repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
squishbug/DataScienceProgramming | 03-NumPy-and-Linear-Algebra/Introduction_class.ipynb | cc0-1.0 | %matplotlib inline
import math
import numpy as np
import matplotlib.pyplot as plt
##import seaborn as sbn
##from scipy import *
"""
Explanation: Introduction to NumPy
Topics
Basic Synatx
creating vectors matrices
special: ones, zeros, identity eye
add, product, inverse
Mechanics: indexing, slicing, concatenating, r... |
zzsza/TIL | Tensorflow-Extended/TFDV(data validation) example.ipynb | mit | from __future__ import print_function
import sys, os
import tempfile, urllib, zipfile
# Confirm that we're using Python 2
assert sys.version_info.major is 2, 'Oops, not running Python 2'
# Set up some globals for our file paths
BASE_DIR = tempfile.mkdtemp()
DATA_DIR = os.path.join(BASE_DIR, 'data')
OUTPUT_DIR = os.pat... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_brainstorm_auditory.ipynb | bsd-3-clause | # Authors: Mainak Jas <mainak.jas@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# Jaakko Leppakangas <jaeilepp@student.jyu.fi>
#
# License: BSD (3-clause)
import os.path as op
import pandas as pd
import numpy as np
import mne
from mne import combine_evoked
from mne.minimum_norm impor... |
maxhutch/thesis-notebooks | Vorticity.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib
matplotlib.rcParams['figure.figsize'] = (10.0, 16.0)
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import interp1d, InterpolatedUnivariateSpline
from scipy.optimize import bisect
import json
from functools import partial
class Foo: pass
"""
Explanation: ... |
otavio-r-filho/AIND-Deep_Learning_Notebooks | sentiment-rnn/Sentiment_RNN_Solution.ipynb | mit | import numpy as np
import tensorflow as tf
with open('../sentiment-network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment-network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
"""
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural... |
WaylonWalker/pyDataVizDay | notebooks/Explore Movie Dataset.ipynb | mit | import os
import pandas as pd
import settings
import etl
%matplotlib inline
%load_ext watermark
%watermark -d -t -v -m -p pea,pandas
data = etl.Data()
data.load()
"""
Explanation: Explore Movie Dataset
End of explanation
"""
data.movie.columns
"""
Explanation: Available Columns
End of explanation
"""
data.movi... |
jphall663/GWU_data_mining | 02_analytical_data_prep/src/py_part_2_impute.ipynb | apache-2.0 | import pandas as pd # pandas for handling mixed data sets
import numpy as np # numpy for basic math and matrix operations
"""
Explanation: License
Copyright (C) 2017 J. Patrick Hall, jphall@gwu.edu
Permission is hereby granted, free of charge, to any person obtaining a copy of this softwar... |
xtr33me/deep-learning | gan_mnist/Intro_to_GANs_Solution.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
d-li14/CS231n-Assignments | assignment2/Dropout.ipynb | gpl-3.0 | # 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... |
robertchase/rhc | mock.ipynb | mit | import sys
sys.path.append('/opt/rhc')
import rhc.micro as micro
import rhc.async as async
import logging
logging.basicConfig(level=logging.DEBUG)
"""
Explanation: Defining mock connections
Start with some setup.
End of explanation
"""
p=micro.load_connection([
'CONNECTION placeholder http://jsonplaceholder.ty... |
woters/ds101 | 1-pandas.ipynb | mit | import pandas as pd
print("Pandas version: {}".format(pd.__version__))
# опции отображения
pd.options.display.max_rows = 6
pd.options.display.max_columns = 6
pd.options.display.width = 100
"""
Explanation: 1 - Введение в Pandas
Pandas это очень мощная библиотека с множеством полезных функций, ею можно пользаться мног... |
thehackerwithin/berkeley | code_examples/spring17_survey/survey.ipynb | bsd-3-clause | import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: The Hacker Within Spring 2017 survey
by R. Stuart Geiger, freely licensed CC-BY 4.0, MIT license
Importing and processing data
Importing libraries
End of explanation
"""
df = pd.read_csv("survey.tsv",sep="\t")
df[0:4]
"""
Explan... |
relopezbriega/mi-python-blog | content/notebooks/MyPy-Python-Tipado-estatico.ipynb | gpl-2.0 | def saludo(nombre):
return 'Hola {}'.format(nombre)
"""
Explanation: MyPy - Python y un sistema de tipado estático
Esta notebook fue creada originalmente como un blog post por Raúl E. López Briega en Mi blog sobre Python. El contenido esta bajo la licencia BSD.
Una de las razones por la que solemos amar a Python, ... |
benwaugh/NuffieldProject2016 | notebooks/InvariantMassCalcExample.ipynb | mit | from ROOT import TLorentzVector
"""
Explanation: How to calculate the invariant mass of a pair of particles
We will use the TLorentzVector class from ROOT, which has useful functions for converting coordinates, adding together four-momenta, and calculating the invariant mass.
