repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
asydorchuk/ml | classes/cs231n/assignment2/ConvolutionalNetworks.ipynb | mit | # As usual, a bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.cnn import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient
from cs231n.layers import *
from cs231n.fast_layers import *
from cs... |
tpin3694/tpin3694.github.io | python/filter_dataframes.ipynb | mit | import pandas as pd
"""
Explanation: Title: Filter pandas Dataframes
Slug: filter_dataframes
Summary: Filter pandas Dataframes
Date: 2016-05-01 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon
Import modules
End of explanation
"""
data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
... |
mathnathan/notebooks | dissertation/Single Gaussian.ipynb | mit | a = -0.7
j_vals = []
kl_vals = []
mus = np.linspace(0,1,100)
for mu in mus:
j_vals.append(J(mu,p_sig,a)[0])
kl_vals.append(KL(mu,p_sig)[0])
fig = plt.figure(figsize=(15,5))
p_vals = p(mus)
plt.plot(mus, p_vals/p_vals.max(), label="$p(x)$")
#plt.plot(mus, j_vals/np.max(np.abs(j_vals)), label='$J$')
plt.plot(mus,... |
martinjrobins/hobo | examples/interfaces/automatic-differentiation-using-autograd.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import warnings
from timeit import repeat
import pints
import pints.toy as toy
import autograd.numpy as np
from autograd.scipy.integrate import odeint
from autograd.builtins import tuple
from autograd import grad
"""
Explanation: Using autograd to calculate the gra... |
phoebe-project/phoebe2-docs | 2.0/examples/sun.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.0,<2.1"
"""
Explanation: Sun (single rotating star)
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
%matplotlib i... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/.ipynb_checkpoints/Collections Module-checkpoint.ipynb | apache-2.0 | from collections import Counter
"""
Explanation: Collections Module
The collections module is a built-in module that implements specialized container datatypes providing alternatives to Python’s general purpose built-in containers. We've already gone over the basics: dict, list, set, and tuple.
Now we'll learn about t... |
sdpython/ensae_teaching_cs | _doc/notebooks/td1a_home/2020_regex.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: Tech - expressions régulières
Les expressions régulières sont utilisées pour rechercher des motifs dans un texte tel que des mots, des dates, des nombres...
End of explanation
"""
poeme = """
A noir, E blanc, I rouge, U vert, O bleu, vo... |
southpaw94/MachineLearning | TextExamples/3547_02_Code.ipynb | gpl-2.0 | %load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
"""
Explanation: Sebastian Raschka, 2015
Python Machine Learning Essentials
Chapter 2 ... |
jrbourbeau/cr-composition | notebooks/legacy/parameter-tuning/RF-parameter-tuning.ipynb | mit | import sys
sys.path.append('/home/jbourbeau/cr-composition')
print('Added to PYTHONPATH')
from __future__ import division, print_function
import argparse
from collections import defaultdict
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import seabor... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_stats_cluster_time_frequency_repeated_measures_anova.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import tfr_morlet
from mne.sta... |
statsmodels/statsmodels.github.io | v0.13.2/examples/notebooks/generated/deterministics.ipynb | bsd-3-clause | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
plt.rc("figure", figsize=(16, 9))
plt.rc("font", size=16)
"""
Explanation: Deterministic Terms in Time Series Models
End of explanation
"""
from statsmodels.tsa.deterministic import DeterministicProcess
index = pd.RangeIndex(0, 100)
det_proc = ... |
gojomo/gensim | docs/notebooks/soft_cosine_tutorial.ipynb | lgpl-2.1 | # Initialize logging.
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
"""
Explanation: Finding similar documents with Word2Vec and Soft Cosine Measure
Soft Cosine Measure (SCM) [1, 3] is a promising new tool in machine learning that allows us to submit a quer... |
usantamaria/iwi131 | ipynb/15-FuncionesAvanzadas/FuncionesAvanzadas.ipynb | cc0-1.0 | from math import exp, cos, pi, sin
from random import randrange, choice
from turtle import * # Evitar
print exp(5.5)
print cos(pi / 2)
print randrange(10)
print choice(['Lunes', 'Martes', 'Viernes'])
"""
Explanation: <header class="w3-container w3-teal">
<img src="images/utfsm.png" alt="" align="left"/>
<img src="ima... |
deeplook/notebooks | color_scheme_3d/visualising_color_schemes_3d.ipynb | mit | from collections import OrderedDict
# values entered manually from https://brandlive.here.com/colors
here_primary_cols = OrderedDict(
HERE_Aqua = '#48dad0',
HERE_Aqua_UNKNOWN = '#00908a', # unknown status, maybe an error?
