repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
hannorein/rebound | ipython_examples/ScreenshotsAndMoviesWithWebGL.ipynb | gpl-3.0 | import rebound
sim = rebound.Simulation()
sim.add(m=1) # add a star
for i in range(10):
sim.add(m=1e-3,a=0.4+0.1*i,inc=0.03*i,omega=5.*i) # Jupiter mass planets on close orbits
sim.move_to_com() # Move to the centre of mass frame
w = sim.getWidget()
w
"""
Explanation: Screenshots and Movies with WebGL
One can use... |
NirantK/deep-learning-practice | 04-InitSeqtoSeq/dlnd_language_translation.ipynb | apache-2.0 | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
yy/dviz-course | m09-estimation/m09-lab.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import altair as alt
import pandas as pd
import scipy.stats as ss
import statsmodels
"""
Explanation: Module 9: Estimation
End of explanation
"""
import vega_datasets
movies = vega_datasets.data.movies()
movies.head()
vega_datasets.__version_... |
LFPy/LFPy | examples/LFPy-example-09.ipynb | gpl-3.0 | # importing some modules, setting some matplotlib values for pl.plot.
import LFPy
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size' : 12,
'figure.facecolor' : '1',
'figure.subplot.wspace' : 0.5,
'figure.subplot.hspace' : 0... |
armgilles/presentation | EPSI/I4/Business Intellgence/EP4/premier_pas_algo_titanic.ipynb | mit | # Importer les lib python
import pandas as pd
"""
Explanation: On va débuter step by step
Inscription et récupération des données :
Aller sur le site Kaggle et inscrivez-vous
Ensuite aller sur le contest du Titanic
Télécharger les données train.csv et test.csv dans l'onglet data
Mettez ces données dans un répertoire ... |
uber/pyro | tutorial/source/ekf.ipynb | apache-2.0 | import os
import math
import torch
import pyro
import pyro.distributions as dist
from pyro.infer.autoguide import AutoDelta
from pyro.optim import Adam
from pyro.infer import SVI, Trace_ELBO, config_enumerate
from pyro.contrib.tracking.extended_kalman_filter import EKFState
from pyro.contrib.tracking.distributions imp... |
fastai/fastai | nbs/examples/migrating_lightning.ipynb | apache-2.0 | from migrating_lightning import *
from fastai.vision.all import *
"""
Explanation: Tutorial - Migrating from Lightning
Incrementally adding fastai goodness to your Lightning training
We're going to use the MNIST training code from Lightning's 'Quick Start' (as at August 2020), converted to a module.
Note: The sour... |
civisanalytics/civis-python | examples/PythonAPIClientDemo.ipynb | bsd-3-clause | print(f"Using Civis Python API Client version {civis.__version__}.")
"""
Explanation: The Civis Python API Client
Stephen Hoover, Lead Data Scientist<br>
August 2017
Civis Platform provides you with a Data Science API which gives you direct access to Civis Platform's cloud-based infrastructure, data science tools, an... |
hhain/sdap17 | notebooks/henrik_ueb02/Mustererkennung_in_Funkmessdaten.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pprint as pp
hdfs = pd.HDFStore("../../data/raw/henrik/TestMessungen_NEU.hdf")
hdfs.keys
"""
Explanation: Mustererkennung in Funkmessdaten
Aufgabe 1: Laden der Datenbank in Jupyter Notebook
End of e... |
statkclee/ThinkStats2 | code/chap07soln-kor.ipynb | gpl-3.0 | %matplotlib inline
from __future__ import print_function
import math
import first
import chap07soln
import thinkstats2
import thinkplot
import numpy as np
"""
Explanation: 통계적 사고 (2판) 연습문제 (thinkstats2.com, think-stat.xwmooc.org)<br>
Allen Downey / 이광춘(xwMOOC)
End of explanation
"""
def ScatterPlot(ages, weights, a... |
googledatalab/notebooks | tutorials/Stackdriver Monitoring/Group metrics.ipynb | apache-2.0 | # set_datalab_project_id('my-project-id')
"""
Explanation: Look at metric data based on Stackdriver groups
In this tutorial, we see how to look at the group structure in a project, and how to use groups to filter and aggregate metric data. For this tutorial to execute completely, you need to have:
* a Stackdriver acco... |
csaladenes/csaladenes.github.io | present/mcc2/PythonDataScienceHandbook/05.04-Feature-Engineering.ipynb | mit | data = [
{'price': 850000, 'rooms': 4, 'neighborhood': 'Queen Anne'},
{'price': 700000, 'rooms': 3, 'neighborhood': 'Fremont'},
{'price': 650000, 'rooms': 3, 'neighborhood': 'Wallingford'},
{'price': 600000, 'rooms': 2, 'neighborhood': 'Fremont'}
]
"""
Explanation: <!--BOOK_INFORMATION-->
<img align="l... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/pipelines/automl_tabular_classification_beans.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[full] $USER_FLAG
"""
Explanation: Vertex AI Pipelines: AutoML Tabular pipelines using google-cloud-pipeline-components
<tab... |
tianchuanting/Udacity | P2/P2.ipynb | mit | %pylab inline
import pandas as pd
import matplotlib.pylab as plt
import seaborn as sns
import statsmodels.api as sm
# read the data and inspect
titanic = pd.read_csv('titanic-data.csv')
print titanic.info()
titanic.head()