End of explanation
"""
pt1 = 25.0
eta1 =... |
nmih/ssbio | docs/notebooks/SeqProp - Protein Sequence Properties.ipynb | mit | import sys
import logging
import os.path as op
# Import the SeqProp class
from ssbio.protein.sequence.seqprop import SeqProp
# Printing multiple outputs per cell
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
"""
Explanation: SeqProp - Protein Sequence Prop... |
davidrpugh/pyAM | examples/negative-assortative-matching.ipynb | mit | # define some workers skill
x, loc1, mu1, sigma1 = sym.var('x, loc1, mu1, sigma1')
skill_cdf = 0.5 + 0.5 * sym.erf((sym.log(x - loc1) - mu1) / sym.sqrt(2 * sigma1**2))
skill_params = {'loc1': 1e0, 'mu1': 0.0, 'sigma1': 1.0}
workers = pyam.Input(var=x,
cdf=skill_cdf,
params=ski... |
intel-analytics/analytics-zoo | apps/dogs-vs-cats/transfer-learning.ipynb | apache-2.0 | import re
from bigdl.nn.criterion import CrossEntropyCriterion
from pyspark.ml import Pipeline
from pyspark.sql.functions import col, udf
from pyspark.sql.types import DoubleType, StringType
from zoo.common.nncontext import *
from zoo.feature.image import *
from zoo.pipeline.api.keras.layers import Dense, Input, Flat... |
mzszym/oedes | examples/light-emitting/doping-dynamics-orgel12.ipynb | agpl-3.0 | %matplotlib inline
from matplotlib import colors
import matplotlib.pylab as plt
from oedes.fvm import mesh1d
from oedes import context,init_notebook,testing,models
import numpy as np
from oedes.functions import Aux2
init_notebook()
"""
Explanation: Transient simulation of organic light emitting electrochemical cell
... |
jburos/survivalstan | example-notebooks/Test pem_survival_model_timevarying with simulated data.ipynb | apache-2.0 | survivalstan.utils.print_stan_summary([testfit], pars='lp__')
survivalstan.utils.plot_stan_summary([testfit], pars='log_baseline')
"""
Explanation: superficial check of convergence
End of explanation
"""
survivalstan.utils.plot_coefs([testfit], element='baseline')
survivalstan.utils.plot_coefs([testfit])
"""
Expl... |
google/applied-machine-learning-intensive | content/00_prerequisites/01_intermediate_python/02-lambdas.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... |
specdb/specdb | docs/nb/Query_Meta.ipynb | gpl-3.0 | # imports
from astropy import units as u
from astropy.coordinates import SkyCoord
import specdb
from specdb.specdb import SpecDB
from specdb import specdb as spdb_spdb
from specdb.cat_utils import flags_to_groups
"""
Explanation: Query Meta data in database Groups [v1.1]
End of explanation
"""
db_file = specdb.__pa... |
JJINDAHOUSE/deep-learning | embeddings/Skip-Gram_word2vec.ipynb | mit | 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... |
Phylliade/poppy-inverse-kinematics | tutorials/Hand follow.ipynb | gpl-2.0 | import time
import numpy as np
from pypot.creatures import PoppyTorso
"""
Explanation: Hand Following example
In this notebook, you will use Pypot and an Inverse Kinematics toolbox to make Torso's hands follow each other.
Your Torso has two arms, and you can use simple methods to get and set the position of each hand.... |
kfollette/AST337-Fall2017 | Labs/Lab6/Unix_Programming_Refresher.ipynb | mit | ls
pwd
cd 2017oct04
"""
Explanation: Appendix 1: Optional Refresher on the Unix Environment
A1.1) A Quick Unix Overview
In Jupyter, many of the same Unix commands we use to navigate in the regular terminal can be used. (However, this is not true when we write standalone code outside Jupyter.) As a quick refresher,... |
jdhp-docs/python_notebooks | nb_misc/misc_read_ca_csv_fr.ipynb | mit | %matplotlib inline
#%matplotlib notebook
from IPython.display import display
import matplotlib
matplotlib.rcParams['figure.figsize'] = (9, 9)
import pandas as pd
import numpy as np
!head -n30 /Users/jdecock/Downloads/CA20170725_1744.CSV
#df = pd.read_csv("/Users/jdecock/Downloads/CA20170725_1744.CSV")
df = pd.rea... |
ceos-seo/data_cube_notebooks | notebooks/animation/3D/GA_Water_3D_Reservoir/GA_Water_3DReservoir.ipynb | apache-2.0 | import sys
import os
sys.path.append(os.environ.get('NOTEBOOK_ROOT'))
