repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
brclark-usgs/flopy | examples/Notebooks/flopy3_Zaidel_example.ipynb | bsd-3-clause | %matplotlib inline
import os
import sys
import platform
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import flopy
print(sys.version)
print('numpy version: {}'.format(np.__version__))
print('matplotlib version: {}'.format(mpl.__version__))
print('flopy version: {}'.format(flopy.__version_... |
diegocavalca/Studies | deep-learnining-specialization/1. neural nets and deep learning/resources/Deep Neural Network - Application v3.ipynb | cc0-1.0 | import time
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
from dnn_app_utils_v2 import *
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams[... |
WNoxchi/Kaukasos | FADL1/L7CA_lesson7-cifar10.ipynb | mit | %matplotlib inline
%reload_ext autoreload
%autoreload 2
"""
Explanation: CIFAR 10
21 Jan 2018
22 Jan 2018
End of explanation
"""
from fastai.conv_learner import *
PATH = "data/cifar10/"
os.makedirs(PATH, exist_ok=True)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
stats ... |
Tahsin-Mayeesha/Udacity-Machine-Learning-Nanodegree | projects/creating_customer_segments/customer_segments.ipynb | mit | # Import libraries necessary for this project
import numpy as np
import pandas as pd
import renders as rs
import seaborn as sns
from IPython.display import display # Allows the use of display() for DataFrames
# Show matplotlib plots inline (nicely formatted in the notebook)
%matplotlib inline
# Load the wholesale cus... |
scheib/chromium | third_party/tensorflow-text/src/docs/tutorials/bert_glue.ipynb | bsd-3-clause | #@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... |
littlewizardLI/Udacity-ML-nanodegrees | Project-practice--naive_bayes_tutorial/Naive_Bayes_tutorial.ipynb | apache-2.0 | '''
Solution
'''
import pandas as pd
# Dataset from - https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection
df = pd.read_table('smsspamcollection/SMSSpamCollection',
sep='\t',
header=None,
names=['label', 'sms_message'])
# Output printing out first 5 col... |
metpy/MetPy | v0.11/_downloads/c1a3b4ec1d09d4debc078297d433a9b2/Point_Interpolation.ipynb | bsd-3-clause | import cartopy.crs as ccrs
import cartopy.feature as cfeature
from matplotlib.colors import BoundaryNorm
import matplotlib.pyplot as plt
import numpy as np
from metpy.cbook import get_test_data
from metpy.interpolate import (interpolate_to_grid, remove_nan_observations,
remove_repeat_coo... |
sonelu/pypot | samples/notebooks/Benchmark your Poppy robot.ipynb | gpl-3.0 | from __future__ import print_function, division
from ipywidgets import interact
%pylab inline
"""
Explanation: Benchmark your Poppy robot
The goal of this notebook is to help you identify the performance of your robot and where the bottle necks are. We will measure:
* the time to read/write the position to one motor... |
ChadFulton/statsmodels | examples/notebooks/glm_weights.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import statsmodels.api as sm
"""
Explanation: Weighted Generalized Linear Models
End of explanation
"""
print(sm.datasets.fair.NOTE)
"""
Explanation: Weighted GLM: Poisson response data
Load data
In this example, we'll use the affair datas... |
ELind77/gensim | docs/notebooks/Tensorboard_visualizations.ipynb | lgpl-2.1 | import gensim
import pandas as pd
import smart_open
import random
# read data
dataframe = pd.read_csv('movie_plots.csv')
dataframe
"""
Explanation: TensorBoard Visualizations
In this tutorial, we will learn how to visualize different types of NLP based Embeddings via TensorBoard. TensorBoard is a data visualization f... |
jbannister/Stanford | Latent_Me.ipynb | mit | !git clone https://github.com/Puzer/stylegan
%cd stylegan
# Use the version this notebook was built with
!git checkout c3fb250c65840c8837ded78e34485227755c2473
!mkdir raw_images aligned_images generated_images latent_representations
"""
Explanation: <a href="https://colab.research.google.com/github/jbannister/Stanfo... |
ZoranPandovski/al-go-rithms | machine_learning/python/gradient boosted tree regressor/GBDTRegressor.ipynb | cc0-1.0 | import numpy as np
from sklearn.tree import DecisionTreeRegressor
"""
Explanation: Simple Implementation of Gradient Boosted Decision Tree For Regression
for this implementation we use squared loss divided by 2 as loss function for GBDT
$$L(y^{true}, y^{pred}) = \frac{1}{2} (y^{true} - y^{pred})^2 $$
so that our loss ... |
RTHMaK/RPGOne | scipy-2017-sklearn-master/notebooks/04 Training and Testing Data.ipynb | apache-2.0 | from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
iris = load_iris()
X, y = iris.data, iris.target
classifier = KNeighborsClassifier()
"""
Explanation: SciPy 2016 Scikit-learn Tutorial
Training and Testing Data
To evaluate how well our supervised models generalize, we can spli... |
zzsza/Datascience_School | 15. 선형 회귀 분석/02. 선형 회귀 분석의 기초.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
from sklearn.datasets import make_regression
bias = 100
X0, y, coef = make_regression(n_samples=100, n_features=1, bias=bias, noise=10, coef=True, random_state=1)
