repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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iptrha/ml_techbier | machine_learning_loesung.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import pylab
pylab.rcParams['figure.figsize'] = (13.0, 8.0)
%matplotlib inline
import pickle
import sklearn
import sklearn.linear_model
import sklearn.preprocessing
import sklearn.gaussian_process
import sklearn.ensemble
# to make the code is compatible with python... |
anugrah-saxena/pycroscopy | jupyter_notebooks/01_Tutorial_Translation.ipynb | mit | # downloading packages:
!pip install wget pycroscopy
# Ensure python 3 compatibility:
from __future__ import division, print_function, absolute_import, unicode_literals
# In case some of these packages are not installed, install them
#!pip install -U os wget numpy h5py matplotlib pycroscopy
# The package for accessi... |
sdpython/cpyquickhelper | _doc/notebooks/cbenchmark_vector.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
"""
Explanation: Use of PYBIND11_MAKE_OPAQUE
pybind11 automatically converts std::vector into python list. That's convenient but not necessarily efficient depending on how it is used after that. PYBIND11_MAKE_OPAQUE is used to create a... |
musketeer191/job_analytics | .ipynb_checkpoints/user_apply_job-p1-checkpoint.ipynb | gpl-3.0 | n_application, n_applicant, n_job, n_job_title = apps.shape[0], apps['uid'].nunique(), apps['job_id'].nunique(), apps['job_title'].nunique()
n_company = apps['reg_no_uen_ep'].nunique()
stats = pd.DataFrame({'n_application': n_application, 'n_applicant': n_applicant,
'n_job': n_job, 'n_job_title'... |
dereneaton/ipyrad | tests/cookbook-tetrad.ipynb | gpl-3.0 | ## conda install ipyrad -c ipyrad
## conda install toytree -c eaton-lab
import ipyrad as ip
import ipyrad.analysis as ipa
import ipyparallel as ipp
import toytree
"""
Explanation: (📗) ipyrad Cookbook: tetrad species tree inference
The ipyrad.analysis package includes a module and command-line tool called tetr... |
undercertainty/ou_nlp | 12_distributed_tensorflow.ipynb | apache-2.0 | # To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
def reset_graph(seed=42):
tf.reset_default_graph()
tf.set_random_seed(seed)
np.random.seed(seed)
# To... |
pastas/pasta | examples/notebooks/02_fix_parameters.ipynb | mit | import pandas as pd
import pastas as ps
import matplotlib.pyplot as plt
ps.set_log_level("ERROR")
ps.show_versions()
"""
Explanation: Time Series Analysis with Pastas
Developed by Mark Bakker, TU Delft
Required files to run this notebook (all available from the data subdirectory):
Head files: head_nb1.csv, B58C06980... |
savkov/CRFSuiteTagger | example/example.ipynb | gpl-3.0 | import gzip, os, wget
def extract(fp):
f = gzip.open(fp, 'rb')
with open(fp[:-3], 'w') as fh:
fh.write(f.read())
f.close()
try:
os.makedirs('data')
except OSError:
pass
try:
os.makedirs('thesauri')
except OSError:
pass
train_url = 'http://www.cnts.ua.ac.be/conll2000/chunking/trai... |
watsonyanghx/CS231n | assignment2/BatchNormalization.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
... |
tigerstat46/Deep-Learning | StateFarmDeep.ipynb | mit | #importing necessary packages
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import tflearn
import tensorflow as tf
from PIL import Image
%matplotlib inline
#for writing text files
import glob
import os
import random
#reading images from a text file
from tflearn.data_utils import imag... |
mne-tools/mne-tools.github.io | 0.20/_downloads/1458a29737cd3695e2bfc763012d8259/plot_report.ipynb | bsd-3-clause | import os
import mne
"""
Explanation: Getting started with mne.Report
This tutorial covers making interactive HTML summaries with
:class:mne.Report.