# Supress Warning
import warnings
warnings.filterwarnings('ignore')
import datacube
import glob
import rasterio
import scipy
import xarray as xr
import numpy as np
import pandas as pd
import geopandas as gpd
from skimage import filters
from skimage... |
hunterherrin/phys202-2015-work | assignments/assignment06/DisplayEx01.ipynb | mit | from IPython.display import Image
from IPython.display import HTML
from IPython.display import display
assert True # leave this to grade the import statements
"""
Explanation: Display Exercise 1
Imports
Put any needed imports needed to display rich output the following cell:
End of explanation
"""
Image(url='http:/... |
karst87/ml | 01_openlibs/tensorflow/01_examples/0_prerequisite/mnist_dataset_intro.ipynb | mit | # 导入MNIST
from tensorflow.examples.tutorials.mnist import input_data
# 加载数据
X_train = mnist.train.images
Y_train = mnist.train.labels
X_test = mnist.test.images
Y_test = mnist.test.labels
print(X_train.shape)
print(Y_train.shape)
print(X_test.shape)
print(Y_test.shape)
"""
Explanation: MNIST数据集介绍
大多数例子使用了手写数字的MNIST数... |
Radiomics/pyradiomics | notebooks/helloFeatureClass.ipynb | bsd-3-clause | from __future__ import print_function
import os
import collections
import SimpleITK as sitk
import numpy
import six
import radiomics
from radiomics import firstorder, glcm, imageoperations, shape, glrlm, glszm
"""
Explanation: Hello Feature Class example: using the feature classes to calculate features
This example sh... |
wanderer2/pymc3 | docs/source/notebooks/stochastic_volatility.ipynb | apache-2.0 | import numpy as np
import pymc3 as pm
from pymc3.distributions.timeseries import GaussianRandomWalk
from scipy import optimize
%pylab inline
"""
Explanation: Stochastic Volatility model
End of explanation
"""
n = 400
returns = np.genfromtxt("../data/SP500.csv")[-n:]
returns[:5]
plt.plot(returns)
"""
Explanation:... |
gtzan/mir_book | data_mining_random_variables.ipynb | cc0-1.0 | %matplotlib inline
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
class Random_Variable:
def __init__(self, name, values, probability_distribution):
self.name = name
self.values = values
self.probability_distribution = probability_distribution
... |
jsvine/spectra | docs/walkthrough.ipynb | mit | import spectra
"""
Explanation: Spectra Walkthrough
This notebook provides basic documentation of the spectra Python library, which aims to simplify the process of creating color scales and converting colors from one "color space" to another.
End of explanation
"""
from IPython.display import HTML
swatch_template =... |
tpin3694/tpin3694.github.io | machine-learning/bernoulli_naive_bayes_classifier.ipynb | mit | # Load libraries
import numpy as np
from sklearn.naive_bayes import BernoulliNB
"""
Explanation: Title: Bernoulli Naive Bayes Classifier
Slug: bernoulli_naive_bayes_classifier
Summary: How to train a Bernoulli naive bayes classifer in Scikit-Learn
Date: 2017-09-22 12:00
Category: Machine Learning
Tags: Naive Bayes... |
mikekestemont/lot2016 | Chapter 1 - Variables.ipynb | mit | print("Mike")
"""
Explanation: Chapter 1: Variables
-- A Python Course for the Humanities by Folgert Karsdorp and Maarten van Gompel, with modifications by Mike Kestemont and Lars Wieneke
First steps
Everyone can learn how to program and the best way to learn it is by doing it. This tutorial on the Python programming... |
dereneaton/ipyrad | tests/quickguide_API.ipynb | gpl-3.0 | import ipyrad as ip
"""
Explanation: Quick guide to the ipyrad API
Getting Started
Welcome! This tutorial will introduce you to the basics of working with ipyrad to assemble RADseq data.
Note: this tutorial was created in a Jupyter Notebook and assumes that you’re following-along in a notebook of your own. If you inst... |
rastala/mmlspark | notebooks/samples/101 - Adult Census Income Training.ipynb | mit | import numpy as np
import pandas as pd
import mmlspark
# help(mmlspark)
"""
Explanation: 101 - Training and Evaluating Classifiers with mmlspark
In this example, we try to predict incomes from the Adult Census dataset.