X = np.hstack([np.ones_like(X0), X0])
np.ones_like(X0)[:5] # no... |
molpopgen/fwdpy | docs/pages/popsizes.ipynb | gpl-3.0 | %matplotlib inline
%pylab inline
from __future__ import print_function
import numpy as np
import array
import matplotlib.pyplot as plt
#population size
N=1000
#nlist corresponds to a constant population size for 10N generations
#note the "dtype" argument. Without it, we'd be defaulting to int64,
#which is a 64-bit sig... |
mne-tools/mne-tools.github.io | 0.19/_downloads/2369809188e1e28fb4d0ad564cdfa36d/plot_source_space_time_frequency.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, source_band_induced_power
print(__doc__)
"""
Explanation: Compute induced power in... |
pybel/pybel-tools | notebooks/Directed, Polar Heat Diffusion.ipynb | mit | import random
import sys
import time
from abc import ABC, abstractmethod
from collections import defaultdict
from dataclasses import dataclass
from itertools import product
from typing import Optional
import matplotlib as mpl
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd... |
timothyb0912/pylogit | examples/notebooks/mlogit Benchmark--Train and Fishing.ipynb | bsd-3-clause | from collections import OrderedDict # For recording the model specification
import pandas as pd # For file input/output
import numpy as np # For vectorized math operations
import pylogit as pl # For MNL model estimation
... |
amorgun/shad-ml-notebooks | notebooks/s1-1/intro.ipynb | unlicense | a = 1 + 2
a
a + 1
_
? sum
! ps -xa | grep python
import time
%time time.sleep(1)
"""
Explanation: IPython
Python
End of explanation
"""
import numpy as np
np.array([[1,2,3], [7,1,2]])
data = np.array([1,2,3,4,5])
data
data[1:-2]
data + 1
data * 2
data * data
np.sum(data * data)
data.dot(data)
data > 2... |
bloomberg/bqplot | examples/Marks/Pyplot/HeatMap.ipynb | apache-2.0 | import numpy as np
from ipywidgets import Layout
import bqplot.pyplot as plt
from bqplot import ColorScale
"""
Explanation: Heatmap
The HeatMap mark represents a 2d matrix of values as a color image. It can be used to visualize a 2d function, or a grayscale image for instance.
HeatMap is very similar to the GridHeatMa... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/text_models/solutions/text_generation.ipynb | apache-2.0 | import os
import time
import numpy as np
import tensorflow as tf
"""
Explanation: Text generation with an RNN
Learning Objectives
Learn how to generate text using a RNN
Create training examples and targets for text generation
Build a RNN model for sequence generation using Keras Subclassing
Create a text generator a... |
quantumlib/OpenFermion | docs/tutorials/bosonic_operators.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... |
NuGrid/NuPyCEE | DOC/Capabilities/AddingDataToStellab.ipynb | bsd-3-clause | %matplotlib nbagg
import matplotlib.pyplot as plt
from NuPyCEE import stellab as st
"""
Explanation: Adding Stellar Data to STELLAB
Contributors: Christian Ritter
In construction
End of explanation
"""
s1=st.stellab()
xaxis='[Fe/H]'
yaxis='[O/Fe]'
s1.plot_spectro(fig=1,xaxis=xaxis,galaxy='carina')
plt.xlim(-4.5,1),... |
OpenWeavers/openanalysis | doc/Langauge/14 - Inheritance.ipynb | gpl-3.0 | class Person:
# Constructor
def __init__(self, name, age):
self.name = name
self.age = age
def __str__(self):
return 'name = {}\nage = {}'.format(self.name,self.age)
# Inherited or Sub class
class Employee(Person):
def __init__(self, name, age, employee_id):
... |
eriksalt/jupyter | Python Quick Reference/Collections.ipynb | mit | from collections import deque
dq = deque()
dq.append(1)
dq.append(2)
dq.appendleft(3)
dq
v = dq.pop()
v
dq.popleft()
dq
"""
Explanation: Python Collections Quick Reference
Table Of Contents
<a href="#1.-Deque">Deque</a>
<a href="#2.-Heapq">Heapq</a>
<a href="#3.-Counter">Counter</a>
1. Deque
End of explanation
"... |
jrg365/gpytorch | examples/07_Pyro_Integration/Clustered_Multitask_GP_Regression.ipynb | mit | import math
import torch
import pyro
import gpytorch
from matplotlib import pyplot as plt
%matplotlib inline
%load_ext autoreload
%autoreload 2
# this is for running the notebook in our testing framework
import os
smoke_test = ('CI' in os.environ)
"""
Explanation: Clustered Multitask GP (w/ Pyro/GPyTorch High-Level ... |
deepfield/ibis | docs/source/notebooks/tutorial/10-Adding-a-new-reduction-expression.ipynb | apache-2.0 | import ibis.expr.datatypes as dt
import ibis.expr.rules as rlz
from ibis.expr.operations import Reduction, Arg
class BitwiseAnd(Reduction):
arg = Arg(rlz.column(rlz.integer))
where = Arg(rlz.boolean, default=None)
output_type = rlz.scalar_like('arg')
"""
Explanation: Extending Ibis Part 2: Adding a New ... |
mne-tools/mne-tools.github.io | 0.19/_downloads/2b9ae87368ee06cd9589fd87e1be1d30/plot_time_frequency_mixed_norm_inverse.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
from mne.inverse_sparse import tf_mixed_n... |
danielfather7/teach_Python | SEDS_Hw/seds-hw-2-procedural-python-part-1-danielfather7/SEDS-HW2.ipynb | gpl-3.0 | import os
filename = 'HCEPDB_moldata.zip'
if os.path.exists(filename):
print('File already exists.')
