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800 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Imports
Step1: Algorithm
Pseudocode
Step2: Actual Code
Step3: Algorithm Conditions
We believe nonMaxSupression will perform well if and only if the histogram of the data is capable of pro... | Python Code:
import sys
import scipy.io as sio
import glob
import numpy as np
import matplotlib.pyplot as plt
from skimage.filters import threshold_otsu
sys.path.append('../code/functions')
import qaLib as qLib
sys.path.append('../../pipeline_1/code/functions')
import connectLib as cLib
from IPython.display import Imag... |
801 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Performing scripts with python-fmrest
This is a short example on how to perform scripts with python-fmrest.
Import the module
Step1: Create the server instance
Step2: Login
The login metho... | Python Code:
import fmrest
Explanation: Performing scripts with python-fmrest
This is a short example on how to perform scripts with python-fmrest.
Import the module
End of explanation
fms = fmrest.Server('https://10.211.55.15',
user='admin',
password='admin',
... |
802 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exploratory data analysis
Whenever you collect a new dataset, a good first step is to explore it. This means different things for different kinds of datasets, but if it's a timeseries then t... | Python Code:
import numpy as np
from scipy import io as si
from matplotlib import pyplot as plt
import h5py
%matplotlib inline
Explanation: Exploratory data analysis
Whenever you collect a new dataset, a good first step is to explore it. This means different things for different kinds of datasets, but if it's a timeser... |
803 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<figure>
<IMG SRC="https
Step1: Simultaneously plot three graphs
This shows a way to read data from the current directory and then plot these in a single figure.
The data file need to be ... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
aList = ['a', 'quark', 'flies', 'in', 'this', 'room', 'at', 3, 'oclock']
# print("{} {} {} {} {} {} {} {} {}".format(1,2,'three',4,5,6,6, 7, 8, 9)) # (aList))
print("{} {} {} {} {} {} {} {} {}".format(*aList))
plt.legend?
import numpy as np ... |
804 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Training DeepMind's Atari DQN with Chimp
Load Chimp modules
Step1: Load Python packages
Step2: Set training parameters
Step3: You may want to set a smaller number of iterations (like 1000... | Python Code:
from chimp.memories import ReplayMemoryHDF5
from chimp.learners.dqn_learner import DQNLearner
from chimp.learners.chainer_backend import ChainerBackend
from chimp.simulators.atari import AtariSimulator
from chimp.agents import DQNAgent
Explanation: Training DeepMind's Atari DQN with Chimp
Load Chimp module... |
805 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Image Recognition on MNIST using PyTorch Lightning
Demonstrating the elements of machine learning
Step1: Pytorch Lightning Module
PyTorch Lightning Module has a PyTorch ResNet18 Model. It i... | Python Code:
%pip install pytorch-lightning --upgrade
%pip install torchmetrics --upgrade
import torch
import torchvision
import wandb
from argparse import ArgumentParser
from pytorch_lightning import LightningModule, Trainer, Callback
from pytorch_lightning.loggers import WandbLogger
from torchmetrics.functional impor... |
806 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Aerosol
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bnu', 'sandbox-2', 'aerosol')
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: BNU
Source ID: SANDBOX-2
Topic: Aerosol
Sub-Topics: Transport, Emissions, ... |
807 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Stock analysis
Step1: The volatility calculation is made using the return for each day, given the close price of any stock. As we are interested in the annual volatility, this needs to be s... | Python Code:
import numpy as np
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt
%matplotlib inline
plt.figure(figsize=(12,12))
Explanation: Stock analysis: returns and volatility
This notebook aims to explore the Markowitz theory on modern portfolios with a little of code and a little of maths... |
808 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Answering Descriptive and Exploratory Questions About my Project
Step1: Descriptive <br />
- What is N<sub>i</sub> for all i?
Step2: What is |V|?