First, we import the packages (use help(mmlspark) to view contents),
End of explanation
"""
dataF... |
tpin3694/tpin3694.github.io | python/pandas_create_column_with_loop.ipynb | mit | import pandas as pd
import numpy as np
"""
Explanation: Title: Create A Pandas Column With A For Loop
Slug: pandas_create_column_with_loop
Summary: Create A Pandas Column With A For Loop
Date: 2016-05-01 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon
Preliminaries
End of explanation
"""
raw_data ... |
ucsd-ccbb/jupyter-genomics | notebooks/networkAnalysis/network_differential_expression_viz/network_differential_expression_viz.ipynb | mit | # import some useful packages
import numpy as np
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
% matplotlib inline
"""
Explanation: Visualize and analyze differential expression data in a network
In analysis of differential expression data, it is often useful to analyze properties of the ... |
GoogleCloudPlatform/professional-services | examples/kubeflow-fairing-example/Fairing_XGBoost.ipynb | apache-2.0 | import argparse
import logging
import joblib
import sys
import pandas as pd
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.impute import SimpleImputer
from xgboost import XGBClassifier
logging.basicConfig(format='%(message)s')
logging.getLogger().setLevel(lo... |
flyinactor91/Find-Me | FindMe.ipynb | mit | import cv2
import numpy as np
CASCADE = cv2.CascadeClassifier('findme/haar_cc_front_face.xml')
def find_faces(img: np.ndarray, sf=1.16, mn=5) -> np.array([[int]]):
"""Returns a list of bounding boxes for every face found in an image"""
return CASCADE.detectMultiScale(
cv2.cvtColor(img, cv2.COLOR_RGB2G... |
ThunderShiviah/code_guild | wk3/notebooks/wk3.0.ipynb | mit | def turn_clockwise(direction):
compass = {"N":"E" , "E": "S", "S":"W", "W":"N"}
return compass[direction]
assert turn_clockwise("N") == "E"
assert turn_clockwise("W") == "N"
turn_clockwise("N")
"""
Explanation: wk3.0
Warm-up
The four compass points can be abbreviated by single-letter strings as “N”, “E”, “S... |
mdeff/ntds_2017 | projects/reports/course_suggester/Weighting Metrics and Graph Diffusion.ipynb | mit | %matplotlib inline
import os
import pandas as pd
import numpy as np
import pickle
from pygsp import graphs, filters, plotting
from scipy.spatial import distance
import matplotlib.pyplot as plt
import itertools
from tqdm import tqdm
plt.rcParams['figure.figsize'] = (17, 5)
plotting.BACKEND = 'matplotlib'
do_prints = Fa... |
DS-100/sp17-materials | sp17/hw/hw2/hw2_solution.ipynb | gpl-3.0 | import math
import numpy as np
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
!pip install -U okpy
from client.api.notebook import Notebook
ok = Notebook('hw2.ok')
"""
Explanation: Homework 2: Language in the 2016 Presidential Election
Popular figures often have help managing their media presenc... |
TurkuNLP/BINF_Programming | lectures/week-5-sequence-alignment.ipynb | gpl-2.0 | from Bio import pairwise2
## load the module
## globalxx
## use global alignment function which only score 1
## for each match (0 for both penalty and mismatch)
alignments = pairwise2.align.globalxx("ACCGT", "ACG")
## perform global alignments (xx) between two sequences.
for alignment in alignments:
## Each ... |
ledeprogram/algorithms | class6/donow/ronga_paul_DoNow_6.ipynb | gpl-3.0 | import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import statsmodels.formula.api as smf
"""
Explanation: 1. Import the necessary packages to read in the data, plot, and create a linear regression model
End of explanation
"""
df = pd.read_csv('../data/hanford.csv')
df.head()
"""
Explanation: 2. ... |
ForestClaw/forestclaw | applications/clawpack/transport/2d/sonic/swirl.ipynb | bsd-2-clause | !swirlcons --user:example=2 --user:rp-solver=4
"""
Explanation: Advection (conservative form)
Scalar advection problem in conservative form with variable velocity field.
There are four Riemann solvers that can be tried out here, all described in
LeVeque (Cambridge Press, 2002)
rp-solver=1 : Q-star approach in whic... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch8-Problem_8-10to11.ipynb | unlicense | %pylab notebook
%precision %.4g
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 8
Problem 8-10 to Problem 8-11
End of explanation
"""
P_rated = 30 # [hp]
Il_rated = 110 # [A]
Vt = 240 # [V]
Nf = 2700
n_0 = 1800 # [r/min]
Nse = 14
Ra = 0.19 # [Ohm]
Rf = 75... |
statsmodels/statsmodels.github.io | v0.12.1/examples/notebooks/generated/tsa_arma_1.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
from statsmodels.graphics.tsaplots import plot_predict
from statsmodels.tsa.arima_process import arma_generate_sample
from statsmodels.tsa.arima.model import ARIMA
np.random.seed(12345)
"""
Explanation: Autoregressive Moving Average (ARMA): Artificial data
E... |
jmhsi/justin_tinker | data_science/lendingclub_bak/dataprep_and_modeling/0.2.0_RF_regressor_no_weighting.ipynb | apache-2.0 | platform = 'lendingclub'
store = pd.HDFStore(
'/Users/justinhsi/justin_tinkering/data_science/lendingclub/{0}_store.h5'.