else:
print("File doesn't exist.")
import requests
url = 'http://faculty.washington.edu/dacb/HCEPDB_moldata.zip'
req = requests.get(url)
assert req.status_code == 200
with open(filename, 'wb') as f:
f.wri... |
NICTA/revrand | demos/reparameterization_trick.ipynb | apache-2.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as pl
pl.style.use('ggplot')
from scipy.stats import norm
from scipy.special import expit
from scipy.integrate import quadrature
from scipy.misc import derivative
from revrand.mathfun.special import softplus
from revrand.optimize import sgd, Adam
# Ini... |
AhmetHamzaEmra/Deep-Learning-Specialization-Coursera | Neural Networks and Deep Learning/Python_Basics_With_Numpy_v3.ipynb | mit | ### START CODE HERE ### (≈ 1 line of code)
test = 'Hello World'
### END CODE HERE ###
print ("test: " + test)
"""
Explanation: Python Basics with Numpy (optional assignment)
Welcome to your first assignment. This exercise gives you a brief introduction to Python. Even if you've used Python before, this will help fami... |
ktaneishi/deepchem | examples/notebooks/Conditional_GAN.ipynb | mit | import deepchem as dc
import numpy as np
import tensorflow as tf
n_classes = 4
class_centers = np.random.uniform(-4, 4, (n_classes, 2))
class_transforms = []
for i in range(n_classes):
xscale = np.random.uniform(0.5, 2)
yscale = np.random.uniform(0.5, 2)
angle = np.random.uniform(0, np.pi)
m = [[xscale... |
probml/pyprobml | deprecated/bernoulli_hmm_example.ipynb | mit |
!pip install git+git://github.com/lindermanlab/ssm-jax-refactor.git
import ssm
"""
Explanation: <a href="https://colab.research.google.com/github/probml/probml-notebooks/blob/main/notebooks/bernoulli_hmm_example.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In... |
mspieg/principals-appmath | PolynomialFun.ipynb | cc0-1.0 | %matplotlib inline
import numpy as np
import scipy.linalg as la
import matplotlib.pyplot as plt
"""
Explanation: <table>
<tr align=left><td><img align=left src="./images/CC-BY.png">
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. ... |
seg/2016-ml-contest | MandMs/03_Facies_classification_MandMs_feature_engineering_derivatives_moments_glcms_nofacies_data.ipynb | apache-2.0 | # import data and filling missing PE values with average
filename = 'nofacies_data.csv'
training_data = pd.read_csv(filename)
training_data['PE'].fillna((training_data['PE'].mean()), inplace=True)
print np.shape(training_data)
training_data['PE'].fillna((training_data['PE'].mean()), inplace=True)
print np.shape(tra... |
gwtsa/gwtsa | examples/notebooks/9_Response function comparison.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
from scipy.special import gammainc, gammaincinv
from scipy.integrate import quad
import pandas as pd
import pastas as ps
%matplotlib inline
rain = ps.read.read_knmi('data_notebook_5/etmgeg_260.txt', variables='RH').series
evap = ps.read.read_knmi('data_notebook_5/etmg... |
ES-DOC/esdoc-jupyterhub | notebooks/cas/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', 'cas', 'sandbox-3', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: CAS
Source ID: SANDBOX-3
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turbulen... |
netodeolino/TCC | TCC 02/Resultados/Janeiro/Janeiro.ipynb | mit | all_crime_tipos.head(10)
all_crime_tipos_top10 = all_crime_tipos.head(10)
all_crime_tipos_top10.plot(kind='barh', figsize=(12,6), color='#3f3fff')
plt.title('Top 10 crimes por tipo (Jan 2017)')
plt.xlabel('Número de crimes')
plt.ylabel('Crime')
plt.tight_layout()
ax = plt.gca()
ax.xaxis.set_major_formatter(ticker.StrM... |
phoebe-project/phoebe2-docs | 2.1/tutorials/requiv_crit_semidetached.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: Critical Radii: Semidetached 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
"""
%ma... |
laurentperrinet/Khoei_2017_PLoSCB | notebooks/SI_controls.ipynb | mit | %%writefile experiment_SI_controls.py
"""
A bunch of control runs
"""
import MotionParticlesFLE as mp
gen_dot = mp.generate_dot
import numpy as np
import os
from default_param import *
image = {}
experiment = 'SI'
N_scan = 5
base = 10.
