repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
dariox2/CADL | session-5/session-5-part-1[1-3].ipynb | apache-2.0 | # First check the Python version
import sys
if sys.version_info < (3,4):
print('You are running an older version of Python!\n\n',
'You should consider updating to Python 3.4.0 or',
'higher as the libraries built for this course',
'have only been tested in Python 3.4 and higher.\n')
... |
cwharland/data-science-from-scratch | Clustering.ipynb | mit | class KMeans:
"""k-means algo"""
def __init__(self, k):
self.k = k # number of clusters
self.means = None # means of clusters
def classify(self, input):
"""return the index of the cluster to closest to input"""
return min(range(self.k),
key = l... |
turbomanage/training-data-analyst | courses/machine_learning/deepdive2/text_classification/solutions/rnn_encoder_decoder.ipynb | apache-2.0 | import os
import pickle
import sys
import nltk
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras.layers import (
Dense,
Embedding,
GRU,
Input,
)
from tensorflow.keras.models import (
load_model,
Model,
)
im... |
Jackporter415/phys202-2015-work | assignments/assignment05/InteractEx03.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
"""
Explanation: Interact Exercise 3
Imports
End of explanation
"""
def soliton(x, t, c, a):
"""Return phi(x, t) for a soliton wave with co... |
CUBoulder-ASTR2600/lectures | lecture_11_arrays_plotting.ipynb | isc | %matplotlib inline
"""
Explanation: Plotting Arrays Using matplotlib
End of explanation
"""
import numpy as np
import matplotlib.pyplot as pl # import this for plotting routines
"""
Explanation: The argument after the ipython magic is called the backend for plotting. There are several available, also for creating t... |
hpparvi/PyTransit | notebooks/roadrunner/roadrunner_model_example_3.ipynb | gpl-2.0 | %pylab inline
rc('figure', figsize=(13,6))
def plot_lc(time, flux, c=None, ylim=(0.9865, 1.0025), ax=None, alpha=1):
if ax is None:
fig, ax = subplots()
else:
fig, ax = None, ax
ax.plot(time, flux, c=c, alpha=alpha)
ax.autoscale(axis='x', tight=True)
setp(ax, xlabel='Time [d]', ylab... |
dereneaton/ipyrad | tests/API_user-guide.ipynb | gpl-3.0 | import ipyrad as ip
"""
Explanation: User guide to the ipyrad API
Welcome! This tutorial will introduce you to the basic and advanced features of working with the ipyrad API to assemble RADseq data in Python. The API offers many advantages over the command-line interface, but requires a little more work up front to le... |
sz2472/foundations-homework | data and database/Homework_4_database_shengyingzhao.ipynb | mit | numbers_str = '496,258,332,550,506,699,7,985,171,581,436,804,736,528,65,855,68,279,721,120'
"""
Explanation: Homework #4
These problem sets focus on list comprehensions, string operations and regular expressions.
Problem set #1: List slices and list comprehensions
Let's start with some data. The following cell contain... |
johnnyliu27/openmc | examples/jupyter/pandas-dataframes.ipynb | mit | import glob
from IPython.display import Image
import matplotlib.pyplot as plt
import scipy.stats
import numpy as np
import pandas as pd
import openmc
%matplotlib inline
"""
Explanation: This notebook demonstrates how systematic analysis of tally scores is possible using Pandas dataframes. A dataframe can be automati... |
ES-DOC/esdoc-jupyterhub | notebooks/pcmdi/cmip6/models/sandbox-2/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'pcmdi', 'sandbox-2', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: PCMDI
Source ID: SANDBOX-2
Topic: Atmos
Sub-Topics: Dynamical Core, Radiation, Turb... |
jjdblast/RoadTrafficSimulator | experiments/report.ipynb | mit | data = pd.read_table("./1.data", sep=" ")
plt.plot(data['multiplier'], data['avg_speed'], '-o')
"""
Explanation: Запустим симулятор с фиксированными значениями времени переключения светофоров.