HERE_Aqua_Dark = '#00afaa',
HERE_Aqua_75 = '#76e3dc',
HERE_Aq... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch9-Problem_9-09.ipynb | unlicense | %pylab notebook
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 9
Problem 9-9
End of explanation
"""
p = 12
n_m = 600 # [r/min]
"""
Explanation: Description
How many pulses per second must be supplied to the control unit of the motor in Problem 9-8 to achieve
a rotational speed of 600 r/min?... |
oldtopos/SDCND_Project1 | P1.ipynb | mit | #importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
"""
Explanation: Self-Driving Car Engineer Nanodegree
Project: Finding Lane Lines on the Road
In this project, you will use the tools you learned about in the lesson to ide... |
xunilrj/sandbox | courses/MITx/MITx 6.86x Machine Learning with Python-From Linear Models to Deep Learning/SVM%20-%20PyTorch.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
X = np.array([[0,0],[2,0],[3,0],[0,2],[2,2],[5,1],[5,2],[2,4],[4,4],[5,5]])
Y = np.array([-1,-1,-1,-1,-1,1,1,1,1,1])
YColor = np.array(["red","red","red","red","red","green","green","green","green","green"])
plt.scatter(x=X[:, 0], y=X[:, 1], c=YColo... |
jcharit1/Amazon-Fine-Foods-Reviews | code/.ipynb_checkpoints/experimental-checkpoint (jimmy-Precision-T1600's conflicted copy 2017-05-14).ipynb | mit | import os
import pandas as pd
import numpy as np
import scipy as sp
import seaborn as sns
import matplotlib.pyplot as plt
import json
from IPython.display import Image
from IPython.core.display import HTML
import tensorflow as tf
retval=os.chdir("..")
clean_data=pd.read_pickle('./clean_data/clean_data.pkl')
clean_da... |
Iwan-Zotow/VV | C25_GP3.ipynb | mit | import math
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import BEAMphsf
import text_loader
import H1Dn
import H1Du
import ListTable
%matplotlib inline
"""
Explanation: Validation and Verification of the 25mm collimator simulation, GP3
Here we provide code and output which verifies and valid... |
tensorflow/docs-l10n | site/en-snapshot/tfx/tutorials/tfx/penguin_tfma.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... |
datacommonsorg/api-python | notebooks/intro_data_science/Regression_Basics_and_Prediction.ipynb | apache-2.0 | # Setup/Imports
!pip install datacommons --upgrade --quiet
!pip install datacommons_pandas --upgrade --quiet
# Data Commons Python and Pandas APIs
import datacommons
import datacommons_pandas
# For manipulating data
import numpy as np
import pandas as pd
# For implementing models and evaluation methods
from sklearn ... |
Aniruddha-Tapas/Applied-Machine-Learning | Regression/Air-Quality-Prediction.ipynb | mit | import os
from sklearn.tree import DecisionTreeClassifier, export_graphviz
import pandas as pd
import numpy as np
from time import time
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import SGDRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import tr... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/end_to_end_ml/solutions/serving_babyweight.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
%%bash
# Check your project name
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
import os
os.environ["BUCKET"] = "your-bucket-id-here" # Recommended: use your project name
... |
ES-DOC/esdoc-jupyterhub | notebooks/inm/cmip6/models/sandbox-3/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inm', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: INM
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbulen... |
BayesianTestsML/tutorial | Python/Bsignedrank.ipynb | gpl-3.0 | import numpy as np
scores = np.loadtxt('Data/accuracy_nbc_aode.csv', delimiter=',', skiprows=1, usecols=(1, 2))
names = ("NBC", "AODE")
"""
Explanation: Bayesian Signed-Rank Test
Module signrank in bayesiantests computes the Bayesian equivalent of the Wilcoxon signed-rank test. It returns probabilities that, based on ... |
cmawer/pycon-2017-eda-tutorial | notebooks/1-RedCard-EDA/4-Redcard-final-joins.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format='retina'
from __future__ import absolute_import, division, print_function
import matplotlib as mpl
from matplotlib import pyplot as plt
from matplotlib.pyplot import GridSpec
import seaborn as sns
import numpy as np
import pandas as pd
import os, sys
from tqdm imp... |
karlstroetmann/Formal-Languages | ANTLR4-Python/AST-2-Dot.ipynb | gpl-2.0 | import graphviz as gv
"""
Explanation: Drawing Abstract Syntax Trees with GraphViz
End of explanation
"""
def tuple2dot(t):
dot = gv.Digraph('Abstract Syntax Tree')
Nodes_2_Names = {}
assign_numbers((), t, Nodes_2_Names)
create_nodes(dot, (), t, Nodes_2_Names)
return dot
"""
Explanation: The fun... |
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn | doc/notebooks/expression.derivation.ipynb | gpl-3.0 | import vcsn
from IPython.display import Latex
def diffs(r, ss):
eqs = []
for s in ss:
eqs.append(r'\frac{{\partial}}{{\partial {0}}} {1}& = {2}'
.format(s,
r.format('latex'),
r.derivation(s).format('latex')))
return Latex(r'''... |
ihmeuw/dismod_mr | examples/export_csv.ipynb | agpl-3.0 | !wget http://ghdx.healthdata.org/sites/default/files/record-attached-files/IHME_GBD_HEP_C_RESEARCH_ARCHIVE_Y2013M04D12.ZIP
!unzip IHME_GBD_HEP_C_RESEARCH_ARCHIVE_Y2013M04D12.ZIP
# This Python code will export predictions
# for the following region/sex/year:
predict_region = 'USA'
predict_sex = 'male'
predict_year = 2... |
kfollette/ASTR200-Spring2017 | Labs/Lab11/Lab11.ipynb | mit | #data from: http://exoplanetarchive.ipac.caltech.edu/cgi-bin/TblView/nph-tblView?app=ExoTbls&config=planets
#download table -> csv format, all rows, all columns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import scipy.stats as st
# these set the pandas defaults so that it... |
valter-lisboa/ufo-notebooks | Python3/ufo-sample-python3.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
"""
Explanation: USA UFO sightings (Python 3 version)
This notebook is based on the first chapter sample from Machine Learning for Hackers with some added features. I did this to present Jupyter Notebook with Python 3 for Tech Days in my Job.