format(platform),
append=True)
loan_info = store['train_filtered_columns']
columns = loan_info.columns.values
# checking dtypes to see which columns need one hotting, and which need nul... |
SlipknotTN/udacity-deeplearning-nanodegree | reinforcement/Q-learning-cart.ipynb | mit | import gym
import tensorflow as tf
import numpy as np
"""
Explanation: Deep Q-learning
In this notebook, we'll build a neural network that can learn to play games through reinforcement learning. More specifically, we'll use Q-learning to train an agent to play a game called Cart-Pole. In this game, a freely swinging p... |
hadrianpaulo/project_deathstar | analytics/Classification_cycle_1.ipynb | mit | df_train = pd.DataFrame()
# MNCHN
df_train['body'] = df_mnchn['body'].append(df_mnchn['Final Keywords'])
df_train['label'] = 1
# Adolescent
df_train = df_train.append(pd.DataFrame({
'body': df_adolescent['body'].append(df_adolescent['Final Keywords']),
'label': 2
}))
# Geriatrics
df_train = df_train.append(pd.D... |
projectmesa/mesa-examples | examples/PD_Grid/Demographic Prisoner's Dilemma Activation Schedule.ipynb | apache-2.0 | from pd_grid import PD_Model
import random
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec
%matplotlib inline
"""
Explanation: Demographic Prisoner's Dilemma
The Demographic Prisoner's Dilemma is a family of variants on the classic two-player Prisoner's Dilemma, first developed by Joshu... |
ES-DOC/esdoc-jupyterhub | notebooks/hammoz-consortium/cmip6/models/sandbox-2/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'hammoz-consortium', 'sandbox-2', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: HAMMOZ-CONSORTIUM
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Top... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session11/Day1/InvestigatingDetectors.ipynb | mit | from astropy.io import fits
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['figure.dpi'] = 120
"""
Explanation: Investigating Detectors
Version 0.1
Understanding the behavior of the CCDs in a camera requires digging deep into calibration exposures. That is where you can unco... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session11/Day3/GalaxyPhotometryAndShapes.ipynb | mit | # Load the packages we will use
import numpy as np
import astropy.io.fits as pf
import astropy.coordinates as co
from matplotlib import pyplot as pl
import scipy.fft as fft
%matplotlib inline
"""
Explanation: Practice with galaxy photometry and shape measurement
To accompany galaxy-measurement lecture from the LSSTC D... |
liganega/Gongsu-DataSci | ref_materials/exams/2015/midterm.ipynb | gpl-3.0 | def interval_point(a, b, x):
if a < b:
return (b-a)*x + a
else:
return a - (a-b)*x
"""
Explanation: 2015년 2학기 공업수학 중간고사 시험지
이름:
학번:
시험지 작성 요령
예제코드를 보면서 문제의 내용을 이해하도록 노력한다.
문제별로 '해야 할 일' 에서 요구하는 방향으로 변경된 코드의 빈자리를 채우거나 답을 한다.
문제 1
세 개의 숫자를 입력받는 함수 interval_point는 아래 기능을 구현한다.
숫자 a와 b는 구간의 처음과 ... |
Rantanen/igraph | examples/ipython.ipynb | mit | import igraph
igraph.draw([(1, 2), (2, 3), (3, 4), (4, 1), (4, 5), (5, 2)])
"""
Explanation: igraph in the IPython notebook
I wrote igraph to visualize graphs in 3D purely out of curiosity. I couldn't find any 3D force-directed graph libraries when I wrote it, so this happened.
It can be used with the notebook to inte... |
ucsd-ccbb/mali-dual-crispr-pipeline | dual_crispr/distributed_files/notebooks/Dual CRISPR 5-Count Plots.ipynb | mit | g_dataset_name = "Notebook5Test"
g_fastq_counts_run_prefix = "TestSet5"
g_fastq_counts_dir = '~/dual_crispr/test_data/test_set_5'
g_collapsed_counts_run_prefix = ""
g_collapsed_counts_dir = ""
g_combined_counts_run_prefix = ""
g_combined_counts_dir = ""
g_plots_run_prefix = ""
g_plots_dir = '~/dual_crispr/test_outputs/... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/production_ml/labs/samples/contrib/mnist/04_Reusable_and_Pre-build_Components_as_Pipeline.ipynb | apache-2.0 | import kfp
import kfp.gcp as gcp
import kfp.dsl as dsl
import kfp.compiler as compiler
import kfp.components as comp
import datetime
import kubernetes as k8s
# Required Parameters
PROJECT_ID='<ADD GCP PROJECT HERE>'
GCS_BUCKET='gs://<ADD STORAGE LOCATION HERE>'
"""
Explanation: Composing a pipeline from reusable, pr... |
spacy-io/thinc | examples/02_transformers_tagger_bert.ipynb | mit | !pip install "thinc>=8.0.0a0" transformers torch "ml_datasets>=0.2.0a0" "tqdm>=4.41"
"""
Explanation: Training a part-of-speech tagger with transformers (BERT)
This example shows how to use Thinc and Hugging Face's transformers library to implement and train a part-of-speech tagger on the Universal Dependencies AnCora... |
pradau/udacity | Data_Analyst_ND_Project0.ipynb | bsd-2-clause | import pandas as pd
# pandas is a software library for data manipulation and analysis
# We commonly use shorter nicknames for certain packages. Pandas is often abbreviated to pd.
# hit shift + enter to run this cell or block of code
path = r'/Users/pradau/Dropbox/temp/Downloads/chopstick-effectiveness.csv'
# Change t... |
data-cube/agdc-v2-examples | notebooks/02_loading_data.ipynb | apache-2.0 | import datacube
dc = datacube.Datacube(app='load-data-example')
"""
Explanation: Loading data from the datacube
This notebook will briefly discuss how to load data from the datacube.
Importing the datacube
To start with, we'll import the datacube module and load an instance of the datacube and call our application nam... |
ireapps/pycar | completed/read_csv_notebook_complete.ipynb | mit | from urllib.request import urlretrieve
import csv
"""
Explanation: Read a CSV
We're going to use built-in Python modules - programs really - to download a csv file from the Internet and save it locally.