End of explanation
"""
data = pd.read_table("./2.data", sep=" ")
plt.plot(data['it'], data['avg_speed'], '-o')
"""
Explanat... |
buruzaemon/natto-py | notebooks/02_わかち書き.ipynb | bsd-2-clause | from natto import MeCab
text = "卓球に人生かけるなんて、気味悪いです。"
wakati = MeCab("-Owakati")
"""
Explanation: わかち書き Parsing
-O オプション
natto-py を利用して文章にある語の区切りに空白を挟んで、わかち書き出力ができます。
End of explanation
"""
wakati.parse(text)
"""
Explanation: 文字列として出力
mecab の -O オプションを利用して wakati 出力を指定して返り値を文字列にする方法です。MeCab インスタンスを取得する際に下記の通り出力フォー... |
tensorflow/docs-l10n | site/ko/agents/tutorials/9_c51_tutorial.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... |
Neuroglycerin/neukrill-net-work | notebooks/model_run_and_result_analyses/Revisiting alexnet based experiment (small).ipynb | mit | tr = np.array(model.monitor.channels['valid_y_y_1_nll'].time_record) / 3600.
fig = plt.figure(figsize=(12,8))
ax1 = fig.add_subplot(111)
ax1.plot(model.monitor.channels['valid_y_y_1_nll'].val_record)
ax1.plot(model.monitor.channels['train_y_y_1_nll'].val_record)
ax1.set_xlabel('Epochs')
ax1.legend(['Valid', 'Train'])
a... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/.ipynb_checkpoints/Object Oriented Programming-checkpoint.ipynb | apache-2.0 | l = [1,2,3]
"""
Explanation: Object Oriented Programming
Object Oriented Programming (OOP) tends to be one of the major obstacles for beginners when they are first starting to learn Python.
There are many,many tutorials and lessons covering OOP so feel free to Google search other lessons, and I have also put some link... |
weleen/mxnet | example/notebooks/moved-from-mxnet/cifar10-recipe.ipynb | apache-2.0 | import mxnet as mx
import logging
import numpy as np
# setup logging
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
"""
Explanation: CIFAR-10 Recipe
In this notebook, we will show how to train a state-of-art CIFAR-10 network with MXNet and extract feature from the network.
This example wiil cover
Networ... |
AaronCWong/phys202-2015-work | assignments/assignment04/MatplotlibExercises.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Visualization 1: Matplotlib Basics Exercises
End of explanation
"""
x = np.random.randn(100)
y = np.random.randn(100)
plt.scatter(x,y, s = 20, c = 'b')
plt.xlabel('Random Number 2')
plt.ylabel('Random Number')
plt.title('Random 2d... |
locie/locie_notebook | base_python/multiprocessing.ipynb | lgpl-3.0 | import multiprocessing as mp
from time import sleep
def a_long_running_function(time):
sleep(time)
return time
# These lines are not blocking
process = mp.Process(target=a_long_running_function, args=(10, ))
process.start()
print(f"before join, process.is_alive: {process.is_alive()}")
# These one will block ... |
ocean-color-ac-challenge/evaluate-pearson | evaluation-participant-c.ipynb | apache-2.0 | w_412 = 0.56
w_443 = 0.73
w_490 = 0.71
w_510 = 0.36
w_560 = 0.01
"""
Explanation: E-CEO Challenge #3 Evaluation
Weights
Define the weight of each wavelength
End of explanation
"""
run_id = '0000000-150630000034908-oozie-oozi-W'
run_meta = 'http://sb-10-16-10-55.dev.terradue.int:50075/streamFile/ciop/run/participant-... |
DJCordhose/ai | notebooks/workshops/d2d/nn-intro.ipynb | mit | import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import matplotlib.pylab as plt
import numpy as np
from distutils.version import StrictVersion
import sklearn
print(sklearn.__version__)
assert StrictVersion(sklearn.__version__ ) >= StrictVersion('0.18.1')
import tensorflow as tf
t... |
kaysg/NLPatelier | libexp_spaCy/libexp_spaCy.ipynb | gpl-3.0 | import spacy
nlp = spacy.load('en')
text = u"We are living in Singapore.\nIt's blazing outside today!\n"
doc = nlp(text)
for token in doc:
print((token.text, token.lemma, token.tag, token.pos))
for token in doc:
print((token.text, token.lemma_, token.tag_, token.pos_)) # lemma means *root form*
"""
Explan... |
jjonte/udacity-deeplearning-nd | py3/project-4/dlnd_language_translation.ipynb | unlicense | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
AlbanoCastroSousa/RESSPyLab | examples/UVC_Calibration_Example_1.ipynb | mit | import RESSPyLab as rpl
import numpy as np
"""
Explanation: Updated Voce-Chaboche Model Fitting Example 1
An example of fitting the updated Voce-Chaboche (UVC) model to a set of test data is provided.