The original link is offline so you ... |
nmih/ssbio | docs/notebooks/GEM-PRO - Calculating Protein Properties.ipynb | mit | import sys
import logging
# Import the GEM-PRO class
from ssbio.pipeline.gempro import GEMPRO
# Printing multiple outputs per cell
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
"""
Explanation: GEM-PRO - Calculating Protein Properties
This notebook gives a... |
mne-tools/mne-tools.github.io | 0.12/_downloads/plot_object_raw.ipynb | bsd-3-clause | from __future__ import print_function
import mne
import os.path as op
from matplotlib import pyplot as plt
"""
Explanation: .. _tut_raw_objects:
The :class:Raw <mne.io.Raw> data structure: continuous data
End of explanation
"""
# Load an example dataset, the preload flag loads the data into memory now
data_pa... |
adrianstaniec/deep-learning | 04_intro-to-tensorflow/intro_to_tensorflow.ipynb | mit | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
print('All m... |
tcstewar/testing_notebooks | Learning and Adjusting Tuning Curves.ipynb | gpl-2.0 | %matplotlib inline
import pylab # plotting
import seaborn # plotting
import numpy as np # math functions
import nengo # neural modelling
"""
Explanation: Learning and Adjusting Tuning Curves
This is a quick notebook just to sketch out some initial stages of looking at modelling Aaron Batista's data... |
tacaswell/conda-prescriptions | scripts/notebooks/DAG build and runtime requirements for NSLS-II stack.ipynb | bsd-3-clause | latest_tagged = defaultdict(dict)
for lib_name, all_versions in all_recipes.items():
versions = sorted(all_versions.keys())
if len(versions) == 1:
version = versions[0]
else:
if 'dev' in versions:
versions.remove('dev')
version = versions[-1]
latest_tagged[lib_name][v... |
berquist/ipython_notebooks_for_qc | notebooks/Frequency Calculations.ipynb | mpl-2.0 | import numpy as np
with open('qm_files/drop_0375_0qm_0mm.out') as f:
contents_qmoutput = f.read()
"""
Explanation: Frequency Calculations
End of explanation
"""
contents_splitlines = contents_qmoutput.splitlines()
contents_splitlines[340:370]
"""
Explanation: Advanced: calculate frequencies directly from the ... |
rabernat/pyqg | docs/examples/linear_stability.ipynb | mit | import numpy as np
from numpy import pi
import matplotlib.pyplot as plt
%matplotlib inline
import pyqg
m = pyqg.LayeredModel(nx=256, nz = 2, U = [.01, -.01], V = [0., 0.], H = [1., 1.],
L=2*pi,beta=1.5, rd=1./20., rek=0.05, f=1.,delta=1.)
"""
Explanation: Built-in linear stability analy... |
gabll/RomeaJam | Traffik_EDA.ipynb | gpl-3.0 | def get_status(dt, category=None):
"""returns road status given specific datetime"""
if category:
return db.session.query(RoadStatus).filter(RoadStatus.timestamp > dt.strftime('%s')).\
filter(RoadStatus.timestamp < (dt+timedelta(0,60)).strftime('%s')).\
... |
SSDS-Croatia/SSDS-2017 | Day-2/segmentation/semantic_segmentation_clean.ipynb | mit | %matplotlib inline
import time
from os.path import join
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import utils
from data import Dataset
tf.set_random_seed(31415)
tf.logging.set_verbosity(tf.logging.ERROR)
plt.rcParams["figure.figsize"] =... |
tensorflow/docs | site/en/r1/tutorials/keras/basic_regression.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... |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_ica_from_raw.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.preprocessing import ICA
from mne.preprocessing import create_ecg_epochs, create_eog_epochs
from mne.datasets import sample
"... |
YihaoLu/statsmodels | examples/notebooks/statespace_structural_harvey_jaeger.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 IPython.display import display, Latex
"""
Explanation: Detrending, Stylized Facts and the Business Cycle
In an influential article, Harvey and Jaeger (1993) described the use of unobserved comp... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/recommendation_systems/solutions/multitask.ipynb | apache-2.0 | # Installing the necessary libraries.