#mp.N_trials = 4
for stimulus_tag, im_arg in zip(stim_labels, stim_args):
#for st... |
GoogleCloudPlatform/training-data-analyst | blogs/sme_academy/tfx/03_train.ipynb | apache-2.0 | import tensorflow as tf
import tensorflow_data_validation as tfdv
import tensorflow_transform as tft
print('TF version: {}'.format(tf.__version__))
print('TFT version: {}'.format(tft.__version__))
print('TFDV version: {}'.format(tfdv.__version__))
PROJECT = 'cloud-training-demos' # Replace with your PROJECT
BUCKET... |
amcdawes/QMlabs | Lab 8 - Two-particle systems.ipynb | mit | import matplotlib.pyplot as plt
from numpy import sqrt,pi,sin,cos,arange
from qutip import *
"""
Explanation: Two-particle systems
An introduction to multi-particle spaces, starting with photon polarization states. This lab answers the question: How do we describe the state of two photons?
End of explanation
"""
H =... |
FISHunderscore/Pendulum-Wave | Pendulum-Wave.ipynb | gpl-3.0 | import math
from math import pi
lengthsM = []
danceDuration = 60
mostOscils = 51
# Where n is the index of the pendulum (starting at 0)
length = lambda n: 9.81 * (danceDuration / (2*pi*(mostOscils + n)))**2
for n in range(12): # 12 pendulums, indexed 0-11
lengthsM.append(length(n))
# Convert lengths to inches fo... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_cluster_stats_time_frequency.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.time_frequency import single_trial_power
from mne.stats import permutation_cluster_test
from mne.datasets import sample
print(_... |
wyndwarrior/HouseRank | DataCollection/craigslist_scraper.ipynb | mit | url_base = 'http://sfbay.craigslist.org/search/eby/apa'
params = dict(search_distance=4, postal=94720)
rsp = requests.get(url_base, params=params)
html = bs4(rsp.text, 'html.parser')
apts = html.find_all('p', attrs={'class': 'row'})
import time
cl_data = []
for i in [0,100,200,300,400,500,600,700,800,900,1000,1100]:
... |
ruleva1983/udacity-mle | boston_housing/boston_housing.ipynb | gpl-3.0 | # Import libraries necessary for this project
import numpy as np
import pandas as pd
import visuals as vs # Supplementary code
from sklearn.cross_validation import ShuffleSplit
# Pretty display for notebooks
%matplotlib inline
# Load the Boston housing dataset
data = pd.read_csv('housing.csv')
prices = data['MEDV']
f... |
nwhidden/ND101-Deep-Learning | gan_mnist/Intro_to_GANs_Exercises.ipynb | mit | %matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
"""
Explanation: Generative Adversarial Network
In this notebook, we'll be building a generativ... |
poethacker/hello | Clustering.ipynb | apache-2.0 | import warnings
warnings.filterwarnings("ignore")
from collections import Counter
import numpy as np
from scipy import stats
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn import metrics
from sklearn.metrics import pairwise_distances
from sklearn.cluster import Aggl... |
queirozfcom/python-sandbox | python3/notebooks/crimes/task.ipynb | mit | # to cluster districts by crimes, I will represent each district as a vector with the counts for each crime