Documentation for all the functions used in this example can be found by either looking at docstrings for any of the f... |
napjon/krisk | notebooks/declarative-visualization.ipynb | bsd-3-clause | # Use this when you want to nbconvert the notebook (used by nbviewer)
from krisk import init_notebook; init_notebook()
from krisk import Chart
chart = Chart()
chart
"""
Explanation: You can use krisk for Declarative Visualization. You don't have to use krisk.plot package, and directly use Chart class to make any cha... |
bjsmith/motivation-simulation | test-jupyter-widgets-clone2.ipynb | gpl-3.0 | from matplotlib.pyplot import figure, plot, xlabel, ylabel, title, show
from IPython.display import display
text = widgets.FloatText()
floatText = widgets.FloatText(description='MyField',min=-5,max=5)
floatSlider = widgets.FloatSlider(description='MyField',min=-5,max=5)
#https://ipywidgets.readthedocs.io/en/stable/... |
marius311/cosmoslik | cosmoslik_plugins/likelihoods/spt_lowl/spt_lowl.ipynb | gpl-3.0 | %pylab inline
from cosmoslik import *
"""
Explanation: South Pole Telescope low-$\ell$
This plugin implements the South Pole Telescope likelihood from Story et al. (2012) and Keisler et al. (2011). The data comes included with this plugin and was downloaded from here and here, respectively.
You can choose which likel... |
jmhsi/justin_tinker | data_science/courses/Transforms with Pytorch and Torchsample.ipynb | apache-2.0 | # some imports we will need
import os
import matplotlib.pyplot as plt
import torch as th
from torchvision import datasets
%matplotlib inline
"""
Explanation: Overview
I will go over the following topics using the pytorch and torchsample packages:
Dataset Creation and Loading
How you create pytorch-suitable datasets... |
kimkipyo/dss_git_kkp | 통계, 머신러닝 복습/160517화_4일차_시각화 Visualization/3.seaborn 시각화 패키지 소개.ipynb | mit | sns.set() #스타일이 정해짐
sns.set_color_codes()
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)
f, axarr = plt.subplots(2, sharex=True)
axarr[0].plot(x, y)
axarr[0].set_title('Sharing X axis')
axarr[1].scatter(x, y);
"""
Explanation: seaborn 시각화 패키지 소개
seaborn은 matplotlib을 기반으로 다양한 색상 테마와 통계용 챠트 등의 기능을 추가한 시각화 패키지이... |
farfan92/SpringBoard- | statistics project 1/.ipynb_checkpoints/cfarfan_statistics_exercise_1-checkpoint.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import scipy.stats as st
from scipy.stats import norm
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(color_codes=True)
from IPython.core.display import HTML
css = open('style-table.css').read() + open('style-notebook.css').read()
HTML('<style>{}</... |
sraejones/phys202-2015-work | days/day19/FittingModels.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy import optimize as opt
from IPython.html.widgets import interact
"""
Explanation: Fitting Models
Learning Objectives: learn to fit models to data using linear and non-linear regression.
This material is licensed under the MIT license and... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_python_intro.ipynb | bsd-3-clause | a = 3
print(type(a))
b = [1, 2.5, 'This is a string']
print(type(b))
c = 'Hello world!'
print(type(c))
"""
Explanation: Introduction to Python
Python is a modern, general-purpose, object-oriented, high-level programming
language. First make sure you have a working python environment and
dependencies (see install_pytho... |
jupyter/nbgrader | nbgrader/docs/source/user_guide/submitted/hacker/ps1/problem1.ipynb | bsd-3-clause | NAME = "Alyssa P. Hacker"
COLLABORATORS = "Ben Bitdiddle"
"""
Explanation: Before you turn this problem in, make sure everything runs as expected. First, restart the kernel (in the menubar, select Kernel$\rightarrow$Restart) and then run all cells (in the menubar, select Cell$\rightarrow$Run All).