!pip install -q tensorflow-recommenders
!pip install -q --upgrade tensorflow-datasets
"""
Explanation: Multi-task recommenders
Learning Objectives
1. Training a model which focuses on ratings.
2. Training a model which focuses on retrieval.
3. Training a joint model that ass... |
ES-DOC/esdoc-jupyterhub | notebooks/mohc/cmip6/models/hadgem3-gc31-hm/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-hm', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: MOHC
Source ID: HADGEM3-GC31-HM
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, ... |
daniestevez/jupyter_notebooks | AmicalSat/ShockBurst image packets.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from collections import Counter
"""
Explanation: AmicalSat ShockBurst image packets processing
This notebook shows how to process ShockBurst S-band image packets to reassemble the image file
End of explanation
"""
data = np.fromfile('/home/daniel/... |
mrcslws/nupic.research | projects/archive/dynamic_sparse/notebooks/mcaporale/2019-10-07--Experiment-Analysis-NonBinaryHeb.ipynb | agpl-3.0 | from IPython.display import Markdown, display
%load_ext autoreload
%autoreload 2
import sys
import itertools
sys.path.append("../../")
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 n... |
csc-training/python-introduction | notebooks/examples/6 - Objects.ipynb | mit | class Student(object):
"""
The above states that the code-block (indented area) below will define a
class Student, that derives from a class called 'object'. Inheriting from 'object' is S
"""
def __init__(self, name, birthyear, interest=None):
"""__init__ is special method that is... |
numerical-mooc/assignment-bank-2015 | Chris Tiu Project/Test2.ipynb | mit | import numpy
from matplotlib import pyplot
%matplotlib inline
from matplotlib import rcParams
rcParams['font.family']= 'serif'
rcParams['font.size']=16
from IPython.display import Image
"""
Explanation: Copyright statement?
A New 'Transition'
Welcome back! At this point in the course you are all likley aces at numeric... |
geoscixyz/gpgLabs | notebooks/seismic/Seis_Refraction.ipynb | mit | plotWavelet();
"""
Explanation: Interpretation and data acquisition strategies of seismic refraction data
In the <a href="https://www.3ptscience.com/app/SeismicRefraction">3pt Science app</a>, you explored the expected arrival times for refractions and reflections from a two-layer over a half-space model.
In this not... |
dougsweetser/ipq | q_notebooks/billiard_calculations.ipynb | apache-2.0 | %%capture
import Q_tools as qt;
Aq1=qt.Q8([1470000000,0,1.1421,0,1.4220,0,0,0])
Aq2=qt.Q8([1580000000,0,4.2966,0,0,0.3643,0,0])
q_scale = qt.Q8([2.2119,0,0,0,0,0,0,0], qtype="S")
Aq1s=Aq1.product(q_scale)
Aq2s=Aq2.product(q_scale)
print(Aq1s)
print(Aq2s)
"""
Explanation: Table of Contents
Observing Billiards Using S... |
laurent-george/tutmom | intro.ipynb | bsd-3-clause | import numpy as np
objective = np.poly1d([1.3, 4.0, 0.6])
print objective
"""
Explanation: Introduction to optimization
The basic components
The objective function (also called the 'cost' function)
End of explanation
"""
import scipy.optimize as opt
x_ = opt.fmin(objective, [3])
print "solved: x={}".format(x_)
%ma... |
CoreSecurity/pysap | docs/protocols/SAPRouter.ipynb | gpl-2.0 | from pysap.SAPRouter import *
from IPython.display import display
"""
Explanation: SAP Router
The following subsections show a graphical representation of the main protocol packets and how to generate them.
First we need to perform some setup to import the packet classes:
End of explanation
"""
for command in router... |
JuBra/cobrapy | documentation_builder/building_model.ipynb | lgpl-2.1 | from cobra import Model, Reaction, Metabolite
# Best practise: SBML compliant IDs
cobra_model = Model('example_cobra_model')
reaction = Reaction('3OAS140')
reaction.name = '3 oxoacyl acyl carrier protein synthase n C140 '
reaction.subsystem = 'Cell Envelope Biosynthesis'
reaction.lower_bound = 0. # This is the defaul... |
Tsiems/machine-learning-projects | Lab1/Lab1_Playground.ipynb | mit | import pandas as pd
import numpy as np
df = pd.read_csv('data/data.csv') # read in the csv file
"""
Explanation: Data Loading and Preprocessing
To begin, we load the data into a Pandas data frame from a csv file.