# drop those columns we are not interested in.
titanic.drop(["Name", "Ticket", "Cabin", "Embarke... |
hparik11/Deep-Learning-Nanodegree-Foundation-Repository | sentiment-rnn/.ipynb_checkpoints/Sentiment_RNN_Solution-checkpoint.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... |
scoaste/showcase | machine-learning/regression/week-4-ridge-regression-assignment-2-completed.ipynb | mit | import graphlab
"""
Explanation: Regression Week 4: Ridge Regression (gradient descent)
In this notebook, you will implement ridge regression via gradient descent. You will:
* Convert an SFrame into a Numpy array
* Write a Numpy function to compute the derivative of the regression weights with respect to a single feat... |
OliverEvans96/rte_matrix | gen_matrices/Diagonal Dominance.ipynb | gpl-3.0 | # Check to see whether sum of scattering coefs. = 2
# nth = number of theta values
# kk = integer index num. of theta to diff other angles wrt.
def check_scat_sum(nth,kk):
th_un, ww_un = leg.leggauss(nth)
th_vals = pi * (th_un + 1)
weights = pi * ww_un
th = th_vals[kk]
thp = th_vals[th_vals!=th... |
feststelltaste/software-analytics | courses/python_pandas_basics/session0/SchnupperkursDataScienceCDR.ipynb | gpl-3.0 | import pandas as pd
"""
Explanation: Einführung
Willkommen bei "Komm' wir spielen NSA!"!
Dieses Notebook ist eine kleine Einführung in die Data Science Welt mit Python, dem Notebook-System Jupyter, dem Datenanalyse-Framework Pandas, der Visualisierungsbibliothek "matplotlib" und der Machine-Learning-Bibliothek "scikit... |
machinelearningdeveloper/lc101-kc | December 05, 2016/Covered in class.ipynb | unlicense | ## List access
fruit = ['cranberries', 'apples']
print(fruit[0])
## As storage
string_key_to_int_value = {'one': 1, 'two': 2}
print(string_key_to_int_value)
int_key_to_string_value = {1: 'one', 2: 'two'}
print(int_key_to_string_value)
fruit = {
'apples': 10,
'oranges': 25,
'pears': 15
}
print(fruit)
... |
therealAJ/python-sandbox | data-science/learning/ud1/DataScience/KMeans.ipynb | gpl-3.0 | from numpy import random, array
#Create fake income/age clusters for N people in k clusters
def createClusteredData(N, k):
random.seed(10)
pointsPerCluster = float(N)/k
X = []
for i in range (k):
incomeCentroid = random.uniform(20000.0, 200000.0)
ageCentroid = random.uniform(20.0, 70.0)... |
ioos/notebooks_demos | notebooks/2017-03-21-ERDDAP_IOOS_Sensor_Map.ipynb | mit | import requests
try:
from urllib.parse import urlencode
except ImportError:
from urllib import urlencode
def encode_erddap(urlbase, fname, columns, params):
"""
urlbase: the base string for the endpoint
(e.g.: https://erddap.axiomdatascience.com/erddap/tabledap).
fname: the data sour... |
zingale/pyro2 | presentations/pyro_intro.ipynb | bsd-3-clause | import mesh.patch as patch
import mesh.boundary as bnd
import numpy as np
g = patch.Grid2d(16, 16, ng=2)
print(g)
bc = bnd.BC(xlb="periodic", xrb="periodic", ylb="reflect", yrb="outflow")
print(bc)
d = patch.CellCenterData2d(g)
d.register_var("a", bc)
d.create()
print(d)
"""
Explanation: Introduction to Pyro
Micha... |
djpine/linfit | linfit.ipynb | lgpl-2.1 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec # for unequal plot boxes
from linfit import linfit
"""
Explanation: Least squares fitting using linfit.py
This notebook demonstrates the function linfit, which I propose adding to the SciPy library.
linfit is designed to be a ... |
kylemede/DS-ML-sandbox | notebooks/.ipynb_checkpoints/Basic_DS_Udemy_notes-checkpoint.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(10)
dossageEffectiveness = abs(np.random.normal(5.0, 1.5, 1000))
repurchaseRate = (dossageEffectiveness + np.random.normal(0, 0.1, 1000)) * 3
repurchaseRate/=np.max(repurchaseRate)
plt.scatter(dossageEffectiveness, repurchaseRate)
plt... |
sdpython/ensae_teaching_cs | _doc/notebooks/data/data_irep.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
"""
Explanation: IREP: registre des émissions polluantes
Ce jeu de données est disponible sous le nombre Registre des émissions polluantes.