CSV stands for comma-separated values. It's a common file format a file format that resembles a spreadsheet or datab... |
Yangqing/caffe2 | caffe2/python/tutorials/Control_Ops.ipynb | apache-2.0 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import workspace
from caffe2.python.core import Plan, to_execution_step, Net
from caffe2.python.net_builder import ops, NetBuilder
"""
Explanation: Co... |
BrandonSmithJ/tensorflow-double-DQN | Double-DQN/tensorflow-deepq/notebooks/.ipynb_checkpoints/karpathy_game-checkpoint.ipynb | mit | g.plot_reward(smoothing=100)
"""
Explanation: Average Reward over time
End of explanation
"""
g.__class__ = KarpathyGame
np.set_printoptions(formatter={'float': (lambda x: '%.2f' % (x,))})
x = g.observe()
new_shape = (x[:-2].shape[0]//g.eye_observation_size, g.eye_observation_size)
print(x[:-2].reshape(new_shape))
p... |
treasure-data/pandas-td | doc/magic.ipynb | apache-2.0 | %load_ext pandas_td.ipython
"""
Explanation: Magic functions
You can enable magic functions by loading pandas_td.ipython:
End of explanation
"""
c = get_config()
c.InteractiveShellApp.extensions = [
'pandas_td.ipython',
]
"""
Explanation: It can be loaded automatically by the following configuration in "~/.ipy... |
ComputationalModeling/spring-2017-danielak | past-semesters/spring_2016/day-by-day/day02-modeling-cold-spread/Day 2 pre-class assignment.ipynb | agpl-3.0 | # The command below this comment imports the functionality that we need to display
# YouTube videos in a Jupyter Notebook. You need to run this cell before you
# run ANY of the YouTube videos.
from IPython.display import YouTubeVideo
"""
Explanation: Day 2 pre-class assignment
Goals for today's pre-class assignment... |
bradhowes/keystrokecountdown | src/articles/poisson/index.ipynb | mit | N = 10000.0
T = 2.0
lmbda = N / T / 60 / 60
lmbda
"""
Explanation: Introduction
We wish to simulate a stochastic process where there are N users of our application that we contend will use our app within a 2 hour time period. To perform the simulation, we would like to have our users attempt to use the application at ... |
TheOregonian/articles | air_quality/air_quality.ipynb | mit | df_list = pd.read_html(
'https://en.wikipedia.org/wiki/Air_quality_index', header=0)
aqi_df = df_list[14].drop(0)
aqi_df[['min','max']] = aqi_df['AQI'].str.split('-', 1, expand=True)
aqi_df.columns
aqi_df.rename(columns={'O3 (ppb).1': 'O3 (ppb) 1 hour', 'AQI.1': 'Category'}, inplace=True)
# The final value for... |
UCSBarchlab/PyRTL | ipynb-examples/example6-memory.ipynb | bsd-3-clause | import random
import pyrtl
from pyrtl import *
pyrtl.reset_working_block()
"""
Explanation: Example 6: Memories in PyRTL
One important part of many circuits is the ability to have data in
locations that are persistent over clock cycles. In previous examples,
we have shown the register wirevector, which is great for ... |
tbarrongh/cosc-learning-labs | src/notebook/03_interface_startup.ipynb | apache-2.0 | help('learning_lab.03_interface_startup')
"""
Explanation: COSC Learning Lab
03_interface_startup.py
Related Scripts:
* 03_interface_shutdown.py
* 03_interface_configuration.py
Table of Contents
Table of Contents
Documentation
Implementation
Execution
HTTP
Documentation
End of explanation
"""
from importlib import... |
jepegit/cellpy | examples/jupyter notebooks/cellpy_batch_processing.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import cellpy
from cellpy import prms
from cellpy import prmreader
from cellpy.utils import batch
%matplotlib inline
## Uncomment this and run for checking your cellpy parameters.
# prmreader.info()
"""
Explanation: Notebook for cellpy batch processing
You can fill... |
albahnsen/PracticalMachineLearningClass | notebooks/02-IntroPython_Numpy_Scypy_Pandas.ipynb | mit | import sys
print('Python version:', sys.version)
import IPython
print('IPython:', IPython.__version__)
import numpy
print('numpy:', numpy.__version__)
import scipy
print('scipy:', scipy.__version__)
import matplotlib
print('matplotlib:', matplotlib.__version__)
import pandas
print('pandas:', pandas.__version__)
... |
hanleilei/note | training/submit/PythonExercises1stAnd2nd.ipynb | cc0-1.0 | planet = "Earth"
diameter = 12742
"""
Explanation: Python入门 第一周和第二周的练习
练习
回答下列粗体文字所描述的问题，如果需要，使用任何合适的方法，以掌握技能，完成自己想要的程序为目标，不用太在意实现的过程。
7 的四次方是多少？
分割以下字符串
s = "Hi there Sam!"