:depth: 2
As usual we'll start by importing the modules we need and loading some
example data <sample-dataset>:
End of explanation
"""
path = mne.datasets.samp... |
poldrack/fmri-analysis-vm | analysis/connectivity/PPI.ipynb | mit | import os,sys
import numpy
%matplotlib inline
import matplotlib.pyplot as plt
sys.path.insert(0,'../')
from utils.mkdesign import create_design_singlecondition
from nipy.modalities.fmri.hemodynamic_models import spm_hrf,compute_regressor
from utils.make_data import make_continuous_data
from statsmodels.tsa.arima_proces... |
ChadFulton/statsmodels | examples/notebooks/statespace_cycles.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from pandas_datareader.data import DataReader
endog = DataReader('UNRATE', 'fred', start='1954-01-01')
"""
Explanation: Trends and cycles in unemployment
Here we consider three methods for separatin... |
SylvainCorlay/bqplot | examples/Marks/Pyplot/Pie.ipynb | apache-2.0 | data = np.random.rand(3)
fig = plt.figure(animation_duration=1000)
pie = plt.pie(data, display_labels='outside', labels=list(string.ascii_uppercase))
fig
"""
Explanation: Basic Pie Chart
End of explanation
"""
n = np.random.randint(1, 10)
pie.sizes = np.random.rand(n)
"""
Explanation: Update Data
End of explanatio... |
ceos-seo/data_cube_notebooks | notebooks/DEM/SRTM_Product_Showcase_v2.ipynb | apache-2.0 | import sys
import os
sys.path.append(os.environ.get('NOTEBOOK_ROOT'))
%matplotlib inline
import datacube
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
from scipy.ndimage import convolve
"""
Explanation: SRTM Product Showcase
Products used:
srtm_google (original source)
Dataset from 11-Feb-... |
edosedgar/xs-pkg | deep_learning/hw3/homework03_part3_gan_basic.ipynb | gpl-2.0 | """
Please, implement everything in one notebook, using if statements to switch between the tasks
"""
TASK = 1 # 2, 3, 4, 5
"""
Explanation: The visualization used for this homework is based on Alexandr Verinov's code.
Generative models
In this homework we will try several criterions for learning an implicit mo... |
datacommonsorg/api-python | notebooks/analyzing_income_distribution.ipynb | apache-2.0 | # Install datacommons
!pip install datacommons_pandas --upgrade --quiet
"""
Explanation: <a href="https://colab.research.google.com/github/datacommonsorg/api-python/blob/master/Case_Study_Analyzing_Income_Distribution.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open... |
Olsthoorn/TransientGroundwaterFlow | readthedocs/Course2016_jupyter/docs/source/WellInLeakyAquifer.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pylab as plt
#%%writefile wells.py # remove first # to write this as file "wells.py" to disk
import pdb
import numpy as np
from scipy.special import expi
def W(u): return -expi(-u) # Theis
def wh(u, rho):
"""Returns Hantush's well function values
Note: works only for... |
abulbasar/machine-learning | SparkML - 04 Text_Analysis.ipynb | apache-2.0 | spark.sparkContext.uiWebUrl
"""
Explanation: Problem Statement: IMDB Comment Sentiment Classifier
Dataset: For this exercise we will use a dataset hosted at http://ai.stanford.edu/~amaas/data/sentiment/
Problem Statement:
This is a dataset for binary sentiment classification containing substantially more data than pr... |
d-meiser/cold-atoms | examples/Ions in Penning trap.ipynb | gpl-3.0 | import coldatoms
import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
from IPython import display
"""
Explanation: Ions in a Penning trap
Penning traps are an important experimental tool for confining charged particles. They are used to collect elementary particles produced in high energy experiment... |
VVard0g/ThreatHunter-Playbook | docs/notebooks/campaigns/apt29Evals.ipynb | mit | # Importing Libraries
from bokeh.io import show
from bokeh.plotting import figure
from bokeh.models import ColumnDataSource, LabelSet, HoverTool
from bokeh.transform import dodge
import pandas as pd
# You need to run this code at the beginning in order to show visualization using Jupyter Notebooks
from bokeh.io import... |
phoebe-project/phoebe2-docs | 2.1/tutorials/requiv_crit_contact.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: Critical Radii: Contact Systems
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 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
"""
%matplot... |
tensorflow/docs-l10n | site/ko/tutorials/load_data/text.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... |
zakandrewking/cobrapy | documentation_builder/loopless.ipynb | lgpl-2.1 | %matplotlib inline
import plot_helper
import cobra.test
from cobra import Reaction, Metabolite, Model
from cobra.flux_analysis.loopless import add_loopless, loopless_solution
from cobra.flux_analysis import pfba
"""
Explanation: Loopless FBA
The goal of this procedure is identification of a thermodynamically consiste... |
tesera/pygypsy | notebooks/#32-address-testing-findings/#32-isolated-profiling-1.ipynb | mit | %%bash
grep --colour -nr append ../gypsy/*.py
"""
Explanation: Initial attemps at profiling had very confusing results; possibly because of module loading and i/o
Here, gypsy will be run and profiled on one plot, with no module loading/io recorded in profiling
Characterize what is happening
In several places, we appen... |
qgoisnard/Exercice-update | 01-LinearBeam.ipynb | mit | import numpy as np
def xR(s):
return s
def yR(s):
return 0.*s
s = np.linspace(0.,1.,20)
xR(s)
"""
Explanation: https://www.explainxkcd.com/wiki/images/f/fd/python.png
Beam Model
Kinematics
Consider a beam of length L with a straigth reference configuration and denote by $s$ the arc-length in this configurati... |
GoogleCloudPlatform/mlops-on-gcp | on_demand/tfx-caip/lab-02-tfx-pipeline/solutions/lab-02.ipynb | apache-2.0 | import yaml