End of explanation
"""
from ensae_teaching_cs.data import load_irep
load_irep()
import panda... |
QinetiQ-datascience/Docker-Data-Science | WooWeb-Presentation/Workspace/Widgets/Using Interact.ipynb | mit | from __future__ import print_function
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
"""
Explanation: Using Interact
The interact function (ipywidgets.interact) automatically creates user interface (UI) controls for exploring code and data interactively. It is the eas... |
bjshaw/phys202-2015-work | assignments/assignment10/ODEsEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
"""
Explanation: Ordinary Differential Equations Exercise 1
Imports
End of explanation
"""
def lorentz_derivs(yvec, t, sigma, rho, beta):
"""Compute the the de... |
claudiuskerth/PhDthesis | Data_analysis/SNP-indel-calling/dadi/02_dadi_1D_two_epoch.ipynb | mit | from ipyparallel import Client
cl = Client()
cl.ids
# clear the namespace in engines
cl.clear()
%px %who
%%px --local
# run whole cell on all engines a well as in the local IPython session
import numpy as np
import sys
sys.path.insert(0, '/home/claudius/Downloads/dadi')
import dadi
"""
Explanation: Table of ... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive/03_tensorflow/c_dataset.ipynb | apache-2.0 | from google.cloud import bigquery
import tensorflow as tf
import numpy as np
import shutil
print(tf.__version__)
"""
Explanation: <h1> 2c. Loading large datasets progressively with the tf.data.Dataset </h1>
In this notebook, we continue reading the same small dataset, but refactor our ML pipeline in two small, but si... |
ewulczyn/talk_page_abuse | src/analysis/Ellery Characterizing Attackers.ipynb | apache-2.0 | t = 0.5
df_first_attack = df_diffs['2015'].query('pred_recipient_score>=%s' % t).sort('rev_timestamp')\
.assign(timestamp = lambda x: x.rev_timestamp)\
.groupby(['user_text'], as_index=False).first()[['user_text', 'timestamp']]
df_counts = df_diffs['2015'].merge(df_first_a... |
samuelsinayoko/kaggle-housing-prices | ols/data_exploration_numerical_features.ipynb | mit | target = pd.read_csv('../data/train_target.csv')
target.describe()
"""
Explanation: Sale price distribution
First step is to look at the target sale price for the training data set, i.e. the column we're trying to predict.
End of explanation
"""
target = target / 1000
sns.distplot(target);
plt.title('SalePrice')
... |
andrew-lundgren/detchar | Notebooks/gaussian_and_lines.ipynb | gpl-3.0 | sample_rate = 4096
nyquist = sample_rate/2
time_length_seconds = 512
"""
Explanation: We're going to simulate data discretely sampled in time. We need to set a sample rate in Hz. The highest resolvable frequency, the Nyquist frequency, is half the sample rate. We also need to decide how long the simulated data record ... |
shareactorIO/pipeline | source.ml/jupyterhub.ml/notebooks/zz_old/TensorFlow/GPU/BasicGPU.ipynb | apache-2.0 | !nvidia-smi
import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
logdir = '/root/pipeline/logs/tensorflow'
import numpy as np
import matplotlib.pyplot as plt
import datetime
from tensorflow.python.framework import ops
from tensorflow.python.platform import gfile
from IPython... |
pligor/predicting-future-product-prices | dfa/notebook/.ipynb_checkpoints/dfa_simulations-checkpoint.ipynb | agpl-3.0 | import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import scipy.signal as ss
import scipy.stats as st
plt.style.use('ggplot')
%matplotlib inline
"""
Explanation: DFA robustness simulations
Dominik Krzemiński
End of explanation
"""
def calc_rms(x, scale):
"""
Root Mean Square in windows wit... |
mommermi/Introduction-to-Python-for-Scientists | notebooks/.ipynb_checkpoints/Numpy_20161014-checkpoint.ipynb | mit | import time
import math
a = range(10000000)
def func(a):
return 1e-6*(4*a**2.) #7+1./(a+3)**3.4-(23*a)**4.2)-20
# measure how long it takes in seconds
start_time = time.time()
new_a = []
for val in a:
new_a.append(func(val))
print time.time()-start_time, "seconds"
"""
Explanation: Numpy
(see http://ww... |
tombstone/models | research/object_detection/colab_tutorials/inference_tf2_colab.ipynb | apache-2.0 | !pip install -U --pre tensorflow=="2.2.0"
import os
import pathlib
# Clone the tensorflow models repository if it doesn't already exist
if "models" in pathlib.Path.cwd().parts:
while "models" in pathlib.Path.cwd().parts:
os.chdir('..')