到一个列表中
提供了一下两个变量
planet = "Earth"
diameter = 12742
使用format()函数输出一下字符串
The diameter of Earth is 12742 kilometers.
End of explanation
... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/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... |
QuantCrimAtLeeds/PredictCode | examples/Case Study Chicago South Side/SEPP2.ipynb | artistic-2.0 | %matplotlib inline
from common import *
datadir = os.path.join("//media", "disk", "Data")
#datadir = os.path.join("..", "..", "..", "..", "..", "Data")
import open_cp.logger
open_cp.logger.log_to_true_stdout()
south_side, points = load_data(datadir)
points.time_range
masked_grid = grid_for_south_side()
masked_grid2 ... |
UWSEDS/LectureNotes | Fall2018/02_Procedural_Python/Lecture-Python-And-Data.ipynb | bsd-2-clause | # Integer arithematic
1 + 1
# Integer division version floating point division
print (6 // 4, 6/ 4)
"""
Explanation: Software Engineering for Data Scientists
Manipulating Data with Python
DATA 515 A
Today's Objectives
0. Cloning LectureNotes
1. Opening & Navigating the Jupyter Notebook
2. Data type basics
3. Loading ... |
PublicHealthEngland/pygom | notebooks/Stochasticity.ipynb | gpl-2.0 | import pygom
import pkg_resources
print('PyGOM version %s' %pkg_resources.get_distribution('pygom').version)
"""
Explanation: Stochastic simulation
Examples taken from https://arxiv.org/pdf/1803.06934.pdf (see page 11 for stochastic simulations).
Examples are performed on an SIR model.
$\frac{dS}{dt} = -\beta S I $
$\... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/miroc-es2h/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'miroc-es2h', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: MIROC
Source ID: MIROC-ES2H
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Tu... |
hail-is/hail | datasets/notebooks/1kg_NYGC_30x_datasets.ipynb | mit | ht_samples = hl.import_table(
"gs://hail-datasets-tmp/1000_Genomes_NYGC_30x/1000_Genomes_NYGC_30x_samples_ped_population.txt.bgz",
delimiter="\s+",
impute=True
)
ht_samples = ht_samples.annotate(
FatherID = hl.if_else(ht_samples.FatherID == "0",
hl.missing(hl.tstr),
... |
Diyago/Machine-Learning-scripts | time series regression/DL aproach for timeseries/pytorch_timeseries_RNN.ipynb | apache-2.0 | import torch
from torch import nn
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.figure(figsize=(8,5))
# how many time steps/data pts are in one batch of data
seq_length = 20
# generate evenly spaced data pts
time_steps = np.linspace(0, np.pi, seq_length + 1)
data = np.sin(time_steps)
data... |
roatienza/Deep-Learning-Experiments | versions/2022/tools/python/np_demo.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Demonstration of numpy for data synthesis and manipulation
numpy is a numerical computing library in Python. It supports linear algebra operations that are useful in deep learning. In particular, numpy is useful for data loading, preparation, synthesi... |
pyemma/deeplearning | assignment2/FullyConnectedNets.ipynb | gpl-3.0 | # As usual, a bit of setup
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.solver import Solver
%matplotlib inline
... |
davisincubator/digblood | notebooks/jfa-1.0-initial_data_exploration.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
data_dir = '../data/raw/'
data_filename = 'blood_train.csv'
df_blood = pd.read_csv(data_dir+data_filename)
df_blood.head()
"""
Explanation: Predicting Blood Donations: Initial Data Exploration
To do:
- Import data
-... |
NeuroDataDesign/pan-synapse | pipeline_1/background/Sort.ipynb | apache-2.0 | def newRandomCentroids(n, l, u):
diff = u-l
return [[random()*diff+l for _ in range(3)] for _ in range(n)]
newRandomCentroids(10, 10, 100)
"""
Explanation: Goal
The goal of this notebook is to explore better methods for the final l2 centorid match during registration in the pipeline.
Generate Data
End of expl... |
carthach/essentia | src/examples/tutorial/example_discontinuitydetector.ipynb | agpl-3.0 | import essentia.standard as es
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import Audio
from essentia import array as esarr
plt.rcParams["figure.figsize"] =(12,9)
def compute(x, frame_size=1024, hop_size=512, **kwargs):
discontinuityDetector = es.DiscontinuityDetector(frameSize=frame_s... |
littlewizardLI/Udacity-ML-nanodegrees | Project1-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... |
jmhsi/justin_tinker | data_science/courses/deeplearning1/fastai-course-1-pytorch/lesson5-pytorch.ipynb | apache-2.0 | from keras.datasets import imdb
idx = imdb.get_word_index()
"""
Explanation: Setup data
We're going to look at the IMDB dataset, which contains movie reviews from IMDB, along with their sentiment. Keras comes with some helpers for this dataset.