Make sure you fill i... |
clubmliimas/cancer | notebooks/sentdex.ipynb | mit | import dicom # for reading dicom files
import os # for doing directory operations
import pandas as pd # for some simple data analysis (right now, just to load in the labels data and quickly reference it)
# Change this to wherever you are storing your data:
# IF YOU ARE FOLLOWING ON KAGGLE, YOU CAN ONLY PLAY WITH THE ... |
AEW2015/PYNQ_PR_Overlay | Pynq-Z1/notebooks/examples/pmod_grove_tmp.ipynb | bsd-3-clause | from pynq.pl import Overlay
Overlay("base.bit").download()
"""
Explanation: Grove Temperature Sensor 1.2
This example shows how to use the Grove Temperature Sensor v1.2 on the Pynq-Z1 board. You will also see how to plot a graph using matplotlib. The Grove Temperature sensor produces an analog signal, and requires an ... |
lilleswing/deepchem | examples/tutorials/27_Using_Reinforcement_Learning_to_Play_Pong.ipynb | mit | !curl -Lo conda_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import conda_installer
conda_installer.install()
!/root/miniconda/bin/conda info -e
!pip install --pre deepchem
import deepchem
deepchem.__version__
!pip install 'gym[atari]'
"""
Explanation: Tutorial Par... |
alexandrejaguar/strata-sv-2015-tutorial | resources/Vis1.ipynb | bsd-3-clause | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Visualization 1: Matplotlib Basics
Imports
The following imports should be used in all of your notebooks where Matplotlib in used:
End of explanation
"""
t = np.linspace(0,4*np.pi,100)
plt.plot(t, np.sin(t))
plt.xlabel('Time')
plt... |
jupyter/nbgrader | nbgrader/docs/source/user_guide/managing_the_database.ipynb | bsd-3-clause | %%bash
# remove the existing database, to start fresh
rm gradebook.db
"""
Explanation: Managing the database
Most of the important information that nbgrader has access to---information about students, assignments, grades, etc.---is stored in the nbgrader database. Much of this is added to the database automatically b... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session05/Day1/ReIntroductionToImageProcessing.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
"""
Explanation: (Re)Introduction to Image Processing
Version 0.1
During Session 1 of the DSFP, Robert Lupton provided a problem that brilliantly introduced some of the basic challenges associated with measuring the flux of a point source. As suc... |
CAChemE/curso-python-datos | notebooks/005_SWC_defensive_programming.ipynb | bsd-3-clause | # This code has an intentional error. You can type it directly or
# use it for reference to understand the error message below.
def favorite_ice_cream():
ice_creams = [
"chocolate",
"vanilla",
"strawberry"
]
print(ice_creams[3])
favorite_ice_cream()
# Syntax error
def some_function... |
briennakh/BIOF509 | Wk07/Wk07_solutions.ipynb | mit | def plot_arm_frequency(simulation, ax, marker='.', linestyle='', color='k', label=''):
"""Plot the frequency with which the second arm is chosen
NOTE: Currently only works for two arms"""
ax.plot(simulation.arm_choice.mean(axis=0),
marker=marker, linestyle=linestyle, color=color, label=label)
... |
jlandmann/oggm | docs/notebooks/getting_started.ipynb | gpl-3.0 | import oggm
from oggm import cfg
from oggm.utils import get_demo_file
cfg.initialize()
srtm_f = get_demo_file('srtm_oetztal.tif')
rgi_f = get_demo_file('rgi_oetztal.shp')
print(srtm_f)
"""
Explanation: <img src="https://raw.githubusercontent.com/OGGM/oggm/master/docs/_static/logo.png" width="40%" align="left">
Gettin... |
laurajchang/NPTFit | examples/Example3_Running_Poissonian_Scans.ipynb | mit | # Import relevant modules
%matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import corner
import matplotlib.pyplot as plt
from NPTFit import nptfit # module for performing scan
from NPTFit import create_mask as cm # module for creating the mask
from NPTFit import dnds_analysis # module for ana... |
MehtapIsik/assaytools | examples/direct-fluorescence-assay/2 MLE fit for two component binding - simulated and real data.ipynb | lgpl-2.1 | import numpy as np
import matplotlib.pyplot as plt
from scipy import optimize
import seaborn as sns
%pylab inline
"""
Explanation: MLE fit for two component binding - simulated and real data
In part one of this notebook we see how well we can reproduce Kd from simulated experimental data with a maximum likelihood fu... |
tensorflow/docs-l10n | site/ko/probability/examples/Eight_Schools.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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, sof... |
getsmarter/bda | module_4/M4_NB3_NetworkClustering.ipynb | mit | import networkx as nx
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pylab as plt
from networkx.drawing.nx_agraph import graphviz_layout
from collections import defaultdict, Counter
import operator
## For hierarchical clustering.
from scipy.cluster import hierarchy
from scipy.spatial imp... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/sdk/sdk_automl_image_object_detection_online.ipynb | apache-2.0 | import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex SDK: AutoML training image object detection model for online prediction
<table align="le... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/end_to_end_ml/solutions/deploy_keras_ai_platform_babyweight.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
import os
"""
Explanation: Deploy and predict with Keras model on Cloud AI Platform.
Learning Objectives
Setup up the environment
Deploy trained Keras model to Cloud AI Platform
Online predict from model on Cloud AI Platform
Batch predict from model ... |
dusenberrymw/systemml | samples/jupyter-notebooks/Deep Learning Image Classification.ipynb | apache-2.0 | from systemml import MLContext, dml
ml = MLContext(sc)
print "Spark Version:", sc.version
print "SystemML Version:", ml.version()
print "SystemML Built-Time:", ml.buildTime()
from sklearn import datasets
from sklearn.cross_validation import train_test_split
from sklearn.metrics import classification_report
import pa... |
tpin3694/tpin3694.github.io | python/repr_vs_str.ipynb | mit | import datetime
"""
Explanation: Title: repr vs. str
Slug: repr_vs_str
Summary: repr vs. str in Python.