End of explanation
"""
df.info()
df.head()
"""
Explanation: Let's take a cursory glance at the data t... |
VandyAstroML/Vandy_AstroML | profiles/Richard/Project_ScikitLearn_Datasets.ipynb | mit | %matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cross_validation import cross_val_score
"""
Explanation: KNN algorithm example with the sklean digits dataset
Purpose
We will... |
rvm-segfault/edx | python_for_data_sci_dse200x/week3/03_Numpy_Notebook.ipynb | apache-2.0 | import numpy as np
an_array = np.array([3, 33, 333]) # Create a rank 1 array
print(type(an_array)) # The type of an ndarray is: "<class 'numpy.ndarray'>"
# test the shape of the array we just created, it should have just one dimension (Rank 1)
print(an_array.shape)
# because this is a 1-rank array, we... |
seg/2016-ml-contest | LA_Team/Facies_classification_LA_TEAM_06.ipynb | apache-2.0 | %%sh
pip install pandas
pip install scikit-learn
pip install tpot
from __future__ import print_function
import numpy as np
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold , StratifiedKFold
from classif... |
mne-tools/mne-tools.github.io | 0.18/_downloads/3a40d74661a066ddd49c83c766d57670/plot_visualize_epochs.ipynb | bsd-3-clause | # sphinx_gallery_thumbnail_number = 7
import os.path as op
import mne
data_path = op.join(mne.datasets.sample.data_path(), 'MEG', 'sample')
raw = mne.io.read_raw_fif(
op.join(data_path, 'sample_audvis_raw.fif'), preload=True)
raw.load_data().filter(None, 9, fir_design='firwin')
raw.set_eeg_reference('average', p... |
deepmind/deepmind-research | gated_linear_networks/colabs/dendritic_gated_network.ipynb | apache-2.0 | # Copyright 2021 DeepMind Technologies Limited. 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 a... |
Alexoner/skynet | notebooks/knn.ipynb | mit | # Run some setup code for this notebook.
import random
import numpy as np
from skynet.utils.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplotlib inline
plt.rcParams['figure.figsize'] ... |
Hugovdberg/timml | notebooks/timml_notebook1_sol.ipynb | mit | %matplotlib inline
from pylab import *
from timml import *
figsize=(8, 8)
ml = ModelMaq(kaq=[10, 20, 5],
z=[0, -20, -40, -80, -90, -140],
c=[4000, 10000])
w = Well(ml, xw=0, yw=0, Qw=10000, rw=0.2, layers=1)
Constant(ml, xr=10000, yr=0, hr=20, layer=0)
Uflow(ml, slope=0.002, angle=0)
ml.so... |
gbtimmon/ase16GBT | code/6/pwang13.ipynb | unlicense | %matplotlib inline
# All the imports
from __future__ import print_function, division
from math import *
import random
import sys
import matplotlib.pyplot as plt
# TODO 1: Enter your unity ID here
__author__ = "pwang13"
class O:
"""
Basic Class which
- Helps dynamic updates
- Pretty Prints
... |
computational-class/cjc2016 | code/08.07-analyzing_titanic_dataset.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: Introduction to the Basics of Statistics
王成军
wangchengjun@nju.edu.cn
计算传播网 http://computational-communication.com
一、使用Pandas清洗泰坦尼克数据
练习使用Pandas
二、分析天涯回帖数据
学习使用Statsmodels
End of explanation
"""
import pandas as pd
"""
Explanation:
End of exp... |
henchc/Data-on-the-Mind-2017-scraping-apis | 02-Scraping/solutions/01-BS_solutions.ipynb | mit | import requests # to make GET request
from bs4 import BeautifulSoup # to parse the HTML response
import time # to pause between calls
import csv # to write data to csv
import pandas # to see CSV
"""
Explanation: Webscraping with Beautiful Soup
In this lesson we'll learn about various techniques to scrape data fr... |
f-guitart/data_mining | exercises/exercise1.ipynb | gpl-3.0 | df1 = pd.read_csv("../data/iqsize.csv")
# we can apply head method, it will return the n first rows
# n = 5 as a default value
df1.head(10)
"""
Explanation: Load iqsize.csv using pd.read_csv
End of explanation
"""
print("Columns: {}".format(df1.columns))
print("Rows: {}".format(df1.index))
"""
Explanation: Print bo... |
irsisyphus/machine-learning | 3 Kernel, Bayes and Models.ipynb | apache-2.0 | %load_ext watermark
%watermark -a '' -u -d -v -p numpy,pandas,matplotlib,scipy,sklearn
%matplotlib inline
# Added version check for recent scikit-learn 0.18 checks
from distutils.version import LooseVersion as Version
from sklearn import __version__ as sklearn_version
"""
Explanation: Assignment 3 - basic classifiers... |
great-expectations/great_expectations | tests/test_fixtures/upgrade_helper/great_expectations_v20_project_with_v30_configuration_and_v20_checkpoints/notebooks/pandas/validation_playground.ipynb | apache-2.0 | import json
import great_expectations as ge
import great_expectations.jupyter_ux
from great_expectations.datasource.types import BatchKwargs
import datetime
"""
Explanation: Validation Playground
Watch a short tutorial video or read the written tutorial
This notebook assumes that you created at least one expectation s... |
whitead/numerical_stats | unit_10/hw_2016/homework_9_key.ipynb | gpl-3.0 | from math import erf, sqrt
import numpy as np
import scipy.stats
"""
Explanation: Homework 9 Key
CHE 116: Numerical Methods and Statistics
Prof. Andrew White
Version 1.0 (3/23/2016)
End of explanation
"""
mu_sample=1070
mu_popul=1064.