elif not pathlib.Path('models').exists():
!git clone --depth 1 https://git... |
halimacc/CS231n-assignments | assignment1/knn.ipynb | unlicense | # Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
from __future__ import print_function
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplotlib inlin... |
TeamHG-Memex/eli5 | notebooks/LIME and synthetic data.ipynb | mit | import numpy as np
import pandas as pd
from matplotlib import cm
from IPython.display import HTML, display_html, display
from sklearn.datasets import make_circles
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression... |
google/floq-client | samples/notebooks/Floq_Client_Colab_Tutorial.ipynb | apache-2.0 | #@title Copyright 2021 Floq authors, All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... |
jrg365/gpytorch | examples/04_Variational_and_Approximate_GPs/Approximate_GP_Objective_Functions.ipynb | mit | # Define some training data
train_x = torch.linspace(0, 1, 100)
train_y = torch.cos(train_x * 2 * math.pi) + torch.randn(100).mul(train_x.pow(3) * 1.)
fig, ax = plt.subplots(1, 1, figsize=(5, 3))
ax.scatter(train_x, train_y, c='k', marker='.', label="Data")
ax.set(xlabel="x", ylabel="y")
"""
Explanation: Different ob... |
jakevdp/PracticalLombScargle | figures/LINEAR_Example.ipynb | bsd-3-clause | import pandas as pd
def get_LINEAR_lightcurve(lcid):
from astroML.datasets import fetch_LINEAR_sample
LINEAR_sample = fetch_LINEAR_sample()
data = pd.DataFrame(LINEAR_sample[lcid],
columns=['t', 'mag', 'magerr'])
data.to_csv('LINEAR_{0}.csv'.format(lcid), index=False)
# Uncom... |
maxis42/ML-DA-Coursera-Yandex-MIPT | 4 Stats for data analysis/Homework/9 test correlation analysis/Test Correlation analysis.ipynb | mit | from __future__ import division
import numpy as np
import pandas as pd
from scipy import stats
from statsmodels.stats.weightstats import *
from statsmodels.stats.proportion import proportion_confint
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.core.interactiveshell import In... |
robertoalotufo/ia898 | master/corrdemo.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import sys,os
ia898path = os.path.abspath('../../')
if ia898path not in sys.path:
sys.path.append(ia898path)
import ia898.src as ia
"""
Explanation: Demo Template Matching using Normalized Cross Correlation
In th... |
desihub/desisim | doc/nb/bgs-reference-spectra.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import desitarget.mock.io as mockio
import multiprocessing
nproc = multiprocessing.cpu_count() // 2
%matplotlib inline
sns.set(style='ticks', font_scale=1.4, palette='Set2')
col = sns.color_palette()
"""
Explanation: BGS Reference ... |
MissouriDSA/twitter-locale | twitter/twitter_4.ipynb | mit | # BE SURE TO RUN THIS CELL BEFORE ANY OF THE OTHER CELLS
import psycopg2
import pandas as pd
from skbio.diversity.alpha import shannon
# query database
statement = """
SELECT *
FROM twitter.job
WHERE description LIKE '%New York City%';
"""
try:
connect_str = "dbname='twitter' user='dsa_ro_user' host='dbase.dsa.... |
ES-DOC/esdoc-jupyterhub | notebooks/hammoz-consortium/cmip6/models/sandbox-1/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'hammoz-consortium', 'sandbox-1', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: HAMMOZ-CONSORTIUM
Source ID: SANDBOX-1
Topic: Ocean
Sub-Topics: Timeste... |
dhgarrette/cnn | pyexamples/tutorials/RNNs.ipynb | apache-2.0 | model = Model()
NUM_LAYERS=2
INPUT_DIM=50
HIDDEN_DIM=10
builder = LSTMBuilder(NUM_LAYERS, INPUT_DIM, HIDDEN_DIM, model)
# or:
# builder = SimpleRNNBuilder(NUM_LAYERS, INPUT_DIM, HIDDEN_DIM, model)
"""
Explanation: An LSTM/RNN overview:
An (1-layer) RNN can be thought of as a sequence of cells, $h_1,...,h_k$, where $h_... |
empiricalstateofmind/blog | content/notebooks/parallel.ipynb | mit | from IPython.parallel import Client
%pylab inline
"""
Explanation: After speaking to a number of academics involved with Monte Carlo simulations, I realised many of them resort to creating bash scripts to
run a number of python instances of simulation before writing another script to piece the data back together. It ... |
zomansud/coursera | ml-regression/week-1/PhillyCrime.ipynb | mit | import graphlab
"""
Explanation: Fire up graphlab create
End of explanation
"""
sales = graphlab.SFrame.read_csv('Philadelphia_Crime_Rate_noNA.csv/')
sales
"""
Explanation: Load some house value vs. crime rate data
Dataset is from Philadelphia, PA and includes average house sales price in a number of neighborhoods... |
ajhenrikson/phys202-2015-work | assignments/midterm/AlgorithmsEx03.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact
"""
Explanation: Algorithms Exercise 3
Imports
End of explanation
"""
def char_probs(s):
"""Find the probabilities of the unique characters in the string s.