End of explanation
"""
idx_arr = sorted(idx, key=idx.get)
idx_arr[:10]
... |
kimkipyo/dss_git_kkp | Python 복습/14일차.금_pandas + SQL_2/14일차_1T_os, shutil 모듈을 이용한 파일,폴더 관리하기 (1) - 폴더 생성 및 제거.ipynb | mit | import os
#os 모듈을 통해서
#운영체제 레벨(서버는 ex.우분투)에서 다루는 파일 폴더 생성하고 삭제하기가 가능
#기존에는 ("../../~~") 이런 식으로 경로를 직접 입력 했으나
os.listdir()
#현재 폴더 안에 있는 파일들을 리스트로 뽑는 것
os.listdir("../")
for csv_file in os.listdir("../"):
pass
"""
Explanation: 1T_os, shutil 모듈을 이용한 파일,폴더 관리하기 (1) - 폴더 생성 및 제거
영화별 매출 - Revenue per Film 이거 어려워. 이거... |
ContextLab/quail | docs/tutorial/egg.ipynb | mit | import quail
%matplotlib inline
"""
Explanation: The Egg data object
This tutorial will go over the basics of the Egg data object, the essential quail data structure that contains all the data you need to run analyses and plot the results. An egg is made up of two primary pieces of data:
pres data - stimuli/feature... |
kmunve/APS | Predict_aval_problem_combined.ipynb | mit | import pandas as pd
import numpy as np
import json
import sklearn
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.simplefilter('ignore')
print('Pandas:\t', pd.__version__)
print('Numpy:\t', np.__version__)
print('Scikit Learn:\t', sklearn.__version__)
print('Matplotlib:\t... |
bigdata-i523/hid335 | experiment/Python_SKL_NeuralNetworkClassifier.ipynb | gpl-3.0 | display(mglearn.plots.plot_logistic_regression_graph())
"""
Explanation: Introduction to Machine Learning
Andreas Mueller and Sarah Guido (2017) O'Reilly
Ch. 2 Supervised Learning
Neural Networks (Deep Learning)
MLP feedforward neural network
Generalization of linear models for classification and regression
Predictio... |
mrcslws/nupic.research | projects/archive/dynamic_sparse/notebooks/ExperimentAnalysis-SigOptTest.ipynb | agpl-3.0 | %load_ext autoreload
%autoreload 2
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import tabulate
import pprint
import click
import numpy as np
import pandas as pd
from ray.tune.commands import *
from nupic.research.frameworks.dynamic... |
dogrdon/native_ad_data | analysis/native_ad_analysis.ipynb | gpl-3.0 | import pandas as pd
from datetime import datetime
import dateutil
import matplotlib.pyplot as plt
from IPython.core.display import display, HTML
import re
from urllib.parse import urlparse
import json
"""
Explanation: Performing Clean-up and Analysis on Native Ad Data Scraped "From Around the Web"
End of explanation
"... |
dvkonst/ml_mipt | task_5/hw1_Modules.ipynb | gpl-3.0 | class Module(object):
def __init__ (self):
self.output = None
self.gradInput = None
self.training = True
"""
Basically, you can think of a module as of a something (black box)
which can process `input` data and produce `ouput` data.
This is like applying a function which is ... |
bbglab/adventofcode | 2015/ferran/day12.ipynb | mit | with open('inputs/input12.txt') as f_input:
s = next(f_input).rstrip()
import re
def sum_numbers(s):
p = re.compile('[-]?[\d]+')
numbers = list(map(int, p.findall(s)))
return sum(numbers)
sum_numbers(s)
"""
Explanation: Day 12: JSAbacusFramework.io
Day 12.1
End of explanation
"""
def transform_red... |
rayjustinhuang/DataAnalysisandMachineLearning | Logistic Regression.ipynb | mit | # Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn import metrics
"""
Explanation: Predicting Grad School Admiss... |
jgoppert/iekf_analysis | Temperature Calibration.ipynb | bsd-3-clause | import sympy
sympy.init_printing()
Theta = sympy.Matrix(sympy.symbols(
'theta_0:3_0:4')).reshape(3,4)
def Y(n):
return sympy.Matrix(sympy.symbols(
'G_x:z_0:{:d}'.format(n+1))).T.reshape(3, n+1)
def C(n):
return sympy.ones(n+1, 1)
def T(n):
return sympy.Matrix(sympy.symbols('T_0:{:d}'.format(... |
jeffcarter-github/MachineLearningLibrary | MachineLearningLibrary/NeuralNetworks/CNN_MNIST_Keras_Tensorflow.ipynb | mit | from __future__ import print_function
import matplotlib.pyplot as plt
%matplotlib notebook
"""
Explanation: This notebook walks through training a CNN Model on the MNIST data using Keras and Tensorflow...
Load Data and Reshape
Build Model
Train / Test
Build interactive OpenCV GUI for playing
import ploting library..... |
ioggstream/python-course | connexion-101/notebooks/05-reusing-and-bundling.ipynb | agpl-3.0 | # Exercise: creating a bundle from a $ref file
#
# You can resolve dependencies and create a bundle file with
!pip install openapi_resolver
# Exercise: create a bundle from the previous file with
!python -m openapi_resolver /code/notebooks/oas3/ex-05-01-bundle.yaml
"""
Explanation: Reusing and bundling
Our strategy... |
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