Step3: Do the graphs G<sub>n<sub>i</sub><... | Python Code:
# Import packages
import igraph as ig
import numpy as np
import math
import os
from subprocess import Popen, PIPE
# Initializing dataset names
dnames = list(['../data/desikan/MRN114', '../data/desikan/KKI2009', '../data/desikan/SWU4'])
print "Datasets: " + ", ".join(dnames)
print "D = " + str(len(dnames))
... |
809 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Aerosol
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'sandbox-1', 'aerosol')
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: SANDBOX-1
Topic: Aerosol
Sub-Topi... |
810 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
机器学习工程师纳米学位
入门
项目 0
Step1: 从泰坦尼克号的数据样本中，我们可以看到船上每位旅客的特征
Survived：是否存活（0代表否，1代表是）
Pclass：社会阶级（1代表上层阶级，2代表中层阶级，3代表底层阶级）
Name：船上乘客的名字
Sex：船上乘客的性别
Age
Step3: 这个例子展示了如何将泰坦尼克号的 Survived 数据从 Data... | Python Code:
import numpy as np
import pandas as pd
# RMS Titanic data visualization code
# 数据可视化代码
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# Load the dataset
# 加载数据集
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)
# Print the first few en... |
811 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example of DOV search methods for soil data (bodemgegevens)
Use cases explained below
Introduction to the bodem-objects
Get bodemsites in a bounding box
Get bodemlocaties with specific prope... | Python Code:
%matplotlib inline
import inspect, sys
import warnings; warnings.simplefilter('ignore')
# check pydov path
import pydov
Explanation: Example of DOV search methods for soil data (bodemgegevens)
Use cases explained below
Introduction to the bodem-objects
Get bodemsites in a bounding box
Get bodemlocaties wit... |
812 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
For this to work you are going to need a collection of engines to connect to. You can probably create a local collection by just running ipcluster -n 8; for more sophisticated setups read th... | Python Code:
c = Client()
c.ids
Explanation: For this to work you are going to need a collection of engines to connect to. You can probably create a local collection by just running ipcluster -n 8; for more sophisticated setups read the ipyparallel docs. You set up "profiles" and can start and connect to different engi... |
813 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Kaggle San Francisco Crime Classification
Berkeley MIDS W207 Final Project
Step1: Local, individual load of updated data set (with weather data integrated) into training, development, and t... | Python Code:
# Additional Libraries
%matplotlib inline
import matplotlib.pyplot as plt
# Import relevant libraries:
import time
import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn import preprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.preproce... |
814 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Optymalizacja i propagacja wsteczna (backprop)
Zaczniemy od prostego przykładu. Funkcji kwadratowej
Step1: Funkcja ta ma swoje minimum w punkcie $x = 0$. Jak widać na powyższym rysunku, gdy... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn
%matplotlib inline
x = np.linspace(-3, 3, 100)
plt.plot(x, x**2, label='f(x)') # optymalizowana funkcja
plt.plot(x, 2 * x, label='pochodna -- f\'(x)') # pochodna
plt.legend()
plt.show()
Explanation: Optymalizacja i propagacja wsteczna (bac... |
815 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Gaussian Mixture Models and Expectation Maximisation in Shogun
By Heiko Strathmann - <a href="mailto
Step2: Set up the model in Shogun
Step3: Sampling from mixture models
Sampling i... | Python Code:
%pylab inline
%matplotlib inline
# import all Shogun classes
from modshogun import *
from matplotlib.patches import Ellipse
# a tool for visualisation
def get_gaussian_ellipse_artist(mean, cov, nstd=1.96, color="red", linewidth=3):
Returns an ellipse artist for nstd times the standard deviation of... |
816 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright (c)2015 DiGangi, C.
Managing Epidemics Through Mathematical Modeling
This lesson will examine the spread of an epidemic over time using Euler's method. The model is a system of non... | Python Code:
%matplotlib inline
import numpy
from matplotlib import pyplot
from matplotlib import rcParams
rcParams['font.family'] = 'serif'
rcParams['font.size'] = 16
Explanation: Copyright (c)2015 DiGangi, C.
Managing Epidemics Through Mathematical Modeling
This lesson will examine the spread of an epidemic over tim... |
817 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Import data
Step1: Analyse Emails
Step2: The columns ExtractedBodyText is supposed to be the content of the mail but some of the mail have a ExtractedBodyText = NaN but the Rawtext seems t... | Python Code:
folder = 'hillary-clinton-emails/'
emails = pd.read_csv(folder + 'Emails.csv', index_col='Id')
emails.head(5)
Explanation: Import data
End of explanation
emails.head()
Explanation: Analyse Emails
End of explanation
emails.columns
print('Number of emails: ', len(emails))
bodyNaN = emails.ExtractedBodyText.i... |
818 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Convolutional Neural Networks
Step2: 2 - Outline of the Assignment
You will be implementing the building blocks of a convolutional neural network! Each function you will implement will have... | Python Code:
import numpy as np
import h5py
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
%load_ext autoreload
%autoreload 2
np.random.seed(1)
Explanation: Con... |
819 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Create Function To Visualize Classification Regions
You can ignore the code below. It is used to visualize the the decision regions of the classifier. However it is unimportant... | Python Code:
# Import packages to visualize the classifer
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import warnings
# Import packages to do the classifying
import numpy as np
from sklearn.svm import SVC
Explanation: Title: SVC Parameters When Using RBF Kernel
Slug: svc_parameters_usin... |
820 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1 Simple Octave/MATLAB Function
As a quick warm up, create a function to return a 5x5 identity matrix.