Parameters
----------
... |
whitead/numerical_stats | unit_4/hw_2016/homework_4_key.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
#Question 1.1
x = np.linspace(0,3, 1000)
exp1 = 0.1 * np.exp(-1.0 * x)
exp2 = 1 * np.exp(-1.5 * x)
exp3 = 10 * np.exp(-2 * x)
plt.plot(x, exp1, label='$\lambda = 1.0$')
plt.plot(x, exp2, label='$\lambda = 1.5$')
plt.plot(x, exp3, label='$\lambda = ... |
bashtage/statsmodels | examples/notebooks/plots_boxplots.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
"""
Explanation: Box Plots
The following illustrates some options for the boxplot in statsmodels. These include violin_plot and bean_plot.
End of explanation
"""
data = sm.datasets.anes96.load_pandas()
party_ID = np.a... |
fastai/course-v3 | nbs/dl2/devise.ipynb | apache-2.0 | path = Config().data_path()/'imagenet'
"""
Explanation: Data
End of explanation
"""
src = (ImageList.from_folder(path)
.split_by_folder()
.label_from_folder())
data = src.presize(size=224).databunch(bs=256).normalize(imagenet_stats)
"""
Explanation: Inputs: precomputed activations
We will build... |
kradams/MIDS-W261-2015-Adams | week01/quiz/MIDS-W261-2015-QUIZ-Week01-Adams.ipynb | mit | %%writefile mapper.py
#!/usr/bin/python
import sys
import re
count = 0
WORD_RE = re.compile(r"[\w']+")
filename = sys.argv[2]
findword = sys.argv[1]
with open (filename, "r") as myfile:
#Please insert your code
for line in myfile.readlines():
words = WORD_RE.findall(line.lower())
for w in words:... |
gboeing/urban-data-science | modules/09-spatial-analysis/lecture.ipynb | mit | import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pysal as ps
import seaborn as sns
from scipy.stats import stats
np.random.seed(0)
"""
Explanation: Spatial Analysis
"Everything is related to everything else, but near things are more related than distant things." -W... |
mediagit2016/workcamp-maschinelles-lernen-grundlagen | 19-04-10-ml-workcamp/credit_data_work-10.ipynb | gpl-3.0 | # hier Ihren Code einfügen und aufüühren
"""
Explanation: Beispiel Credit Data - Aufgabe - Klassifikation:
AI, Machine Learning & Data Science
Author list: Ramon Rank
Die in KNIME durchgeführte Klassifikation der Kreditdaten soll mit Python umgesetzt werden.
Die Zellen für die Klassifikation sind bereits vorbereitet... |
nikitaswinnen/model-for-predicting-rapid-response-team-events | model/model/modeling_base.ipynb | apache-2.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import scipy as sp
# import datetime as datetime
import cPickle as pickle
%matplotlib inline
plt.style.use('ggplot')
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split, KFold... |
watsonyanghx/CS231n | assignment2/FullyConnectedNets.ipynb | mit | # 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
... |
Jim00000/Numerical-Analysis | 3_Interpolation.ipynb | unlicense | # Import modules
import sys
import math
import numpy as np
from matplotlib import pyplot as plt
from scipy import linalg
"""
Explanation: ★ Interpolation ★
End of explanation
"""
def newton_divided_diff(X, Y, n):
V = {}
for j in range(n):
V[j, 0] = Y[j]
for i in range(1,n):
for j in ... |
fggp/ctcsound | cookbook/06-csoundmagics.ipynb | lgpl-2.1 | %load_ext csoundmagics
"""
Explanation: Magic Commands and Syntax highlighting for Csound
Once the csoundmagic extension is installed (see the 05-installingCsoundmagics notebook), one can use the Csound magics and functions by loading the extension:
End of explanation
"""
%%csd crossfm
<CsoundSynthesizer>
<CsOption... |
bgroveben/python3_machine_learning_projects | learn_kaggle/deep_learning/coding_in_tensorflow_and_keras.ipynb | mit | from os.path import join
hot_dog_image_dir = 'inputs/seefood/train/hot_dog'
hot_dog_paths = [join(hot_dog_image_dir, filename) for filename in
['1000288.jpg',
'127117.jpg']]
not_hot_dog_image_dir = 'inputs/seefood/train/not_hot_dog'
not_hot_dog_paths = [join(not_hot_dog_image_dir, fil... |
bbfamily/abu | abupy_lecture/18-自定义裁判决策交易(ABU量化使用文档).ipynb | gpl-3.0 | # 基础库导入
from __future__ import print_function
from __future__ import division
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import os
import sys
# 使用insert 0即只使用github，避免交叉使用了pip安装的abupy，导致的版本不一致问题
sys.path.insert(0, os.path.abspath('../'))
import abupy
# 使用沙盒数据，目的是和书中一样的... |
hadim/public_notebooks | Analysis/MSD_Analyzer/notebook.ipynb | mit | # Some classic Python modules import
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (15, 10)
import pandas as pd
import numpy as np
from scipy import optimize
"""
Explanation: How to analyze particle motion with MSD (Mean Square Displacement)
Note that this notebook is largely i... |
amitkaps/hackermath | Module_2a_Basic_Stat_Metrics.ipynb | mit | #Import the required libraries
import numpy as np
import pandas as pd
from datetime import datetime as dt
from scipy import stats
"""
Explanation: Basic Metrics
When we think about summarizing data, what are the metrics that we look at?