# that happened in there
# but District is a floating point number
# how many different Districts are there?
districts = df.District.unique()
# ok so even though it's a float it's probably a categorical column... |
bassio/omicexperiment | omicexperiment/docs/02_experiment_filters.ipynb | bsd-3-clause | %load_ext autoreload
%autoreload 2
#Load our data
from omicexperiment.experiment.microbiome import MicrobiomeExperiment
mapping = "example_map.tsv"
biom = "example_fungal.biom"
tax = "blast_tax_assignments.txt"
exp = MicrobiomeExperiment(biom, mapping,tax)
"""
Explanation: Experiment objects filters - the rationale... |
catalystcomputing/DSIoT-Python-sessions | Session4/code/02 Decision Tree Classifier - random_state.ipynb | apache-2.0 | # Imports
from sklearn import metrics
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
# Training Data
training_raw = pd.read_table("../data/training_data.dat")
df_training = pd.DataFrame(training_raw)
# test Data
test_raw = pd.read_table("../data/test_data.dat")
df_test = pd.DataFrame(test_raw)
# t... |
stbaercom/europython2015_logging | europython_2015_logging_talk.ipynb | mit | from datetime import datetime
def my_division_p(dividend, divisor):
try:
print("Debug, Division : {}/{}".format(dividend,divisor))
result = dividend / divisor
return result
except (ZeroDivisionError, TypeError):
print("Error, Division Failed")
return None
def division_ta... |
marcelomiky/PythonCodes | Coursera/CICCP2/.ipynb_checkpoints/Curso Introdução à Ciência da Computação com Python - Parte 2-checkpoint.ipynb | mit | def cria_matriz(tot_lin, tot_col, valor):
matriz = [] #lista vazia
for i in range(tot_lin):
linha = []
for j in range(tot_col):
linha.append(valor)
matriz.append(linha)
return matriz
x = cria_matriz(2, 3, 99)
x
def cria_matriz(tot_lin, tot_col, valor):
matriz =... |
AllenDowney/ModSim | soln/chap07.ipynb | gpl-2.0 | # install Pint if necessary
try:
import pint
except ImportError:
!pip install pint
# download modsim.py if necessary
from os.path import exists
filename = 'modsim.py'
if not exists(filename):
from urllib.request import urlretrieve
url = 'https://raw.githubusercontent.com/AllenDowney/ModSim/main/'
... |
eric11/PMCnetworks | 1-scape-parse/parser-postprocessing.ipynb | mit | import sqlite3
conn = sqlite3.connect('pmcv2-full.db')
c = conn.cursor()
c.execute('''CREATE INDEX pmidix ON refs(pmid)''')
c.execute('''CREATE INDEX pmcidix ON pmcidmap(pmid)''')
c.execute('''CREATE INDEX metaix ON meta(pmid)''')
c.execute('''CREATE INDEX authorsix ON authors(pmid)''')
c.execute('''CREATE INDEX keywo... |
Silmathoron/nest-simulator | doc/model_details/noise_generator.ipynb | gpl-2.0 | import sympy
sympy.init_printing()
x = sympy.Symbol('x')
sympy.series((1-sympy.exp(-x))/(1+sympy.exp(-x)), x)
"""
Explanation: The NEST noise_generator
Hans Ekkehard Plesser, 2015-06-25
This notebook describes how the NEST noise_generator model works and what effect it has on model neurons.
NEST needs to be in your PY... |
Kaggle/learntools | notebooks/ml_explainability/raw/tut5_shap_advanced.ipynb | apache-2.0 | import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
data = pd.read_csv('../input/fifa-2018-match-statistics/FIFA 2018 Statistics.csv')
y = (data['Man of the Match'] == "Yes") # Convert from string "Yes"/"No" to binary
feature_... |
vravishankar/Jupyter-Books | Errors+and+Exceptions.ipynb | mit | print('Hello)
"""
Explanation: Errors and Exceptions
While executing a python program we may encounter errors. There are 2 types of errors:
Syntax Errors - When you don't follow the proper structure of the python program (Like missing a quote during initialising a string).
Exceptions - Sometimes even when the syntax ... |
markdewing/qmc_algorithms | Wavefunctions/Explain_Bspline.ipynb | mit | xs = Symbol('x')
knots = [0,1,2,3,4,5,6]
# Third-order bspline
sym_basis = bspline_basis_set(3, knots, xs)
# Form for one basis function
sym_basis[0]
# Plot some basis functions
nbasis_to_plot = 3
npoints_to_plot = 40
basis_y = np.zeros((nbasis_to_plot, npoints_to_plot))
xvals = np.zeros(npoints_to_plot)
for i in ran... |
ProfessorKazarinoff/staticsite | content/code/flask/sqlite_play.ipynb | gpl-3.0 | import sqlite3
db = sqlite3.connect("name_database.db")
"""
Explanation: To create a new database, we first import sqlite3 and then instantiate a new database object with the sqlite3.connect() method.