# Set `PATH` to include the directory containing TFX CLI and skaffold.
PATH=%env PATH
%env PATH=/home/jupyter/.local/bin:{PATH}
"""
Explanation: Continuous training with TFX and Google Cloud AI Platform
Learning Objectives
Use the TFX CLI to build a TFX pipeline.
Deploy a new TFX pipeline version with tu... |
dtamayo/reboundx | ipython_examples/Radiation_Forces_Circumplanetary_Dust.ipynb | gpl-3.0 | import rebound
import reboundx
import numpy as np
sim = rebound.Simulation()
sim.G = 6.674e-11 # SI units
sim.dt = 1.e4 # Initial timestep in sec.
sim.N_active = 2 # Make it so dust particles don't interact with one another gravitationally
sim.add(m=1.99e30, hash="Sun") # add Sun with mass in kg
sim.add(m=5.68e26, a=1.... |
thunder-project/thunder-docs | tutorials/series.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
sns.set_context('notebook')
from showit import image
import thunder as td
"""
Explanation: Series
A Series object is a collection of 1D arrays, all of which share a common index. Under the hood, it wraps an n-dimension... |
AtmaMani/pyChakras | udemy_ml_bootcamp/Deep Learning/MNIST with Multi-Layer Perceptron.ipynb | mit | import tensorflow as tf
# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
MNIST Multi-Layer Perceptron
In this lecture we will b... |
zhaojijet/UdacityDeepLearningProject | examples/Q-learning-cart.ipynb | apache-2.0 | import gym
import tensorflow as tf
import numpy as np
# Create the Cart-Pole game environment
env = gym.make('CartPole-v0')
"""
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 a... |
amitkaps/applied-machine-learning | Module-03g-Model-HyperParameterOpt.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('fivethirtyeight')
#Read the data
#Read the data
df = pd.read_csv("data/historical_loan.csv")
# refine the data
df.years = df.years.fillna(np.mean(df.years))
# Setup the features and target
X = df.iloc[:,1:]
y =... |
rasbt/pattern_classification | dimensionality_reduction/projection/principal_component_analysis_old.ipynb | gpl-3.0 | import numpy as np
np.random.seed(0)
mu_vec1 = np.array([0, 0, 0])
cov_mat1 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
class1_sample = np.random.multivariate_normal(mu_vec1, cov_mat1, 20).T
assert class1_sample.shape == (3, 20), "The matrix has not the dimensions 3x20"
mu_vec2 = np.array([1, 1, 1])
cov_mat2 = np.... |
richlewis42/scikit-chem | docs/tutorial/pipelines.ipynb | bsd-3-clause | pipeline = skchem.pipeline.Pipeline([
skchem.standardizers.ChemAxonStandardizer(keep_failed=True),
skchem.forcefields.UFF(),
skchem.filters.OrganicFilter(),
skchem.descriptors.MorganFeaturizer()])
"""
Explanation: Pipelining
scikit-chem expands on the scikit-learn Pipeline object to sup... |
NeuroDataDesign/seelviz | Jupyter/Filter_and_Plotly_Luke.ipynb | apache-2.0 | import os
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from ndreg import *
import ndio.remote.neurodata as neurodata
import nibabel as nb
refToken = "ara_ccf2"
refImg = imgDownload(refToken)
refAnnoImg = imgDownload(refToken, channel="annotation")
randValues = np.random.rand(1000,3)
randV... |
karlstroetmann/Artificial-Intelligence | Python/3 Games/Minimax.ipynb | gpl-2.0 | def maxValue(State):
if finished(State):
return utility(State)
return max([ minValue(ns) for ns in next_states(State, gPlayers[0]) ])
"""
Explanation: The Minimax Algorithm with Memoization
This notebook implements the minimax algorithm with memoization
and thereby implements a program that can play v... |
Lstyle1/Deep_learning_projects | 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... |
liopic/scbcn17-customer-segmentation | Workshop-with-answers.ipynb | mit | # This is a cell with code. Try to execute it: use the mouse to focus it and press control+Enter.