Date: 2016-01-23 12:00
Category: Python
Tags: Basics
Authors: Chris Albon
Interesting in learning more? Check out Fluent Python
Preliminaries
End of explanation
"""
class Regiment(object):
def __init__(... |
rishuatgithub/MLPy | torch/PYTORCH_NOTEBOOKS/03-CNN-Convolutional-Neural-Networks/01-MNIST-with-CNN.ipynb | apache-2.0 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from torchvision.utils import make_grid
import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
%matplotlib... |
hasecbinusr/pysal | pysal/contrib/spint/notebooks/ODW_example.ipynb | bsd-3-clause | origins = ps.weights.lat2W(4,4)
dests = ps.weights.lat2W(4,4)
origins.n
dests.n
ODw = ODW(origins, dests)
print ODw.n, 16*16
ODw.full()[0].shape
"""
Explanation: With an equal number of origins and destinations (n=16)
End of explanation
"""
origins = ps.weights.lat2W(3,3)
dests = ps.weights.lat2W(5,5)
origins... |
sdpython/ensae_teaching_cs | _doc/notebooks/td2a_ml/ml_scikit_learn_simple_correction.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
"""
Explanation: Rappels sur scikit-learn et le machine learning (correction)
Quelques exercices simples sur scikit-learn. Le notebook est long pour ceux qui débutent en machine learning et sans doute sans suspens pour ceux qui en ont ... |
nwjs/chromium.src | third_party/tensorflow-text/src/docs/guide/text_tf_lite.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... |
mitdbg/modeldb | client/workflows/demos/registry/sklearn-census-end-to-end.ipynb | mit | from __future__ import print_function
import warnings
from sklearn.exceptions import ConvergenceWarning
warnings.filterwarnings("ignore", category=ConvergenceWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
import itertools
import os
import time
import six
import numpy as np
import pandas as pd
i... |
raman-sharma/stanford-mir | basic_feature_extraction.ipynb | mit | kick_filepaths, snare_filepaths = stanford_mir.download_drum_samples()
"""
Explanation: ← Back to Index
Basic Feature Extraction
Somehow, we must extract the characteristics of our audio signal that are most relevant to the problem we are trying to solve. For example, if we want to classify instruments by timbre,... |
ALEXKIRNAS/DataScience | Coursera/Machine-learning-data-analysis/Course 5/Week_01/salary.ipynb | mit | plt.figure(figsize(15,10))
sm.tsa.seasonal_decompose(salary.WAG_C_M).plot()
print("Критерий Дики-Фуллера: p=%f" % sm.tsa.stattools.adfuller(salary.WAG_C_M)[1])
"""
Explanation: Проверка стационарности и STL-декомпозиция ряда:
End of explanation
"""
salary['salary_box'], lmbda = stats.boxcox(salary.WAG_C_M)
plt.figur... |
deculler/DataScienceTableDemos | BirthweightRegression.ipynb | bsd-2-clause | # HIDDEN
from datascience import *
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plots
plots.style.use('fivethirtyeight')
"""
Explanation: Illustration of datascience Tables for multivariate analysis
David E. Culler
This notebook illustrates some of the use of datascience Tables to perform regressi... |
pligor/predicting-future-product-prices | 02_preprocessing/exploration02-price_history-remove-spikes.ipynb | agpl-3.0 | axis_indifferent = np.arange(len(df.columns))
axis_indifferent[:4]
plt.figure(figsize=(17,8))
for ind, history in df.loc[price_histories_big_outliers.index].iterrows():
#nums = [float(str) for str in history.values]
#print history.values
plt.plot(axis_indifferent, history.values)
plt.title('Original Price ... |
intel-analytics/BigDL | python/chronos/use-case/fsi/stock_prediction_prophet.ipynb | apache-2.0 | import numpy as np
import pandas as pd
import os
FILE_NAME = 'all_stocks_5yr.csv'
filepath = os.path.join('data', FILE_NAME)
print(filepath)
# read data
data = pd.read_csv(filepath)
print(data[:10])
# change input column name
data = data[data['Name']=='MMM'].rename(columns={"date":"ds", "close":"y"})
data.head()
... |
liufuyang/deep_learning_tutorial | course-deeplearning.ai/course4-cnn/week4-facenet-nstyle/FaceRecognition/Face+Recognition+for+the+Happy+House+-+v3.ipynb | mit | from keras.models import Sequential
from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate
from keras.models import Model
from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import MaxPooling2D, AveragePooling2D
from keras.layers.merge import Concatenate
from kera... |
dsacademybr/PythonFundamentos | Cap05/Notebooks/DSA-Python-Cap05-04-Heranca.ipynb | gpl-3.0 | # Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
"""
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 5</font>
Download: http://github.com/dsacademybr
End of explanation
"""
# Crian... |
syednasar/talks | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, we’re going ... |
ES-DOC/esdoc-jupyterhub | notebooks/csir-csiro/cmip6/models/sandbox-2/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csir-csiro', 'sandbox-2', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: CSIR-CSIRO
Source ID: SANDBOX-2
Sub-Topics: Radiative Forcings.