End of explanation
"""
# create a database called name_database.db
# add one table to the database called names_tabl... |
Jay-Jay-D/LeanSTP | Jupyter/KitchenSinkQuantBookTemplate.ipynb | apache-2.0 | %matplotlib inline
# Imports
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Common")
AddReference("QuantConnect.Jupyter")
AddReference("QuantConnect.Indicators")
from System import *
from QuantConnect import *
from QuantConnect.Data.Custom import *
from QuantConnect.Data.Market import Tr... |
anilcs13m/Projects | MovieReviewSentimentAnalysis/MovieReveiw/.ipynb_checkpoints/NLP_Movies-checkpoint.ipynb | gpl-2.0 | import re
from bs4 import BeautifulSoup
"""
Explanation: Natural Language Processing in a Kaggle Competition: Movie Reviews
<img src='Movie_thtr.jpg', width = 800, height = 600>
Source
I decided to try playing around with a Kaggle competition. In this case, I entered the "When bag of words meets bags of popcorn" cont... |
sassoftware/sas-viya-programming | communities/Your First CAS Connection from Python.ipynb | apache-2.0 | # Import the SWAT package which contains the CAS interface
import swat
# Create a CAS session on mycas1 port 12345
conn = swat.CAS('mycas1', 12345, 'username', 'password')
"""
Explanation: Your First CAS Connection from Python
Let's start with a gentle introduction to the Python CAS client by doing some basic operat... |
mgalardini/2017_python_course | notebooks/[3]-Exercises.ipynb | gpl-2.0 | # Import the packages that will be usefull for this part of the lesson
from collections import OrderedDict, Counter
import pandas as pd
from pprint import pprint
# Small trick to get a larger display
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:90% !important; }</style>"))
""... |
Olsthoorn/TransientGroundwaterFlow | exercises_notebooks/FirstExercise.ipynb | gpl-3.0 | import numpy as np # import numerical package and locally call it np
import matplotlib.pyplot as plt
from scipy.special import k0 # import the K0-bessel function
import scipy # import scientific package
K0 = scipy.special.k0 # assign variable name K0 to function k0
# this makes K0 and k0 point at the same memory loca... |
with-git/tensorflow | tensorflow/tools/docker/notebooks/3_mnist_from_scratch.ipynb | apache-2.0 | from __future__ import print_function
from IPython.display import Image
import base64
Image(data=base64.decodestring("iVBORw0KGgoAAAANSUhEUgAAAMYAAABFCAYAAAARv5krAAAYl0lEQVR4Ae3dV4wc1bYG4D3YYJucc8455yCSSIYrBAi4EjriAZHECyAk3rAID1gCIXGRgIvASIQr8UTmgDA5imByPpicTcYGY+yrbx+tOUWpu2e6u7qnZ7qXVFPVVbv2Xutfce+q7hlasmTJktSAXrnn8... |
chinmaymk/machine-learning-experiments | 03-adult-income-by-census.ipynb | mit | def read_data(path):
return pd.read_csv(path,
index_col=False,
skipinitialspace=True,
names=['age', 'workclass', 'fnlwgt', 'education', 'education_num',
'marital_status', 'occupation', 'relationship', 'race', 'sex',
... |
Kaggle/learntools | notebooks/data_viz_to_coder/raw/tut3.ipynb | apache-2.0 | #$HIDE$
import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
print("Setup Complete")
"""
Explanation: Now that you can create your own line charts, it's time to learn about more chart types!
By the way, if this is your first experi... |
kit-cel/wt | qc/basic_concepts_Python.ipynb | gpl-2.0 | # defining lists
sport_list = [ 'cycling', 'football', 'fitness' ]
first_prime_numbers = [ 2, 3, 5, 7, 11, 13, 17, 19 ]
# getting contents
sport = sport_list[ 2 ]
third_prime = first_prime_numbers[ 2 ]
# printing
print( 'All sports:', sport_list )
print( 'Sport to be done:', sport )
print( '\nFirst primes:', first_p... |
weissmanlab/magic | magicplots.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import magic
"""
Explanation: Setup
Import packages and modules
End of explanation
"""
stats = ['your_prefix1', 'your_prefix2', 'your_prefix3']
# Example: ['chr1_pair', 'chr1_tbl', 'chr2_pair', 'itip']
"""
Explanation: Change t... |
jsharpna/DavisSML | 2018_material/labs/lab1-soln.ipynb | mit | # Import the necessary packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import LeaveOneOut
from sklearn import linear_model, neighbors
%matplotlib inline
plt.style.use('ggplot')
# Where to save the figures
PROJECT_ROOT_DIR = ".."
datapath = PROJECT_ROOT_DIR +... |
zklgame/CatEyeNets | test/BatchNormalization.ipynb | mit | import os
os.chdir(os.getcwd() + '/..')