st_dev=5
z=(-mu_popul+mu_sample)/st_dev
print('Z:', z)
p=(1 - np.abs((scipy.stats.... |
jorisvandenbossche/DS-python-data-analysis | _solved/case2_observations_processing.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
"""
Explanation: <p><font size="6"><b> CASE - Observation data - data cleaning and enrichment</b></font></p>
© 2021, Joris Van den Bossche and Stijn Van Hoey (jorisvan... |
Code-Girls/2016Summer | Day6/code/k-means_clustering.ipynb | mit | # NOTE: we use non-random initializations for the cluster centers
# to make autograding feasible; normally cluster centers would be
# randomly initialized.
data = np.load('data/X.npz')
X = data['X']
centers = data['centers']
print ('X: \n' + str(X))
print ('\ncenters: \n' + str(centers))
"""
Explanation: In this... |
NelisW/ComputationalRadiometry | 12g-Plume-texture.ipynb | mpl-2.0 | # to prepare the environment
import numpy as np
import scipy as sp
import pandas as pd
import os.path
from scipy.optimize import curve_fit
from scipy import interpolate
from scipy import integrate
from scipy import signal
from scipy import ndimage
import matplotlib.pyplot as plt
import scipy.constants as const
import p... |
merryjman/astronomy | templateGraphing.ipynb | gpl-3.0 | # import software packages
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
inline_rc = dict(mpl.rcParams)
"""
Explanation: Data Analysis Template
This notebook is a template for data analysis and includes some useful code for calculations and plotting.... |
hankcs/HanLP | plugins/hanlp_demo/hanlp_demo/zh/tutorial.ipynb | apache-2.0 | !pip install hanlp_restful
"""
Explanation: 欢迎来到HanLP在线交互环境，这是一个Jupyter记事本，可以输入任意Python代码并在线执行。请点击左上角【Run】来运行这篇NLP教程。
安装
量体裁衣，HanLP提供RESTful（云端）和native（本地）两种API，分别面向轻量级和海量级两种场景。无论何种API何种语言，HanLP接口在语义上保持一致，你可以任选一种API来运行本教程。
轻量级RESTful API
仅数KB，适合敏捷开发、移动APP等场景。简单易用，无需GPU配环境，强烈推荐，秒速安装：
End of explanation
"""
from hanlp... |
mathLab/RBniCS | tutorials/07_nonlinear_elliptic/tutorial_nonlinear_elliptic_deim.ipynb | lgpl-3.0 | from dolfin import *
from rbnics import *
"""
Explanation: Tutorial 07 - Non linear Elliptic problem
Keywords: DEIM, POD-Galerkin
1. Introduction
In this tutorial, we consider a non linear elliptic problem in a two-dimensional spatial domain $\Omega=(0,1)^2$. We impose a homogeneous Dirichlet condition on the boundary... |
ibm-et/defrag2015 | notebooks/doodle.ipynb | mit | import requests
api_key = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
url = "https://api.meetup.com/2/open_events"
params = {'topic':'bluemix', 'key':api_key}
r = requests.get(url, params=params)
r.raise_for_status()
resp = r.json()
resp.keys()
"""
Explanation: Upcoming Bluemix Meetups
http://www.meetup.com/meetup_api/d... |
csc-training/python-introduction | notebooks/answers/3 - Control Structures.ipynb | mit | breakfast = ["sausage", "eggs", "bacon", "spam"]
for item in breakfast:
print(item)
"""
Explanation: Control Structures
Simple for loop
Write a for loop which iterates over the list of breakfast items "sausage", "eggs", "bacon" and "spam" and prints out the name of item
End of explanation
"""
squares = []
for i ... |
apryor6/apryor6.github.io | visualizations/seaborn/notebooks/.ipynb_checkpoints/colors-checkpoint.ipynb | mit | %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (20.0, 10.0)
df = pd.read_csv('../../../datasets/movie_metadata.csv')
df.head()
"""
Explanation: seaborn.countplot
Bar graphs are useful for displaying relationships between categorical data... |
daniel-koehn/Theory-of-seismic-waves-II | 05_2D_acoustic_FD_modelling/lecture_notebooks/3_fdac2d_num_stability_anisotropy.ipynb | gpl-3.0 | # Execute this cell to load the notebook's style sheet, then ignore it
from IPython.core.display import HTML
css_file = '../../style/custom.css'
HTML(open(css_file, "r").read())
"""
Explanation: Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2018 by D. Koehn, notebook ... |
kpyuan1776/mvv_analyse | graphCompDistances.ipynb | gpl-3.0 | import googlemaps
import json
from datetime import datetime
import networkx as nx
import csv
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# here is my API key from my project
gmaps = googlemaps.Client(key='PUT_GOOGLE_MAPS_API_KEY_HERE')
