import warnings
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
handler = logging.StreamHandler(stream=sys.stdout)
formatter = logging.Formatter('%(message)s')
han... |
ecell/ecell4-notebooks | en/tests/MSD.ipynb | gpl-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy
from ecell4.prelude import *
"""
Explanation: Mean Square Displacement (MSD)
This is a validation for the E-Cell4 library. Here, we test a mean square displacement (MSD) for each simulation algorithms.
End of explanation
"""
radius, D = 0.005, 1
m = Net... |
probml/pyprobml | notebooks/book1/15/attention_torch.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import math
from IPython import display
try:
import torch
except ModuleNotFoundError:
%pip install -qq torch
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils import data
import random
import os
import time
np.random... |
cdawei/flickr-photo | src/suburb-names.ipynb | gpl-2.0 | from osgeo import ogr, osr
import pandas as pd
import numpy as np
import collections
"""
Explanation: Assign Suburbs to POIs
Use SA2 level from ABS to find suburbs. Uses file downloaded from ABS, which is the ZIP link called Statistical Area Level 2 (SA2) ASGS Ed 2011 Digital Boundaires in ESRI Shapefile Format. The f... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-1/cmip6/models/sandbox-3/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-1', 'sandbox-3', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: TEST-INSTITUTE-1
Source ID: SANDBOX-3
Topic: Seaice
Sub-Topics: Dynami... |
konstantinstadler/pymrio | doc/source/notebooks/working_with_eora26.ipynb | gpl-3.0 | import pymrio
eora_storage = '/tmp/mrios/eora26'
eora = pymrio.parse_eora26(year=2005, path=eora_storage)
"""
Explanation: Parsing the Eora26 EE MRIO database
Getting Eora26
The Eora 26 database is available at http://www.worldmrio.com .
You need to register there and can then download the files from http://www.wor... |
shubham0704/ATR-FNN | MAMs discussion.ipynb | mit | # dependencies
import matplotlib.pyplot as plt
import pickle
import numpy as np
f = open('final_dataset.pickle','rb')
dataset = pickle.load(f)
sample_image = dataset['train_dataset'][0]
sample_label = dataset['train_labels'][0]
print(sample_label)
plt.figure()
plt.imshow(sample_image)
plt.show()
# lets make Wxx and ... |
emiliom/stuff | pyoos_ndbc_demo.ipynb | cc0-1.0 | import pandas as pd
from pyoos.collectors.ndbc.ndbc_sos import NdbcSos
import owslib.swe.sensor.sml as owslibsml
fmt = '{:*^64}'.format
"""
Explanation: pyoos NDBC demo
Demo the capabilities of the pyoos NdbcSos collector.
Starting point was a notebook from Filipe, for the OOI Endurance Array, which has a spatial e... |
vadim-ivlev/STUDY | handson-data-science-python/DataScience-Python3/ConditionalProbabilitySolution.ipynb | mit | from numpy import random
random.seed(0)
totals = {20:0, 30:0, 40:0, 50:0, 60:0, 70:0}
purchases = {20:0, 30:0, 40:0, 50:0, 60:0, 70:0}
totalPurchases = 0
for _ in range(100000):
ageDecade = random.choice([20, 30, 40, 50, 60, 70])
purchaseProbability = 0.4
totals[ageDecade] += 1
if (random.random() < pu... |
ereodeereigeo/dataTritiumWS22 | numero_de_datos_perdidos_slide.ipynb | gpl-2.0 | import pandas as pd
"""
Explanation: Número de datos obtenidos y perdidos
Importamos las librerías necesarias
End of explanation
"""
import ext_datos as ext
import procesar as pro
import time_plot as tplt
"""
Explanation: Importamos las librerías creadas para trabajar
End of explanation
"""
dia1 = ext.extraer_da... |
0u812/nteract | example-notebooks/download-stats.ipynb | bsd-3-clause | import IPython.display
import pandas as pd
import requests
# Note:
data = requests.get('https://api.github.com/repos/nteract/nteract/releases').json()
print("{}:\n\t{}\n\t{}".format(
data[0]['tag_name'],