# Run some setup code for this notebook
import random
import numpy as np
import matplotlib.pyplot as plt
from utils.data_utils import get_CIFAR10_data
from utils.metrics_utils import rel_error
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size o... |
certik/climate | RSS.ipynb | mit | #!wget http://www.remss.com/data/msu/data/netcdf/uat4_tb_v03r03_avrg_chTLT_197812_201308.nc3.nc
#!mv uat4_tb_v03r03_avrg_chTLT_197812_201308.nc3.nc data/
#!wget http://www.remss.com/data/msu/data/netcdf/uat4_tb_v03r03_anom_chTLT_197812_201308.nc3.nc
#!mv uat4_tb_v03r03_anom_chTLT_197812_201308.nc3.nc data/
"""
Explan... |
sergivalverde/cnn-ms-lesion-segmentation | example_1.ipynb | gpl-3.0 | %load_ext autoreload
%autoreload 2
import os
from collections import OrderedDict
from base import *
from build_model_nolearn import cascade_model
from config import *
"""
Explanation: Multiple Sclerosis (MS) lesion segmentation of MRI images using a cascade of two 3D convolutional neural networks
This script assumes... |
Kaggle/learntools | notebooks/computer_vision/raw/ex5.ipynb | apache-2.0 | # Setup feedback system
from learntools.core import binder
binder.bind(globals())
from learntools.computer_vision.ex5 import *
# Imports
import os, warnings
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing import image_datas... |
sueiras/training | tensorflow/02-text/01-char_languaje_model/Text_generation_with_Quijote.ipynb | gpl-3.0 | # Header
from __future__ import print_function
import numpy as np
import tensorflow as tf
print('Tensorflow version: ', tf.__version__)
import time
#Show images
import matplotlib.pyplot as plt
%matplotlib inline
# plt configuration
plt.rcParams['figure.figsize'] = (10, 10) # size of images
plt.rcParams['image.... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/machine_learning_in_the_enterprise/labs/model_monitoring.ipynb | apache-2.0 | import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = "--user"
# Import necessa... |
kevinjliang/Duke-Tsinghua-MLSS-2017 | 01B_TensorFlow_Fundamentals.ipynb | apache-2.0 | import tensorflow as tf
"""
Explanation: The TensorFlow Tutorial I wish I had
This notebook is adapted from notes that I took when learning TensorFlow. It provides a slow, thorough introduction to the fundamentals of TensorFlow, answering questions like: What exactly is TensorFlow? Why do we need it? How does the comp... |
lrq3000/unireedsolomon | Generating the exponent and log tables.ipynb | mit | generator = ff.GF2int(3)
generator
"""
Explanation: I used 3 as the generator for this field. For a field defined with the polynomial x^8 + x^4 + x^3 + x + 1, there may be other generators (I can't remember)
End of explanation
"""
generator*generator
generator*generator*generator
generator**1
generator**2
genera... |
liangjg/openmc | examples/jupyter/hexagonal-lattice.ipynb | mit | %matplotlib inline
import openmc
fuel = openmc.Material(name='fuel')
fuel.add_nuclide('U235', 1.0)
fuel.set_density('g/cm3', 10.0)
fuel2 = openmc.Material(name='fuel2')
fuel2.add_nuclide('U238', 1.0)
fuel2.set_density('g/cm3', 10.0)
water = openmc.Material(name='water')
water.add_nuclide('H1', 2.0)
water.add_nuclide... |
LorenzoBi/courses | TSAADS/tutorial 2/.ipynb_checkpoints/Untitled-checkpoint.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import scipy.io as sio
from sklearn import datasets, linear_model
%matplotlib inline
def set_data(p, x):
temp = x.flatten()
n = len(temp[p:])
x_T = temp[p:].reshape((n, 1))
X_p = np.ones((n, p + 1))
for i in range(1, p + 1):
X_p[:, i] = tem... |
UWSEDS/LectureNotes | Fall2018/09_UnitTests/unit-tests.ipynb | bsd-2-clause | import numpy as np
# Code Under Test
def entropy(ps):
items = ps * np.log(ps)
return np.abs(-np.sum(items))
# Smoke test
entropy([0.2, 0.8])
"""
Explanation: Unit Tests
Overview and Principles
Testing is the process by which you exercise your code to determine if it performs as expected. The code you are test... |
NuSTAR/nustar_pysolar | notebooks/Convert_Example.ipynb | mit | import sys
from os.path import *
import os
# For loading the NuSTAR data
from astropy.io import fits
# Load the NuSTAR python libraries
from nustar_pysolar import convert, utils
"""
Explanation: Code for converting an observation to solar coordinates
Step 1: Run the pipeline on the data to get mode06 files with the... |
tensorflow/docs-l10n | site/ja/addons/tutorials/layers_weightnormalization.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is d... |
rudyryk/LearnAI | notebooks/2_fullyconnected.ipynb | unlicense | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import os
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
"""
Explanation: Deep Learning
Assignment 2
Previou... |
m3at/Labelizer | Labelizer_part1.ipynb | mit | #%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
%load_ext watermark
# for reproducibility
%watermark -a 'Paul Willot' -mvp numpy,scipy,spacy
"""
Explanation: Extracting Structure from Scientific Abstracts
using a LSTM neural network
Paul Willot
This project was made for the ICADL 20... |
ledeprogram/algorithms | class7/donow/hon_jingyi_donow_7.ipynb | gpl-3.0 | import pandas as pd
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression
"""
Explanation: Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the data, plot, and create a logistic regressi... |
zhuanxuhit/deep-learning | embeddings/Skip-Grams-Solution.ipynb | mit | import time
import numpy as np
import tensorflow as tf
import utils
"""
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language p... |
mne-tools/mne-tools.github.io | 0.21/_downloads/2567f25ca4c6b483c12d38184d7fe9d7/plot_decoding_xdawn_eeg.ipynb | bsd-3-clause | # Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import StratifiedKFold
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import c... |
gcallah/Indra | notebooks/Agent.ipynb | gpl-3.0 | cd ..