Propert... |
mbeyeler/opencv-machine-learning | notebooks/09.04-Training-an-MLP-in-OpenCV-to-Classify-Handwritten-Digits.ipynb | mit | from keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
"""
Explanation: <!--BOOK_INFORMATION-->
<a href="https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv" target="_blank"><img align="left" src="data/cover.jpg" style="width: 76px; height: 100px; back... |
jonathanrocher/pandas_tutorial | climate_timeseries/climate_timeseries-Part2.ipynb | mit | %matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.set_option("display.max_rows", 16)
LARGE_FIGSIZE = (12, 8)
# Change this cell to the demo location on YOUR machine
%cd ~/Projects/pandas_tutorial/climate_timeseries/
%ls
"""
Explanation: Last updated: June 29th 2016
Climat... |
mspcvsp/cincinnati311Data | ClusterServiceCodes.ipynb | gpl-3.0 | import csv
import re
import numpy as np
import matplotlib.pyplot as plt
import nltk
from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from collections import defaultdict
import seaborn as sns
%matplotlib inline
"""
Expl... |
dsacademybr/PythonFundamentos | Cap10/Mini-Projeto2-Solucao/Mini-Projeto2 - Analise2.ipynb | gpl-3.0 | # Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
# Imports
import os
import subprocess
import stat
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mat
import matplotlib.pyplot as plt
fr... |
basnijholt/holoviews | examples/user_guide/Customizing_Plots.ipynb | bsd-3-clause | import numpy as np
import holoviews as hv
from holoviews import dim, opts
hv.extension('bokeh', 'matplotlib')
"""
Explanation: Customizing Plots
End of explanation
"""
hv.HoloMap({i: hv.Curve([1, 2, 3-i], group='Group', label='Label') for i in range(3)}, 'Value')
"""
Explanation: The HoloViews options system allow... |
tammoippen/nest-simulator | doc/model_details/aeif_models_implementation.ipynb | gpl-2.0 | import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (15, 6)
"""
Explanation: NEST implementation of the aeif models
Hans Ekkehard Plesser and Tanguy Fardet, 2016-09-09
This notebook provides a reference solution for the Adaptive Expo... |
AllenDowney/ThinkStats2 | code/chap06ex.ipynb | gpl-3.0 | from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve(url, filename)
print("Downloaded " + local)
download("https://github.com/AllenDowney/ThinkStats2/raw/master/code/th... |
kfollette/ASTR200-Spring2017 | Labs/Lab6/Lab6.ipynb | mit | from numpy import *
"""
Explanation: <small><i>This notebook is based on one put together by Jake Vanderplas and has been modified to suit the purposes of this course, including expansion/modification of explanations and additional exercises. Source and license info for the original is on GitHub.</i></small>
Names: [... |
opesci/devito | examples/seismic/tutorials/04_dask_pickling.ipynb | mit | #NBVAL_IGNORE_OUTPUT
# Set up inversion parameters.
param = {'t0': 0.,
'tn': 1000., # Simulation last 1 second (1000 ms)
'f0': 0.010, # Source peak frequency is 10Hz (0.010 kHz)
'nshots': 5, # Number of shots to create gradient from
'shape': (1... |
ML4DS/ML4all | U3.PCA/PCA_professor.ipynb | mit | # Basic imports
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
"""
Explanation: Principal Component Analysis
The code in this notebook has been taken from a notebook in the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub.
The c... |
riddhishb/ipython-notebooks | Kalman Filter/Kalman-first.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import random
"""
Explanation: This is an jupyter notebook.
Lectures about Python, useful both for beginners and experts, can be found at http://scipy-lectures.github.io.