' '.join(['Hello', 'world'])
"""
Explanation: Customer segmentation with unsupervised learning
We will learn by doing: you should complete with code the places marked with [WORKSHOP]
We will be using the following tools ... |
saketkc/hatex | 2015_Fall/MATH-578B/Homework1/Homework1.ipynb | mit | %matplotlib inline
from __future__ import division
import numpy as np
from numpy import linalg as LA
k_a=0.2
k_b=0.2
k_p = 0.5
P = np.matrix([[1-k_a-k_b, k_a ,k_b, 0, 0, 0],
[k_a, 1-k_a-k_b, 0, k_b, 0, 0],
[k_b, 0, 1-k_a-k_b, k_a, 0, 0],
[0, k_b, k_a, 1-k_a-k_b-k_p, k_p, 0],... |
mtchem/ETL-MarchMadness-data | data_to_SQLite_DB.ipynb | mit | import sqlite3 as sql
import pandas as pd
"""
Explanation: This notebook will walk you through each step of creating a SQLite Database for the kaggle.com March Machine Learning Mania 2017 competition.
*In order to use this notebook you need to obtain the data from kaggle.com
Create a Database
1. Download DB Browser fo... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session06/Day2/IntroToParaView.ipynb | mit | from pyevtk.hl import gridToVTK, pointsToVTK
"""
Explanation: First and foremost, you should have already installed ParaView on your machine. If not, you can download the package from paraview.org.
An Introduction to ParaView
version 0.1
Adam A Miller (Northwestern CIERA/Adler Planetarium)
01 May 2018
Our data do not... |
arcyfelix/Courses | 18-11-22-Deep-Learning-with-PyTorch/01-Introduction to neural networks/07-Analysing-Student-Data-Example/StudentAdmissions.ipynb | apache-2.0 | # Importing pandas and numpy
import pandas as pd
import numpy as np
# Reading the csv file into a pandas DataFrame
data = pd.read_csv('student_data.csv')
# Printing out the first 10 rows of our data
data[:10]
"""
Explanation: Predicting Student Admissions with Neural Networks
In this notebook, we predict student adm... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/miroc-es2l/aerosol.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'miroc-es2l', 'aerosol')
"""
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MIROC
Source ID: MIROC-ES2L
Topic: Aerosol
Sub-Topics: Transport, Emissions, C... |
boffi/boffi.github.io | dati_2015/ha03/03_Numerical_Integration.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from numpy import cos, exp, pi, sin, sqrt
from IPython.display import Latex, display
plt.style.use(['fivethirtyeight', './00_mplrc'])
"""
Explanation: Numerical Integration
Preliminaries
We have to import the array library numpy and the plotting li... |
quantumlib/OpenFermion | docs/fqe/tutorials/fermi_hubbard.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... |
ShubhamDebnath/Coursera-Machine-Learning | Course 4/Convolution model Step by Step v2.ipynb | mit | import numpy as np
import h5py
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
%load_ext autoreload
%autoreload 2
np.random.seed(1)
"""
Explanation: Convolut... |
spulido99/Programacion | Camilo/.ipynb_checkpoints/Taller 3 y 4-checkpoint.ipynb | mit | import numpy as np #libreria de datos numericos
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import statsmodels.formula.api as smf
import pylab as pl
from sklearn import datasets
%matplotlib inline
from sklearn import datasets
"""
Explanation: Taller 3 Regresión y Clustering
Este taller... |
GoogleCloudPlatform/training-data-analyst | quests/endtoendml/labs/4_preproc.ipynb | apache-2.0 | # Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.1
%pip install apache-beam[gcp]==2.13.0
"""
Explanation: <h1> Preprocessing using Cloud Dataflow </h1>
<h2>Learning Objectives</h2>
<ol>
<li>Create ML dataset using <a href="https://cloud.google.com/dataflow/">Cloud Dataflow<... |
steven-murray/halomod | docs/examples/wdm.ipynb | mit | from matplotlib.pyplot import *
import matplotlib.pyplot as plt
%matplotlib inline
%load_ext autoreload
%autoreload 2
from hmf.alternatives.wdm import TransferWDM # Transfer-function class with WDM modifications
from hmf import MassFunction # Full mass-function class in CDM
from hmf.alternatives.wdm import MassFun... |
ES-DOC/esdoc-jupyterhub | notebooks/ncc/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', 'ncc', 'sandbox-2', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: NCC
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties: 6... |
quantopian/research_public | notebooks/lectures/Why_Hedge_II/notebook.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Why You Should Hedge Beta and Sector Exposures (Part II)
by Jonathan Larkin and Maxwell Margenot
Part of the Quantopian Lecture Series:
www.quantopian.com/lectures
github.com/quantopian/research_public
In the first lecture on Why You Should Hedge B... |
tpin3694/tpin3694.github.io | python/pandas_assign_new_column_dataframe.ipynb | mit | import pandas as pd
"""
Explanation: Title: Assign A New Column To A Pandas DataFrame
Slug: pandas_assign_new_column_dataframe
Summary: Assign a new column to a pandas dataframe.