from indra2.agent import Agent
"""
Explanation: Indra Agent Class
agent.py is the base class of all agents, environments, and objects contained in an environment.
Its basic character is that it is a vector, and supports basic
vector and matrix operations.
End of explanation
"""
def newt_action(agent):
pri... |
Jackporter415/phys202-2015-work | assignments/midterm/InteractEx06.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import Image
from IPython.html.widgets import interact, interactive, fixed
"""
Explanation: Interact Exercise 6
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.
End of explan... |
ewulczyn/talk_page_abuse | src/analysis/Characterizing Attackers and Victims.ipynb | apache-2.0 | %load_ext autoreload
%autoreload 2
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
from load_utils import *
from analysis_utils import compare_groups,get_genders
d = load_diffs()
df_events, df_blocked_use... |
mne-tools/mne-tools.github.io | 0.20/_downloads/075ba1175413b0aa0dc66e721f312729/plot_mixed_norm_inverse.ipynb | bsd-3-clause | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.inverse_sparse import mixed_norm, make_stc_from_dipoles
from mne.minimum_norm import make_inverse... |
grokkaine/biopycourse | day2/.ipynb_checkpoints/ML_regression-checkpoint.ipynb | cc0-1.0 | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
diabetes = datasets.load_diabetes()
#diabetes
print(diabetes.data.shape, diabetes.target.shape)
print(diabetes.data[:5,:3])
print(diabetes.target)
ft = 2 # feature type 0 - age, 1 - sex, 2 - bmi
X = diab... |
stevenydc/2015lab1 | hw0.ipynb | mit | import sys
print sys.version
"""
Explanation: Homework 0
Survey due 4th September, 2015
Submission due 10th September, 2015
Welcome to CS109 / STAT121 / AC209 / E-109 (http://cs109.org/). In this class, we will be using a variety of tools that will require some initial configuration. To ensure everything goes smooth... |
charlesreid1/rejoyce | Lestrygonians Part 4.ipynb | mit | %matplotlib inline
import nltk, re, io
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib.pylab import *
txtfile = 'txt/08lestrygonians.txt'
from nltk.tokenize import RegexpTokenizer
tokenizer = RegexpTokenizer(r'\w+')
with io.open(txtfile) as f:
tokens = tokenizer.tokenize(f.read())
p... |
UWSEDS/LectureNotes | Spring2019/02_Procedural_Python/Procedural Programming.ipynb | bsd-2-clause | instructors = ['Dave', 'Joe', 'Bernease', 'Dorkus the Clown']
instructors
"""
Explanation: Procedural programming in python
Topics
Flow control, part 1
If
For
range() function
Some hacky hack time
Exercises
<hr>
<hr>
Review of Data Types
| type | description |
|------|------------|
| primitive | int, float, string,... |
owlas/magpy | docs/source/notebooks/two-particle-equilibrium.ipynb | bsd-3-clause | import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
from tqdm import tqdm_notebook
#import tqdm
import magpy as mp
%matplotlib inline
"""
Explanation: Two particle equilibrium
If you haven't read the One particle equilibrium notebook yet, go and read it now.
In the previous noteb... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive/05_review/6_deploy.ipynb | apache-2.0 | PROJECT = "cloud-training-demos" # Replace with your PROJECT
BUCKET = "cloud-training-bucket" # Replace with your BUCKET
REGION = "us-central1" # Choose an available region for Cloud MLE
TFVERSION = "1.14" # TF version for CMLE to use
import os
os.environ["BUCKET"] = BUCKET
os.environ["PROJ... |
empet/PSCourse | MarkovChains.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import networkx as nx
"""
Explanation: Lanturi Markov ireductibile si aperiodice
End of explanation
"""
def GraphTr(Q):
G=nx.from_numpy_matrix(Q, create_using=nx.DiGraph())
nx.draw(G, node_color='b', alpha=0.3)
"""
Explanation: Definim o ... |
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