In this notebook, we will look at the car dataset
To read how the data was acquire... |
junhwanjang/DataSchool | Lecture/14. 선형 회귀 분석/3) 레버리지와 아웃라이어.ipynb | mit | from sklearn.datasets import make_regression
X0, y, coef = make_regression(n_samples=100, n_features=1, noise=20, coef=True, random_state=1)
# add high-leverage points
X0 = np.vstack([X0, np.array([[4],[3]])])
X = sm.add_constant(X0)
y = np.hstack([y, [300, 150]])
plt.scatter(X0, y)
plt.show()
model = sm.OLS(pd.Data... |
dafrie/lstm-load-forecasting | notebooks/6_all_features.ipynb | mit | # Model category name used throughout the subsequent analysis
model_cat_id = "06"
# Which features from the dataset should be loaded:
# ['all', 'actual', 'entsoe', 'weather_t', 'weather_i', 'holiday', 'weekday', 'hour', 'month']
features = ['all']
# LSTM Layer configuration
# ========================
# Stateful True ... |
CivicTechTO/ttc_subway_times | doc/filtering_observed_arrivals.ipynb | gpl-3.0 | import datetime
from psycopg2 import connect
import configparser
import pandas as pd
import pandas.io.sql as pandasql
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
import matplotlib.ticker as ticker
con = connect(user='rad', database='ttc')
CONFIG = configparser.ConfigParser(interpola... |
seg/2016-ml-contest | esaTeam/esa_Submission01c_VALIDATION.ipynb | apache-2.0 | # Import
from __future__ import division
get_ipython().magic(u'matplotlib inline')
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams['figure.figsize'] = (20.0, 10.0)
inline_rc = dict(mpl.rcParams)
from classification_utilities import make_facies_log_plot
import pandas as pd
import numpy as np
impo... |
Kaggle/learntools | notebooks/python/raw/tut_3.ipynb | apache-2.0 | x = True
print(x)
print(type(x))
"""
Explanation: Booleans
Python has a type of variable called bool. It has two possible values: True and False.
End of explanation
"""
def can_run_for_president(age):
"""Can someone of the given age run for president in the US?"""
# The US Constitution says you must be at le... |
GoogleCloudPlatform/ai-platform-samples | notebooks/samples/explanations/ai-explanations-tabnet-algorithm.ipynb | apache-2.0 | PROJECT_ID = "[<your-project-id>]"
"""
Explanation: TabNet: Attentive Interpretable Tabular Learning
<table align="left">
<td>
<a href="https://colab.research.google.com/github/GoogleCloudPlatform/ml-on-gcp/blob/master/tutorials/explanations/ai-explanations-tabnet-algorithm.ipynb">
<img src="https://cloud.... |
jrbourbeau/cr-composition | notebooks/anisotropy_random_trials.ipynb | mit | %load_ext watermark
%watermark -u -d -v -p numpy,matplotlib,scipy,pandas,sklearn,mlxtend
"""
Explanation: <a id='top'> </a>
End of explanation
"""
%matplotlib inline
from __future__ import division, print_function
from collections import defaultdict
import os
import numpy as np
import pandas as pd
import matplotlib.... |
josePhoenix/wfirst-tools | notebooks/Pandeia-WFIRST Imaging.ipynb | bsd-3-clause | %matplotlib inline
%config InlineBackend.figure_format = 'svg'
import sys
import os
import numpy as np
import matplotlib
from matplotlib import style
style.use('ggplot') # see http://matplotlib.org/users/style_sheets.html
# for info on matplotlib styles
matplotlib.rcParams['axes.grid'] = False
mat... |
quantopian/research_public | notebooks/lectures/Fundamental_Factor_Models/notebook.ipynb | apache-2.0 | import pandas as pd
import numpy as np
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data import Fundamentals
from quantopian.pipeline.factors import CustomFactor, Returns, Latest
from quantopian.pipeline.classifiers import Classifier
from quantopian.pipeline.filters import QTradableStocksUS
from qu... |
jasontlam/snorkel | tutorials/workshop/Workshop_4_Discriminative_Model_Training.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
import os
import numpy as np
# Connect to the database backend and initalize a Snorkel session
from lib.init import *
from snorkel.annotations import load_marginals
from snorkel.models import candidate_subclass
Spouse = candidate_subclass('Spouse', ['person1', 'pe... |
ahmadpgh/deepSimDEF | data/sequence_homology_data_prepration.ipynb | mit | import pandas as pd
import numpy as np
import os
import requests
import easydict
import linecache
import pprint
import random
pp = pprint.PrettyPrinter(indent=4)
"""
Explanation: Sequence Homology Data Prepration<a id='top'></a>
Sections:<br>
0) Description<br>
1) BLAST<br>
2) Importing Modules and Packages<br>
3) Co... |
opalytics/opalytics-ticdat | examples/expert_section/notebooks/pandas_and_ticdat_2.ipynb | bsd-2-clause | from diet import input_schema
dat = input_schema.sql.create_tic_dat_from_sql("diet_sample_data.sql")
dat.nutrition_quantities
pan_dat = input_schema.copy_to_pandas(dat)
pan_dat.nutrition_quantities
{pan_dat.nutrition_quantities.__class__, pan_dat.categories.__class__, pan_dat.foods.__class__}
"""
Explanation: pand... |
rashikaranpuria/Machine-Learning-Specialization | Regression/Assignment_two/week-2-multiple-regression-assignment-2-blank.ipynb | mit | import graphlab
"""
Explanation: Regression Week 2: Multiple Regression (gradient descent)
In the first notebook we explored multiple regression using graphlab create. Now we will use graphlab along with numpy to solve for the regression weights with gradient descent.