Open the notebook by (1) copying this file into a directory, (2) in that dir... |
arne-cl/alt-mulig | python/maz176-statistics.ipynb | gpl-3.0 | %matplotlib inline
import os
from collections import Counter
from operator import itemgetter
import pandas as pd
from networkx import Graph
from networkx.algorithms.components.connected import connected_components
from discoursegraphs import select_edges_by, get_pointing_chains
from discoursegraphs.readwrite import C... |
empet/Math | Animating-the-Dragon-curve-construction.ipynb | bsd-3-clause | import numpy as np
from numpy import pi
import plotly.graph_objects as go
def rot_matrix(alpha):
#Define the matrix of rotation about origin with an angle of alpha radians:
return np.array([[np.cos(alpha), -np.sin(alpha)],
[np.sin(alpha), np.cos(alpha)]])
def rotate_dragon(x, y, alpha=pi/2):... |
wrgeorge1983/pcap-plotting | pcap.ipynb | mit | # This whole business is totally unnecessary if you're path is setup right. But if it's not,
# this is probably easier than actually fixing it.
%load_ext autoreload
import os
wireshark_path = "C:\\Program Files\\Wireshark\\" + os.pathsep
# or, if it's under 'program files(x86)'...
# wireshark_path = "C:\\Program File... |
Vettejeep/Data-Analysis-and-Data-Science-Projects | Support Vector Machines and the UCI Mushroom Data Set.ipynb | gpl-3.0 | %matplotlib inline
import pandas as pd
import numpy as np
import itertools
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn import metrics
import matplotlib.pyplot as plt
"""
Explanation: Support Vector Machines and the UCI Mushroom Data Set
Kevin Maher
<span style="colo... |
jorgemauricio/INIFAP_Course | algoritmos/Validacion_App_Movil_climMAPcore_AGS_BW.ipynb | mit | # librerias
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.formula.api as sm
%matplotlib inline
plt.style.use('grayscale')
# leer archivo
data = pd.read_csv('../data/dataFromAguascalientesClimmapcore.csv')
# verificar su contenido
data.head()
# diferencia entre valores de p... |
karlstroetmann/Algorithms | Python/Chapter-05/Selection-Sort.ipynb | gpl-2.0 | def sort(L):
if L == []:
return []
x = min(L)
return [x] + sort(delete(x, L))
"""
Explanation: Selection Sort
The algorithm <em style="color:blue;">selection sort</em> is specified via two equations:
If $L$ is empty, $\texttt{sort}(L)$ is the empty list:
$$ \mathtt{sort}([]) = [] $$
Otherwise... |
sz2472/foundations-homework | homework_6_shengying_zhao.ipynb | mit | type(data)
data.keys()
print(data['currently'])
print(data['currently']['temperature']-data['currently']['apparentTemperature'])
"""
Explanation: 2) What's the current wind speed? How much warmer does it feel than it actually is?
End of explanation
"""
print(data['daily'])
type(data['daily'])
data['daily'].keys... |
AllenDowney/ModSim | python/soln/examples/queue_soln.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/'
... |
tensorflow/docs-l10n | site/en-snapshot/model_optimization/guide/combine/sparse_clustering_example.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... |
mauroalberti/gsf | docs/notebooks/DEM-plane intersections.ipynb | gpl-3.0 | from pygsf.io.gdal.raster import try_read_raster_band
"""
Explanation: Plane-DEM intersections
First dated version: 2019-06-11
Current version: 2021-04-24
Last run: 2021-04-24
A few simulated topographic surfaces were used to validate the routine for calculating the plane-DEM intersection.
Loading the dataset can be m... |
parrt/msan692 | notes/excel.ipynb | mit | with open('data/SampleSuperstoreSales.xls', "rb") as f:
txt = f.read()
print(txt[0:100])
"""
Explanation: Reading data from Excel
Let's get some data. Download Sample Superstore Sales .xls file or my local copy and open it in Excel to see what it looks like.