file_edges = 'edges_sbahn_fixed.tx... |
gschivley/Index-variability | Notebooks/Compile population data.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
from os.path import join
cwd = os.getcwd()
data_directory = join(cwd, '..', 'Data storage')
"""
Explanation: Combine data files with state populations
The first data file has 2000-2010
End of expla... |
encima/Comp_Thinking_In_Python | Session_8/8_Recursion and Dicts.ipynb | mit | empty_dict = {}
contact_dict = {
"name": "Homer",
"email": "homer@simpsons.com",
"phone": 999
}
print(contact_dict)
"""
Explanation: Recursion and Dictionaries
Dr. Chris Gwilliams
gwilliamsc@cardiff.ac.uk
Overview
Scripts in Python
Types
Methods and Functions
Flow control
Lists
Iteration
for loops
while l... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/tensorflow_extended/labs/penguin_tfdv.ipynb | apache-2.0 | # Install the TensorFlow Extended library
!pip install -U tfx
"""
Explanation: Data validation using TFX Pipeline and TensorFlow Data Validation
Learning Objectives
Understand the data types, distributions, and other information (e.g., mean value, or number of uniques) about each feature.
Generate a preliminary schem... |
ireapps/pycar | completed/analyzing_data_with_pandas_notebook_complete.ipynb | mit | import pandas as pd
"""
Explanation: Analyzing data with Pandas
First a little setup. Importing the pandas library as pd
End of explanation
"""
%matplotlib inline
pd.set_option("max_columns", 150)
pd.set_option('max_colwidth',40)
pd.options.display.float_format = '{:,.2f}'.format
"""
Explanation: Set some helpful d... |
abatula/MachineLearningIntro | LinearRegression_Tutorial.ipynb | gpl-2.0 | import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model, datasets # Import the linear regression function and dataset from scikit-learn
from sklearn.model_selection import train_test_split, KFold
from sklearn.metrics import mean_squared_error, r2_score
# Print figures in the notebook
%matpl... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_visualize_evoked.ipynb | bsd-3-clause | import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
# sphinx_gallery_thumbnail_number = 9
"""
Explanation: Visualize Evoked data
In this tutorial we focus on plotting functions of :class:mne.Evoked.
End of explanation
"""
data_path = mne.datasets.sample.data_path()
fname = op.join(da... |
ueapy/ueapy.github.io | content/notebooks/2020-11-05-ways-of-python.ipynb | mit | from pygments import highlight
from pygments.lexers import PythonLexer
from pygments.formatters import HtmlFormatter
import IPython
"""
Explanation: Unlike other programs that have a single programming interface (matlab) or a dominant interface de jour (R with RStudio), Python has a whole ecosystem of programs for wri... |
marcelomiky/PythonCodes | codes/back_to_basics/conceitos/Lists.ipynb | mit | list1 = ['apple', 'banana', 'orange']
list1
list2 = [7, 11, 13, 17, 19]
list2
list3 = ['text', 23, 66, -1, [0, 1]]
list3
empty = []
empty
list1[0]
list1[-1]
list1[-2]
'orange' in list1
'pineapple' in list1
0 in list3
0 in list3[-1]
None in empty
66 in list3
len(list2)
len(list3)
del list2[2]
list2
new_l... |
LSSTDESC/Monitor | examples/simple_error_model.ipynb | bsd-3-clause | import os
import desc.monitor
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from lsst.sims.photUtils import calcNeff
%matplotlib inline
%load_ext autoreload
%autoreload 2
"""
Explanation: Simple Error Model for Twinkles
This notebook will calculate a simple error model for the Twinkles da... |
gjwo/nilm_gjw_data | notebooks/disaggregation-hart-active_and_reactive(normal).ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import pandas as pd
from os.path import join
from pylab import rcParams
import matplotlib.pyplot as plt
rcParams['figure.figsize'] = (13, 6)
plt.style.use('ggplot')
#import nilmtk
from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore
from nilmtk.disaggregate.hart_85 impor... |
EricChiquitoG/Simulacion2017 | Modulo1/.ipynb_checkpoints/Clase4_OsciladorArmonico-checkpoint.ipynb | mit | from IPython.display import YouTubeVideo
YouTubeVideo('k5yTVHr6V14')
"""
Explanation: ¿Cómo se mueve un péndulo?