data[0]['assets'][0]['browser_download_url'],
data[0]['assets'][0]['download_count']
))
"""
Explanation:... |
ljchang/psyc63 | Notebooks/7_Introduction_to_Scraping.ipynb | mit | try:
import requests
except:
!pip install requests
try:
from bs4 import BeautifulSoup
except:
!pip install bs4
"""
Explanation: Introduction to Web Scraping
Often we are interested in getting data from a website. Modern websites are often built using a REST framework that has an Application Programmin... |
IBMDecisionOptimization/docplex-examples | examples/mp/jupyter/boxes.ipynb | apache-2.0 | import sys
try:
import docplex.mp
except:
raise Exception('Please install docplex. See https://pypi.org/project/docplex/')
"""
Explanation: Objects in boxes
This tutorial includes everything you need to set up IBM Decision Optimization CPLEX Modeling for Python (DOcplex), build a Mathematical Programming... |
sanger-pathogens/Roary | contrib/roary_plots/roary_plots.ipynb | gpl-3.0 | # Plotting imports
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
# Other imports
import os
import pandas as pd
import numpy as np
from Bio import Phylo
"""
Explanation: Roary pangenome plots
<h6><a href="javascript:toggle()" target="_self">Toggle source code</a></h6... |
sdss/marvin | docs/sphinx/jupyter/whats_new_v21.ipynb | bsd-3-clause | import matplotlib
%matplotlib inline
# only necessary if you have a local DB
from marvin import config
config.forceDbOff()
"""
Explanation: What's New in Marvin 2.1
Marvin is Python 3.5+ compliant!
End of explanation
"""
from marvin.tools.cube import Cube
plateifu = '8485-1901'
cube = Cube(plateifu=plateifu)
print(... |
lorgor/vulnmine | vulnmine/ipynb/170523-train-vendor-match.ipynb | gpl-3.0 | # Initialize
import pandas as pd
import numpy as np
import pip #needed to use the pip functions
# Show versions of all installed software to help debug incompatibilities.
for i in pip.get_installed_distributions(local_only=True):
print(i)
"""
Explanation: Train vendor matching algorithm
The ML classification al... |
chebee7i/palettable | demo/Cubehelix Demo.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Cubehelix Color Maps in Palettable
Cubehelix was designed by D.A. Green to provide a color mapping that would degrade gracefully to grayscale without losing information. This quality makes Cubehelix very useful for continuous colou... |
frankbearzou/Data-analysis | Pixar Movies/Pixar Movies.ipynb | mit | pixar_movies['Domestic %'] = pixar_movies['Domestic %'].str.rstrip('%').astype('float')
pixar_movies['International %'] = pixar_movies['International %'].str.rstrip('%').astype('float')
"""
Explanation: Data cleaning
Because Domestic % and International % columns data end with %, and its data type are objects, it is ... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/launching_into_ml/solutions/rapid_prototyping_bqml_automl.ipynb | apache-2.0 | import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = "--user"
# Install Python... |
edwardd1/phys202-2015-work | assignments/assignment04/MatplotlibEx01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Matplotlib Exercise 1
Imports
End of explanation
"""
import os
assert os.path.isfile('yearssn.dat')
"""
Explanation: Line plot of sunspot data
Download the .txt data for the "Yearly mean total sunspot number [1700 - now]" from th... |
ubcgif/gpgLabs | notebooks/em/FDEM_ThreeLoopModel.ipynb | mit | %matplotlib inline
from geoscilabs.em.FDEM3loop import interactfem3loop
from geoscilabs.em.FDEMpipe import interact_femPipe
from matplotlib import rcParams
rcParams['font.size'] = 14
"""
Explanation: Electromagnetics: 3-loop model
In the first part of this notebook, we consider a 3 loop system, consisting of a transm... |
alexkeenan/jupyternotebookworkflow | bike_exercise_deeper_analysis.ipynb | mit | from sklearn.decomposition import PCA