Step1: 2 Linear Regression with One Variable
In this part of this exercise, you will i... | Python Code:
A = np.eye(5)
print(A)
Explanation: 1 Simple Octave/MATLAB Function
As a quick warm up, create a function to return a 5x5 identity matrix.
End of explanation
datafile = 'ex1\\ex1data1.txt'
df = pd.read_csv(datafile, header=None, names=['Population', 'Profit'])
def plot_data(x, y):
plt.figure(figsize=(1... |
821 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Numpy elegance and curve smoothing
I recently came across a blog where smoothing of data was discussed. The following function was used to smooth a data set with a Gaussian kernel. Read the ... | Python Code:
def smoothListGaussian(list,degree=5):
list =[list[0]]*(degree-1) + list + [list[-1]]*degree
window=degree*2-1
weight=np.array([1.0]*window)
weightGauss=[]
for i in range(window):
i=i-degree+1
frac=i/float(window)
gauss=1/(np.exp((4*(frac))**2))
... |
822 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
we can check the output of this cell to kind of see how much new structural data we acquire with more properties
Step1: so some keras version stuff. 1.0 uses keras.losses to store its loss ... | Python Code:
properties = ['density', 'cpt', 'viscosity', 'thermal_conductivity',
'melting_point']
for i in range(len(properties)):
props = properties[:i+1]
devmodel = salty.aggregate_data(props, merge='Union')
devmodel.Data['smiles_string'] = devmodel.Data['smiles-cation'] + "." + devmodel.Da... |
823 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
GA4GH 1000 Genome Sequence Annotation Example
This example illustrates how to access the sequence annotations for a given set of ....
Initialize Client
In this step we create a client object... | Python Code:
import ga4gh_client.client as client
c = client.HttpClient("http://1kgenomes.ga4gh.org")
Explanation: GA4GH 1000 Genome Sequence Annotation Example
This example illustrates how to access the sequence annotations for a given set of ....
Initialize Client
In this step we create a client object which will be ... |
824 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The HRT for mixed data types (Python implementation)
In my last post I showed how the holdout random test (HRT) could be used to obtain valid p-values for any machine learning model by sampl... | Python Code:
# import the necessary modules
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
import seaborn as sns
Explanation: The HRT for mixed data types (Python implementation)
In my last post I showed how the holdout random ... |
825 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Stationarity and detrending (ADF/KPSS)
Stationarity means that the statistical properties of a time series i.e. mean, variance and covariance do not change over time. Many statistical models... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
Explanation: Stationarity and detrending (ADF/KPSS)
Stationarity means that the statistical properties of a time series i.e. mean, variance and covariance do not change over time. Many sta... |
826 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Index - Back
Asynchronous Widgets
This notebook covers two scenarios where we'd like widget-related code to run without blocking the kernel from acting on other execution requests
Step1: We... | Python Code:
%gui asyncio
Explanation: Index - Back
Asynchronous Widgets
This notebook covers two scenarios where we'd like widget-related code to run without blocking the kernel from acting on other execution requests:
Pausing code to wait for user interaction with a widget in the frontend
Updating a widget in the bac... |
827 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Initialize set up
Amplifyer is fet -15V and = +85V
Calibrate strain gauge to zero with the kinesis software
Pull fiber back
Set nicard to -3.75
Step1: Move close with fiber | Python Code:
cavitylogic._ni.cavity_set_voltage(0.0)
cavitylogic._current_filepath = r'C:\BittorrentSyncDrive\Personal - Rasmus\Rasmus notes\Measurements\171001_position15_2'
cavitylogic.last_sweep = None
cavitylogic.get_nth_full_sweep(sweep_number=1, save=True)
Explanation: Initialize set up
Amplifyer is fet -15V and ... |
828 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Train tensorflow or keras model on GCP or Kubeflow from Notebooks