In this notebook we will cover estimating multiple... |
GoogleCloudPlatform/tensorflow-without-a-phd | tensorflow-rnn-tutorial/old-school-tensorflow/tutorial/02_RNN_generator_temperatures_playground.ipynb | apache-2.0 | import math
import sys
import time
import numpy as np
import utils_batching
import utils_args
import tensorflow as tf
from tensorflow.python.lib.io import file_io as gfile
print("Tensorflow version: " + tf.__version__)
from matplotlib import pyplot as plt
import utils_prettystyle
import utils_display
"""
Explanation:... |
wikistat/Intro-Python | Cal3-PythonGraphes.ipynb | mit | %matplotlib inline
"""
Explanation: <center>
<a href="http://www.insa-toulouse.fr/" ><img src="http://www.math.univ-toulouse.fr/~besse/Wikistat/Images/logo-insa.jpg" style="float:left; max-width: 120px; display: inline" alt="INSA"/></a>
<a href="http://wikistat.fr/" ><img src="http://www.math.univ-toulouse.fr/~besse/... |
JaggedParadigm/pairplotr | .ipynb_checkpoints/pairplotr_demo-checkpoint.ipynb | apache-2.0 | %matplotlib inline
import sys
import pairplotr.pairplotr as ppr
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Pairplotr introduction
Here I introduce pairplotr, a tool I developed to do pairwise plots of features, including mixtures of numerical and categorical ones, starti... |
ES-DOC/esdoc-jupyterhub | notebooks/cccr-iitm/cmip6/models/sandbox-2/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccr-iitm', 'sandbox-2', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: CCCR-IITM
Source ID: SANDBOX-2
Topic: Ocean
Sub-Topics: Timestepping Framework,... |
StudyExchange/Udacity | MachineLearning(Advanced)/p5_image_classification/image_classification.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
class DLProgress(tqdm):
last_block = 0
def hoo... |
boffi/boffi.github.io | dati_2020/01/half_power_estimation.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from jupyterthemes import jtplot ; jtplot.style()
np.random.seed(1969-7-20)
"""
Explanation: Half Power Estimation of $\zeta$
End of explanation
"""
N = 51
b = np.linspace(0.5, 1.5, N) + (np.random.random(N)-0.5)/100
z = 0.035
D = 1/np.sqrt((1-b*... |
calebmadrigal/insurance-plan-analysis | fireeye-insurance-individual-2015.ipynb | mit | %matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
class Plan:
def __init__(self): pass
# Plan 1 = Cigna HDHP/HSA
p1 = Plan()
p1.family_deductible = 2000.00 # Same deductible for both family and individual
p1.individual_deductible = 2000.00
p1.family_oopmax = 3000.00 ... |
Dioptas/pymatgen | examples/Basic functionality.ipynb | mit | import pymatgen as mg
"""
Explanation: Introduction
This notebook demostrates the core functionality of pymatgen, including the core objects representing Elements, Species, Lattices, and Structures.
By convention, we import pymatgen as mg.
End of explanation
"""
si = mg.Element("Si")
print("Atomic mass of Si is {}"... |
pmorissette/bt | examples/Strategy_Combination.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import ffn
import bt
"""
Explanation: This notebook creates a parent strategy(combined) with 2 child strategies(Equal Weight, Inv Vol).
Alternatively, it creates the 2 child strategies, runs the backtest, combines the results, and creates a paren... |
subutai/htmresearch | projects/l2_pooling/notebooks/similar_objects.ipynb | agpl-3.0 | import itertools
import random
from htmresearch.algorithms.column_pooler import ColumnPooler
INPUT_SIZE = 10000
def createFeatureLocationPool(size=10):
duplicateFound = False
for _ in xrange(5):
candidateFeatureLocations = [frozenset(random.sample(xrange(INPUT_SIZE), 40))
... |
rusucosmin/courses | ml/ex01/npprimer.ipynb | mit | # Useful starting lines
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
%load_ext autoreload
%autoreload 2
"""
Explanation: Welcome to the jupyter notebook! To run any cell, press Shift+Enter or Ctrl+Enter.