X=pivoted.fillna(0).T.values
X2=PCA(2).fit_transform(X)# this tells it the number of components we want to reduce this to is 2
X2.shape
import matplotlib.pyplot as plt
plt.scatter(X2[:,0],X2[:,1])
"""
Explanation: # First thing we're going to do is reduce the dimensionality ... |
bigdata-i523/hid335 | experiment/Python_SKLearn_RandomForestClassifier.ipynb | gpl-3.0 | import sklearn
import mglearn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import Image, display
from sklearn.tree import DecisionTreeClassifier
"""
Explanation: Introduction to Machine Learning
Andreas Muller and Sarah Guido
Ch. 2 Supervised Learnin... |
thomasantony/CarND-Projects | Exercises/Term1/TensorFlow-Tutorials/05_Ensemble_Learning.ipynb | mit | from IPython.display import Image
Image('images/02_network_flowchart.png')
"""
Explanation: TensorFlow Tutorial #05
Ensemble Learning
by Magnus Erik Hvass Pedersen
/ GitHub / Videos on YouTube
Introduction
This tutorial shows how to use a so-called ensemble of convolutional neural networks. Instead of using a single n... |
mulhod/reviewer_experience_prediction | jupyter_notebooks/Exploring_Label_Distribution.ipynb | mit | import math
import itertools
from collections import Counter
import numpy as np
import scipy as sp
from pymongo import collection
import matplotlib.pyplot as plt
%matplotlib inline
from src import *
from src.mongodb import *
from src.datasets import *
from src.experiments import *
from data import APPID_DICT
"""
Exp... |
arturops/deep-learning | intro-to-tensorflow/intro_to_tensorflow.ipynb | mit | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
print('All m... |
turbomanage/training-data-analyst | quests/serverlessml/04_keras/labs/keras_dnn.ipynb | apache-2.0 | %%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
import os, json, math
import numpy as np
import shutil
import tensorflow as tf
print("TensorFlow version: ",tf.version.VERSION)
PROJECT = "your-gcp-project-here" # REPLACE WITH YOUR PRO... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/quasibinomial.ipynb | bsd-3-clause | import statsmodels.api as sm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from io import StringIO
"""
Explanation: Quasi-binomial regression
This notebook demonstrates using custom variance functions and non-binary data
with the quasi-binomial GLM family to perform a regression analysis using... |
mommermi/Introduction-to-Python-for-Scientists | notebooks/CodeOptimization_20161209.ipynb | mit | import time
def square(x):
return x**2
def quadrature(func, a, b, n=10000000):
""" use the quadrature rule to determine the integral over the function from a to b"""
# calculate individual elements
integral_elements = [func(a)/2.]
for k in range(1, n):
integral_elements.append(func(a+k*fl... |
Benedicto/ML-Learning | numpy-tutorial.ipynb | gpl-3.0 | 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... |
brookthomas/GeneDive | preprocessing/Typeahead_ChemicalDisease.ipynb | mit | import sqlite3
import json
DATABASE = "data.sqlite"
conn = sqlite3.connect(DATABASE)
cursor = conn.cursor()
"""
Explanation: Build Adjacency Matrix
End of explanation
"""
# For getting the maximum row id
QUERY_MAX_ID = "SELECT id FROM interactions ORDER BY id DESC LIMIT 1"
# Get interaction data
QUERY_INTERACTION... |
hpparvi/PyTransit | notebooks/example_quadratic_model.ipynb | gpl-2.0 | %pylab inline
sys.path.append('..')
from pytransit import QuadraticModel
seed(0)
times_sc = linspace(0.85, 1.15, 1000) # Short cadence time stamps
times_lc = linspace(0.85, 1.15, 100) # Long cadence time stamps
k, t0, p, a, i, e, w = 0.1, 1., 2.1, 3.2, 0.5*pi, 0.3, 0.4*pi
pvp = tile([k, t0, p, a, i, e, w], (5... |
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