Data of interest that we want to process in Python o... |
ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/sandbox-1/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'sandbox-1', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: SANDBOX-1
Topic: Seaice
Sub-Topics: Dynamics, Ther... |
nadvamir/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... |
sauravrt/signal-processing | ipynb/ComplexCircularGaussian.ipynb | gpl-2.0 | # magic
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
# prettyplot stuff
import seaborn as sns
sns.set(style='ticks', palette='Set2')
sns.despine()
mu = 0
sigmasq = 1
sd = np.sqrt(sigmasq)
# Generate complex gaussian r.v. samples
x = np.random.normal(loc = mu, scale = sd/np.sqrt(2), size = 10... |
ageron/tensorflow-safari-course | 10_training_deep_nets_ex9.ipynb | apache-2.0 | from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
tf.__version__
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("tmp/data/")
"""
Explanation: Try not to peek at the solutions when you go through the exercises. ;-)
... |
googledatalab/notebooks | samples/ML Toolbox/Image Classification/Flower/Local End to End.ipynb | apache-2.0 | !mkdir -p /content/flowerdata
!gsutil -m cp gs://cloud-datalab/sampledata/flower/* /content/flowerdata
"""
Explanation: Efficient training for image classification
Transfer learning using Inception Package - Local Run Experience
Traditionally, image classification required a very large corpus of training data - often... |
mne-tools/mne-tools.github.io | 0.19/_downloads/03c9d71de135994dbf45db72856a1f9a/plot_mne_inverse_envelope_correlation.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
# Sheraz Khan <sheraz@khansheraz.com>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.connectivity import envelope_correlation
from mn... |
davicsilva/dsintensive | notebooks/miniprojects/data_wrangling_json/sliderule_dsi_json_exercise.ipynb | apache-2.0 | import pandas as pd
"""
Explanation: JSON examples and exercise
get familiar with packages for dealing with JSON
study examples with JSON strings and files
work on exercise to be completed and submitted
reference: http://pandas.pydata.org/pandas-docs/stable/io.html#io-json-reader
data source: http://jsonstudio.... |
rgbrown/invariants | code/Moving frame calculations.ipynb | mit | from sympy import Function, Symbol, symbols, init_printing, expand, I, re, im
from IPython.display import Math, display
init_printing()
from transvectants import *
def disp(expr):
display(Math(my_latex(expr)))
# p and q are \bar{x} \bar{y}
x, y = symbols('x y')
p, q = symbols('p q')
a, b, c, d = symbols('a b c d... |
sjsrey/pysal | notebooks/explore/giddy/Rank_Markov.ipynb | bsd-3-clause | import pysal.lib as ps
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import pandas as pd
import geopandas as gpd
"""
Explanation: Full Rank Markov and Geographic Rank Markov
Author: Wei Kang weikang9009@gma... |
ptrendx/mxnet | example/autoencoder/convolutional_autoencoder.ipynb | apache-2.0 | import random
import matplotlib.pyplot as plt
import mxnet as mx
from mxnet import autograd, gluon
"""
Explanation: Convolutional Autoencoder
In this example we will demonstrate how you can create a convolutional autoencoder in Gluon
End of explanation
"""
batch_size = 512
ctx = mx.gpu() if len(mx.test_utils.list_... |
nick-youngblut/SIPSim | ipynb/theory/.ipynb_checkpoints/diff_bound_layer-checkpoint.ipynb | mit | import os
import numpy as np
from scipy.integrate import quad
%load_ext rpy2.ipython
workDir = '/home/nick/notebook/SIPSim/dev/theory/'
%%R
library(readxl)
library(dplyr)
library(tidyr)
library(ggplot2)
library(rootSolve)
if not os.path.isdir(workDir):
os.makedirs(workDir)
%cd $workDir
"""
Explanation: Goal:
M... |
davewsmith/notebooks | temperature/AfterMovingSensors.ipynb | mit | %matplotlib inline
import matplotlib
matplotlib.rcParams['figure.figsize'] = (12, 5)
import pandas as pd
df = pd.read_csv('after-sensor-move.csv', header=None, names=['time', 'mac', 'f', 'h'], parse_dates=[0])
per_sensor_f = df.pivot(index='time', columns='mac', values='f')
downsampled_f = per_sensor_f.resample('2T')... |
SteveDiamond/cvxpy | examples/notebooks/dqcp/minimum_length_least_squares.ipynb | gpl-3.0 | !pip install --upgrade cvxpy
import cvxpy as cp
import numpy as np
"""
Explanation: Minimum-length least squares
This notebook shows how to solve a minimum-length least squares problem, which finds a minimum-length vector $x \in \mathbf{R}^n$ achieving small mean-square error (MSE) for a particular least squares prob... |
sarahmid/programming-bootcamp-v2 | lab6_exercises.ipynb | mit | def fancy_calc(a, b, c):
x1 = basic_calc(a,b)
x2 = basic_calc(b,c)
x3 = basic_calc(c,a)
z = x1 * x2 * x3
return z
def basic_calc(x, y):
result = x + y
return result
x = 1
y = 2
z = 3
result = fancy_calc(x, y, z)
"""
Explanation: Programming Bootcamp 2016
Lesson 6 Exercises
Earning point... |
a-mt/dev-roadmap | docs/!ml/notebooks/Naive Bayes.ipynb | mit | import numpy as np
import pandas as pd
from IPython.core.display import display, HTML
display(HTML('''
<style>
.dataframe td, .dataframe th {
border: 1px solid black;
background: white;
}
.dataframe td {
text-align: left;
}
</style>
'''))
df = pd.DataFrame({
'Outlook': ['sunny', 'sunny', 'overcast', 'rain',... |
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