Se dice que un sistema cualquiera, mecánico, eléctrico, neumático, etc., es un oscilador armónico si, cuando se deja en libertad fuera de su posición de equilibrio, vuelve hacia ella describiendo oscilacio... |
yangliuy/yangliuy.github.io | markdown_generator/publications.ipynb | mit | !cat publications.tsv
"""
Explanation: Publications markdown generator for academicpages
Takes a TSV of publications with metadata and converts them for use with academicpages.github.io. This is an interactive Jupyter notebook (see more info here). The core python code is also in publications.py. Run either from the m... |
taraokelly/Problem-set-Jupyter-Pyplot-and-Numpy | solutions/solution.ipynb | mit | import numpy as np
# Load in data from csv file.
sepal_length, sepal_width, petal_length, petal_width = np.genfromtxt('../data/IRIS.csv', delimiter=',', usecols=(0,1,2,3), unpack=True, dtype=float)
iris_class = np.genfromtxt('../data/IRIS.csv', delimiter=',', usecols=(4), unpack=True, dtype=str)
# Loaded the columns ... |
sisnkemp/deep-learning | intro-to-tensorflow/intro_to_tensorflow.ipynb | mit | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
print('All m... |
sassoftware/sas-viya-programming | python/python-integration-viya/SAS Viya_ CAS & Python Integration Workshop.ipynb | apache-2.0 | ## Data Management
import swat
import pandas as pd
## Data Visualization
from matplotlib import pyplot as plt
import seaborn as sns
%matplotlib inline
## Global Options
swat.options.cas.trace_actions = False # Enabling tracing of actions (Default is False. Will change to true later)
swat.options.cas.trace_ui_a... |
stephenbeckr/convex-optimization-class | Homeworks/APPM5630_HW8_helper.ipynb | mit | import numpy as np
def mySimpleSolver(f,x0,maxIters=13):
x = np.asarray(x0,dtype='float64').copy()
for k in range(maxIters):
fx = f(x)
x -= .001*x # some weird update rule, just to make something interesting happen
return x
# Let's solve this in 1D
f = lambda x : x**2
x = mySimpleSolver( f, 1 )
print(... |
mne-tools/mne-tools.github.io | dev/_downloads/b99fcf919e5d2f612fcfee22adcfc330/40_autogenerate_metadata.ipynb | bsd-3-clause | from pathlib import Path
import matplotlib.pyplot as plt
import mne
data_dir = Path(mne.datasets.erp_core.data_path())
infile = data_dir / 'ERP-CORE_Subject-001_Task-Flankers_eeg.fif'
raw = mne.io.read_raw(infile, preload=True)
raw.filter(l_freq=0.1, h_freq=40)
raw.plot(start=60)
# extract events
all_events, all_ev... |
kubeflow/code-intelligence | Issue_Embeddings/notebooks/01_AcquireData.ipynb | mit | from mdparse.parser import transform_pre_rules, compose
import pandas as pd
from tqdm import tqdm_notebook
from fastai.text.transform import defaults
"""
Explanation: Running This Notebook
This notebook should be run using the github/mdtok container on DockerHub. The Dockerfile that defines this container is located ... |
tarashor/vibrations | py/notebooks/.ipynb_checkpoints/MatricesForPlaneCorrugatedShells1-checkpoint.ipynb | mit | from sympy import *
from geom_util import *
from sympy.vector import CoordSys3D
import matplotlib.pyplot as plt
import sys
sys.path.append("../")
%matplotlib inline
%reload_ext autoreload
%autoreload 2
%aimport geom_util
# Any tweaks that normally go in .matplotlibrc, etc., should explicitly go here
%config InlineBa... |
computational-class/cjc | code/pytorch.ipynb | mit | import torch
"""
Explanation: Install
conda install pytorch torchvision -c soumith
Import
End of explanation
"""
x = torch.Tensor(5, 3)
print(x)
"""
Explanation: Tutorial
http://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html
http://pytorch.org/tutorials/
End of explanation
"""
import torch
from torch.a... |
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