IMPORTANT : Please have a look at Help->User Interface Tour and Help->Keyboard Short... |
oscar6echo/ezhc | demo_ezhc_themes_and_global_options.ipynb | mit | th = hc.Theme()
th.info()
"""
Explanation: Themes
Directly available in ezhc
End of explanation
"""
hc_global = hc.GlobalOptions({'lang': {'decimalPoint': '.', 'thousandsSep': ','}})
hc_global.inject(verbose=True)
"""
Explanation: Global Options
Can be accessed through the GlobalOptions class
See the Highcharts d... |
gaufung/PythonStandardLibrary | DataCompression/tarfile.ipynb | mit | import tarfile
for filename in ['zlib_server.py', 'example.tar',
'tarfile.ipynb', 'notthere.tar']:
try:
print('{:>15} {}'.format(filename, tarfile.is_tarfile(
filename)))
except IOError as err:
print('{:>15} {}'.format(filename, err))
"""
Explanation: The tarfile... |
steinam/teacher | jup_notebooks/data-science-ipython-notebooks-master/commands/linux.ipynb | mit | !df -h
"""
Explanation: This notebook was prepared by Donne Martin. Source and license info is on GitHub.
Linux Commands
Disk Usage
Splitting Files
Grep, Sed
Compression
Curl
View Running Processes
Terminal Syntax Highlighting
Vim
Disk Usage
Display human-readable (-h) free disk space:
End of explanation
"""
!du -... |
dipanjank/ml | tensorflow/simple_linear_regression_tf_v2.ipynb | gpl-3.0 | import tensorflow as tf
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
"""
Explanation: Simple Linear Regression in Tensorflow
End of explanation
"""
x = np.random.rand(100, 3)
w = np.array([1.0, 2.0, 3.0])
y = x @ w
y.shape
"""
Explanation: First we make some test data for 100 observa... |
AWPorter/aima-python | learning.ipynb | mit | from learning import *
"""
Explanation: Learning
This notebook serves as supporting material for topics covered in Chapter 18 - Learning from Examples , Chapter 19 - Knowledge in Learning, Chapter 20 - Learning Probabilistic Models from the book Artificial Intelligence: A Modern Approach. This notebook uses implementa... |
pligor/predicting-future-product-prices | ARIMA/.ipynb_checkpoints/ShampooSales-checkpoint.ipynb | agpl-3.0 | import pandas as pd
from pandas import read_csv
from pandas import datetime
from matplotlib import pyplot as plt
from pandas.plotting import autocorrelation_plot
from statsmodels.tsa.arima_model import ARIMA
from pandas import DataFrame
from sklearn.metrics import mean_squared_error
%matplotlib inline
def date_parser... |
mbeyeler/opencv-machine-learning | notebooks/10.03-Using-Random-Forests-for-Face-Recognition.ipynb | mit | from sklearn.datasets import fetch_olivetti_faces
dataset = fetch_olivetti_faces()
X = dataset.data
y = dataset.target
"""
Explanation: <!--BOOK_INFORMATION-->
<a href="https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv" target="_blank"><img align="left" src="data/cover.jpg" style="wi... |
jrrembert/cybernetic-organism | dato/recommendations/Song recommender.ipynb | gpl-2.0 | import graphlab
"""
Explanation: Building a song recommender
Fire up GraphLab Create
End of explanation
"""
song_data = graphlab.SFrame('song_data.gl/')
"""
Explanation: Load music data
End of explanation
"""
song_data.head()
"""
Explanation: Explore data
Music data shows how many times a user listened to a song... |
quantopian/research_public | notebooks/lectures/Futures_Trading_Considerations/notebook.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from quantopian.research.experimental import continuous_future, history
"""
Explanation: Futures Trading Considerations
by Maxwell Margenot and Delaney Mackenzie
Part of the Quantopian Lecture Series:
www.quantopian.com/lectures
github.com/quantop... |
scikit-rf/scikit-rf | doc/source/tutorials/Circuit.ipynb | bsd-3-clause | import skrf as rf
rf.stylely()
Z_0 = 50
Z_L = 75
theta = 0
# the necessary Frequency description
freq = rf.Frequency(start=1, stop=2, unit='GHz', npoints=3)
# The combination of a transmission line + a load can be created
# using the convenience delay_load method
# important: all the Network must have the parameter ... |
jbcayrou/scapy | doc/notebooks/Scapy in 15 minutes.ipynb | gpl-2.0 | send(IP(dst="1.2.3.4")/TCP(dport=502, options=[("MSS", 0)]))
"""
Explanation: Scapy in 15 minutes (or longer)
Guillaume Valadon & Pierre Lalet
Scapy is a powerful Python-based interactive packet manipulation program and library. It can be used to forge or decode packets for a wide number of protocols, send them on the... |
ES-DOC/esdoc-jupyterhub | notebooks/cccr-iitm/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', 'cccr-iitm', 'sandbox-2', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: CCCR-IITM
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topics: Tracers.
P... |
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