Date: 2017-01-16 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon
Preliminaries
End of explanation
"""
# Create empty d... |
mohanprasath/Course-Work | numpy/numpy_exercises_from_kyubyong/Input_and_Output.ipynb | gpl-3.0 | from __future__ import print_function
import numpy as np
author = "kyubyong. https://github.com/Kyubyong/numpy_exercises"
np.__version__
from datetime import date
print(date.today())
"""
Explanation: Input and Output
End of explanation
"""
x = np.arange(10)
...
# Check if there exists the 'temp.npy' file.
import... |
bjshaw/phys202-2015-work | assignments/assignment03/NumpyEx03.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import antipackage
import github.ellisonbg.misc.vizarray as va
"""
Explanation: Numpy Exercise 3
Imports
End of explanation
"""
def brownian(maxt, n):
"""Return one realization of a Brownian (Wiener) process with n steps... |
kdestasio/online_brain_intensive | nipype_tutorial/notebooks/introduction_python.ipynb | gpl-2.0 | import math
"""
Explanation: <div align="center"><img src="https://www.python.org/static/img/python-logo.png"></div>
Python
This section is meant as a general introduction to Python and is by far not complete. It is based amongst others on the IPython notebooks from J. R. Johansson, on http://www.stavros.io/tutorials... |
mne-tools/mne-tools.github.io | 0.21/_downloads/96cdfef13f5475e2f32b862fa94904b7/plot_visualize_stc.ipynb | bsd-3-clause | import os
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse, read_inverse_operator
from mne import read_evokeds
data_path = sample.data_path()
sample_dir = os.path.join(data_path, 'MEG', 'sample')
subjects_dir... |
ethen8181/machine-learning | ab_tests/causal_inference/diff_in_diff.ipynb | mit | # code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(plot_style=False)
os.chdir(path)
# 1. magic for inline plot
# 2. magic to print vers... |
jseabold/statsmodels | examples/notebooks/statespace_cycles.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from pandas_datareader.data import DataReader
endog = DataReader('UNRATE', 'fred', start='1954-01-01')
endog.index.freq = endog.index.inferred_freq
"""
Explanation: Trends and cycles in unemployment... |
dfm/dfm.io | static/downloads/notebooks/stan-c++.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = "retina"
from matplotlib import rcParams
rcParams["savefig.dpi"] = 100
rcParams["figure.dpi"] = 100
rcParams["font.size"] = 20
"""
Explanation: Title: Using external C++ functions with PyStan & radial velocity exoplanets
Date: 2017-05-04
Category: Data Analysis... |
CyberCRI/dataanalysis-herocoli-redmetrics | v1.52/Tests/1.4 Google form analysis - Statistics.ipynb | cc0-1.0 | %run "../Functions/1. Google form analysis.ipynb"
"""
Explanation: Google form analysis tests
Purpose: determine in what extent the current data can accurately describe correlations, underlying factors on the score.
Especially concerning the 'before' groups: are there underlying groups explaining the discrepancies in ... |
torgebo/deep_learning_workshop | 2-mlp/multilayer-perceptron.ipynb | mit | # Create input signal
x = np.array([[ 1, -4, 0, 7]])
# Create weight matrices for the three layers
W1 = np.array([[-0.1, 0.2, 0.1],
[ 0.1, 0.4, 0.1],
[ 0.0, -0.7, 0.2],
[ 0.6, 0.3, -0.4]])
W2 = np.array([[ 0.3, -0.8, 0.1, 0.0],
[ 0.0, 0.1, 0.2, 0.8]... |
rubensfernando/mba-analytics-big-data | Python/2016-07-25/aula3-parte2-series.ipynb | mit | import pandas as pd
"""
Explanation: Series
End of explanation
"""
import numpy as np
ser1 = pd.Series(np.random.rand(7))
ser1
"""
Explanation: Series é na verdade um array NumPy de 1 dimensão. Ele consiste de um array NumPy com um array de rótulos.
Criando Series
O construtor geral para criar uma Series é da segu... |
florianwittkamp/FD_ACOUSTIC | JupyterNotebook/1D/FD_1D_DX4_DT3_ABS.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import time as tm
import matplotlib.pyplot as plt
"""
Explanation: FD_1D_DX4_DT3_ABS 1-D acoustic Finite-Difference modelling
GNU General Public License v3.0
Author: Florian Wittkamp
Finite-Difference acoustic seismic wave simulation
Discretization of the first-order acoustic wave... |
mohanprasath/Course-Work | numpy/numpy_exercises_from_kyubyong/Discrete_Fourier_Transform.ipynb | gpl-3.0 | a = 1+1j
output = ...
print(output)
"""
Explanation: Complex Numbers
Q1. Return the angle of a in radian.
End of explanation
"""
a = np.array([1+2j, 3+4j, 5+6j])
real = ...
imag = ...
print("real part=", real)
print("imaginary part=", imag)
"""
Explanation: Q2. Return the real part and imaginary part of a.
End of e... |
mnschmit/LMU-Syntax-nat-rlicher-Sprachen | 01-notebook.ipynb | apache-2.0 | from nltk.tree import Tree
np1 = Tree('NP', [Tree('ART', ['der']), Tree('N', ['Hund'])])
np2 = Tree('NP', [Tree('ART', ['die']), Tree('N', ['Katze'])])
vp = Tree('VP', [Tree('V', ['jagt']), np2])
"""
Explanation: Übung 1
Bäume zeichnen
Zunächst konstruieren wir manuell einen Baum:
End of explanation
"""
vp
"""
Exp... |
tritemio/PyBroMo | notebooks/Theory - On Browniam motion and Diffusion coefficient.ipynb | gpl-2.0 | D = 1
t = 1
N = 3
sigma = np.sqrt(2*N*D*t)
sigma_1d = np.sqrt(2*D*t)
sigma
n_trajectories = 100
n_time_steps = 1000
np.random.seed(1)
dx = sigma_1d*np.random.randn(n_time_steps, n_trajectories)
dy = sigma_1d*np.random.randn(n_time_steps, n_trajectories)
dz = sigma_1d*np.random.randn(n_time_steps, n_trajectories)
x,... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/n08_simple_q_learner_11_actions.ipynb | mit | # Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
from multiprocessing import Pool
%matplotlib inline
%pylab inline
pylab.rcPar... |
arkottke/pysra | examples/example-06.ipynb | mit | import re
import matplotlib.pyplot as plt
import numpy as np
import pysra
%matplotlib inline
# Increased figure sizes
plt.rcParams["figure.dpi"] = 120
"""
Explanation: Example 6 : TS with Geopsy Profiles
Time series analysis to compute surface response spectrum and site
amplification functions using velocity prof... |
ajul/zerosum | python/examples/hazard.ipynb | bsd-3-clause | import _initpath
import numpy
import zerosum.balance
"""
Explanation: Hazard handicap function example
This example uses a handicap function and data taken from a talk by Hazard. This example appears on Slide 36.
Hazard, C. J. 2010. What every game designer should know about game theory. Triangle Game Conference. Rale... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session03/Day2/Documentation.ipynb | mit | def do_something(arg1, arg2):
"""
A short sentence describing what this function does.
More description
Parameters
----------
arg1 : type1
Description of the parameter ``arg1``
arg2 : type2
Description of the parameter ``arg2``
Returns
-------
t... |
Elixeus/NLP | sequence_models/HMM/ViterbiAlgorithm.ipynb | mit | transi = pd.DataFrame({'VB': [.019, .0038, .83, .0040, .23],
'TO': [.0043, .035, 0, .016, .00079],
'NN': [.041, .047, .00047, .087, .0012],
'PPSS': [.067, .0070, 0, .0045, .00014]},
index=['s', 'VB', 'TO', 'NN', 'PPSS'],
... |
chrinide/hep_ml | notebooks/DemoReweighting.ipynb | apache-2.0 | %pylab inline
figsize(16, 8)
import root_numpy
import pandas
from hep_ml import reweight
"""
Explanation: Demonstration of distribution reweighting
hep_ml.reweight contains methods to reweight distributions.
Typically we use reweighting of monte-carlo to fight drawbacks of simulation, though there are many applicati... |
napsternxg/pyLDAvis | notebooks/sklearn.ipynb | bsd-3-clause | from __future__ import print_function
import pyLDAvis
import pyLDAvis.sklearn
pyLDAvis.enable_notebook()
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.decomposition import LatentDirichletAllocation
"""
Explanation: pyLDAvis.s... |
tanghaibao/goatools | notebooks/Optional_attribute_definition.ipynb | bsd-2-clause | from os.path import exists
from goatools.obo_parser import GODag
if not exists('go-basic.obo'):
!wget http://geneontology.org/ontology/go-basic.obo
dag = GODag('go-basic.obo', optional_attrs=['def'])
"""
Explanation: Loading the optional definition attribute
1) Load the ontology file while also loading the option... |
the-deep-learners/nyc-ds-academy | notebooks/tensor-fied_intro_to_tensorflow.ipynb | mit | import numpy as np
np.random.seed(42)
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
tf.set_random_seed(42)
"""
Explanation: Introduction to TensorFlow, fitting point by point
In this notebook, we introduce TensorFlow by fitting a line of the form y=m*x+b point by point.... |
synthicity/activitysim | activitysim/examples/example_estimation/notebooks/01_estimation_mode.ipynb | agpl-3.0 | import os
import larch # !conda install larch -c conda-forge # for estimation
import pandas as pd
"""
Explanation: Estimation Mode
This set of notebooks illustrates how to re-estimate ActivitySim's choice models. These models include
school_location
workplace_location
auto_ownership
free_parking
cdap
mandatory_tour... |
marioberges/F16-12-752 | homeworks/homework2/homework2_solution.ipynb | gpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import datetime as dt
import itertools
import pickle
%matplotlib inline
"""
Explanation: 12-752: Data-Driven Building Energy Management
Fall 2016, Carnegie Mellon University
Assignment #2
We will begin by unpickling the dataset we had played aroun... |
franciscodominguezmateos/DeepLearningNanoDegree | first-neural-network/Your_first_neural_network.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
mrcslws/nupic.research | projects/whydense/gsc/analyze_scaling.ipynb | agpl-3.0 | sparse_all = pd.read_pickle("scaling_results/sparse_scaling.pkl")
dense_all = pd.read_pickle("scaling_results/dense_scaling_baselines.pkl")
sparse_activations = sparse_all[sparse_all["Activation sparsity"] == 1]
sparse_wts = sparse_all[sparse_all["Activation sparsity"] == 0]
sparse_all.head()
dense_all.head()
spa... |
harikrishna1302/Convolutional-Neural-Network-using-MNIST-images | CNN.ipynb | apache-2.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from sklearn.metrics import confusion_matrix
import time
from datetime import timedelta
import math
"""
Explanation: Import all the required libraries.
End of explanation
"""
tf.__version__
"""
Explanation: Print the Tens... |
JasonJWilliamsNY/biocoding_2015 | lessons/biocoding_2015_pythonlab_04.ipynb | cc0-1.0 | # build a random dna sequence...
from numpy import random
final_sequence_length = eighty
initial_sequence_length = 81
dna_sequence = ''
my_nucleotides = [a,t,g,c]
my_nucleotide_probs = [0.25,0.25,0.25,0.3]
while initial_sequence_length < final_sequence_length:
nucleotide = random.choice(my_nucleotides,p=my_nucleotide_... |
jorgemf/kaggle_redefining_cancer_treatment | notebook/final_notebook.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import log_loss, accuracy_score
from sklearn.feature_extraction.text import Count... |
ondrolexa/sg2 | 10_Introduction_to_strain_analysis.ipynb | mit | %pylab inline
"""
Explanation: Homogeneous deformation
Often described as deformation during which lines remain as lines and parallel lines remain parallel. Homogeneous deformation could be described as affine transformation of initial coordinates
$$\begin{align}x &= aX+bY+t_X \ y &= cX+dY+t_Y \end{align}$$
or in matr... |
NumCosmo/NumCosmo | notebooks/DataNCount/run_ascaso_mc_unbinned.ipynb | gpl-3.0 | #CCL cosmology
cosmo_ccl = ccl.Cosmology(Omega_c = 0.30711 - 0.048254, Omega_b = 0.048254, h = 0.677, sigma8 = 0.8822714165197718, n_s=0.96, Omega_k = 0, transfer_function='eisenstein_hu')
#ccl_cosmo_set_high_prec (cosmo_ccl)
cosmo_numcosmo, dist, ps_lin, ps_nln, hmfunc = create_nc_obj (cosmo_ccl)
psf = hmfunc.peek_p... |
diging/methods | 2.0. Co-author networks/2.0. Co-Author Graphs.ipynb | gpl-3.0 | from tethne.readers import wos
metadata = wos.read('../data/Baldwin/PlantPhysiology',
streaming=True, index_fields=['date'], index_features=['authors'])
"""
Explanation: 2.0. Co-author Graphs
Social network analysis is a popular method in the field of historical network research. By far the most ... |
AllenDowney/ModSimPy | soln/chap08soln.ipynb | mit | # If we're running on Colab, install modsimpy
# https://pypi.org/project/modsimpy/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install pint
!pip install modsimpy
!mkdir figs
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# import functions from the mod... |
tensorflow/docs-l10n | site/en-snapshot/guide/keras/customizing_what_happens_in_fit.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... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/.ipynb_checkpoints/Lists-checkpoint.ipynb | apache-2.0 | # Assign a list to an variable named my_list
my_list = [1,2,3]
"""
Explanation: Lists
Earlier when discussing strings we introduced the concept of a sequence in Python. Lists can be thought of the most general version of a sequence in Python. Unlike strings, they are mutable, meaning the elements inside a list can be ... |
bosonbeard/Funny-models-and-scripts | 3.Machine_learning/2.family_registered_habr.ipynb | unlicense | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn import linear_model
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
"""
Explanation: Введени... |
tensorflow/docs-l10n | site/zh-cn/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder_lite.ipynb | apache-2.0 | # Copyright 2018 The TensorFlow Hub 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
fluffy-hamster/A-Beginners-Guide-to-Python | A Beginners Guide to Python/Homework Solutions/06. Names (HW).ipynb | mit | cake = 3.14
diameter = 2
belt_size = cake * diameter
"""
Explanation: Names, Homework
Below is three lines of python code, the only thing you have seen yet is the "*" symbol which means multiplication. Your task comes in three parts:
Figure out what the code is supposed to do. (this might take some googling hint: th... |
karlstroetmann/Algorithms | Python/Chapter-02/Power.ipynb | gpl-2.0 | def power(m, n):
r = 1
for i in range(n):
r *= m
return r
power(2, 3), power(3, 2)
%%time
p = power(3, 500000)
p
"""
Explanation: Efficient Computation of Powers
The function power takes two natural numbers $m$ and $n$ and computes $m^n$. Our first implementation is inefficient and takes $n-1$ ... |
heroxbd/SHTOOLS | examples/notebooks/Introduction-1.ipynb | bsd-3-clause | %matplotlib inline
from __future__ import print_function # only necessary if using Python 2.x
import matplotlib.pyplot as plt
import numpy as np
import pyshtools
"""
Explanation: Introduction to pySHTOOLS
pyshtools is a Python module that provides functions for working with spherical harmonics. These routines can be ... |
bgroveben/python3_machine_learning_projects | learn_kaggle/machine_learning/starting_your_ml_project.ipynb | mit | import pandas as pd
# Load the data and greet everyone.
main_file_path = 'input/melbourne_data.csv'
data = pd.read_csv(main_file_path)
# If you want to remove entries with missing values:
# filtered_data = data.dropna(axis=0)
print('hello world')
"""
Explanation: Starting your ML project
Introduction
This will be you... |
csiu/datasci | d3/2015-07-14_stacked-bar-chart.ipynb | mit | import pandas as pd
datfile = 'dat/adult.data'
def strip(text):
try:
return text.strip()
except AttributeError:
return text
def make_int(text):
return int(text.strip('" '))
df = pd.read_csv(datfile,
names=['age', 'workclass', 'fnlwgt',
'educa... |
leonarduk/stockmarketview | timeseries-analysis-python/src/main/python/FinanceOps/02_Comparing_Stock_Indices.ipynb | apache-2.0 | %matplotlib inline
# Imports from Python packages.
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
import pandas as pd
import numpy as np
import os
# Imports from FinanceOps.
from data_keys import *
from data import load_index_data, load_usa_cpi
from data import load_usa_gov_bond_1year, co... |
elenduuche/deep-learning | tv-script-generation/dlnd_tv_script_generation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/ml_fairness_explainability/explainable_ai/solutions/xai_image_caip.ipynb | apache-2.0 | from datetime import datetime
TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S")
import os
PROJECT_ID = "" # TODO: your PROJECT_ID here.
os.environ["PROJECT_ID"] = PROJECT_ID
BUCKET_NAME = PROJECT_ID # TODO: replace BUCKET_NAME if needed
REGION = "us-central1"
os.environ["BUCKET_NAME"] = BUCKET_NAME
os.environ[... |
rubensfernando/mba-analytics-big-data | Python/2016-08-05/aula6-parte4-foto.ipynb | mit | import facebook
access_token = 'EAACUzLmOZC7kBAPfCPMRBG23rGoY3iQWKJMIO7ESZCp0LPZCwQQv0AoQeEtBm9IyNDi5yP2RHMGzCzjquLb4ZCWUHLZA6vY1Pp6x8oFXZA7IMissQbporZAwUoIZCuZBoOBrWQDxi8PUUZCb96uWmSwB2ZBqEIwnvZCRZBnqJjGZBQJVhl1gZDZD'
api = facebook.GraphAPI(access_token)
api.version
foto = open("fia.jpg", "rb")
api.put_photo(fo... |
janusnic/21v-python | unit_20/parallel_ml/rendered_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... |
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