This notebook introduces you to using Kubeflow Fairing to train the model to Kubeflow on Google Kubernetes Engine (GKE), and... | Python Code:
import os
import logging
import tensorflow as tf
import fairing
import numpy as np
from datetime import datetime
from fairing.cloud import gcp
# Setting up google container repositories (GCR) for storing output containers
# You can use any docker container registry istead of GCR
# For local notebook, GCP_P... |
829 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Graphs
Step1: Variables
Step2: lazy evaluation
Step3: This is what happens when we operate on two variables that are from different graphs
Step4: Variable initialization
all tf.Variables... | Python Code:
tf.get_default_graph()
graph = tf.Graph()
graph
tf.get_default_graph()
Explanation: Graphs
End of explanation
x1 = tf.Variable(3.0, name='x')
y1 = tf.Variable(4.0)
x1, y1
x1.name, y1.name
x1.graph is tf.get_default_graph()
y1.graph is tf.get_default_graph()
with graph.as_default():
x2 = tf.Variable(4.1... |
830 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced Python Objects Test
Advanced Numbers
Problem 1
Step1: Problem 2
Step2: Advanced Strings
Problem 3
Step3: Problem 4
Step4: Advanced Sets
Problem 5
Step5: Problem 6
Step6: Advan... | Python Code:
print hex(1024)
Explanation: Advanced Python Objects Test
Advanced Numbers
Problem 1: Convert 1024 to binary and hexadecimal representation:
End of explanation
print round(5.23222,2)
Explanation: Problem 2: Round 5.23222 to two decimal places
End of explanation
s = 'hello how are you Mary, are you feeling ... |
831 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Данные
Зайдите на https
Step1: Предобработка
<div class="panel panel-warning">
<div class="panel-heading">
<h3 class="panel-title">Обратите внимание</h3>
</div>
</div>
Пред... | Python Code:
train_X, train_y = pd.read_csv( # путь к вашему файлу train.csv
'data/WaterTable/train.csv'
), pd.read_csv( # путь к вашему файлу trainLabels.csv
'data/WaterTable/trainLabels.csv'
)
df = pd.merge(train_X, train_y, how='left')
df_test = pd.read_csv( # путь к вашему файлу test.csv
'data/WaterTabl... |
832 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python 基本語法與科學計算套件的使用： Python科學計算套件(一)
四堂課程大綱
第一堂-Python 基礎(一)：Python 簡介及環境建立、Python程式的編寫及執行、資料型態、基本輸入輸出、流程控制
第二堂-Python 基礎(二)：檔案讀寫、例外處理、函數、模組、物件導向
第三堂-Python科學計算套件(一)：Numpy、Matplotlib
第四堂-... | Python Code:
period = [2.4, 5, 6.3, 4.1]
print(60 * period)
bh_mass = [4.3, 5.8, 9.5, 7.6]
MASS_SUN = 1.99 * 10 ** 30
print(MASS_SUN * bh_mass)
time = list(range(1,10, 0.1))
Explanation: Python 基本語法與科學計算套件的使用： Python科學計算套件(一)
四堂課程大綱
第一堂-Python 基礎(一)：Python 簡介及環境建立、Python程式的編寫及執行、資料型態、基本輸入輸出、流程控制
第二堂-Python 基礎(二)：檔案讀寫、例... |
833 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced
Step1: As always, let's do imports and initialize a logger and a new Bundle.
Step2: Passband Options
Passband options follow the exact same rules as dataset columns.
Sending a sin... | Python Code:
#!pip install -I "phoebe>=2.3,<2.4"
Explanation: Advanced: Datasets
Datasets tell PHOEBE how and at what times to compute the model. In some cases these will include the actual observational data, and in other cases may only include the times at which you want to compute a synthetic model.
If you're not a... |
834 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Understanding Tree SHAP for Simple Models
The SHAP value for a feature is the average change in model output by conditioning on that feature when introducing features one at a time over all ... | Python Code:
import sklearn
import shap
import numpy as np
import graphviz
Explanation: Understanding Tree SHAP for Simple Models
The SHAP value for a feature is the average change in model output by conditioning on that feature when introducing features one at a time over all feature orderings. While this is easy to s... |
835 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Jupyter Notebooks (libros de notas o cuadernos Jupyter)
Puedes ejecutar un Cell (celda) pulsando [shift] + [Enter] o presionando el botón Play en la barra de herramientas.
Puedes obtener ayu... | Python Code:
import numpy as np
# Semilla de números aleatorios (para reproducibilidad)
rnd = np.random.RandomState(seed=123)
# Generar una matriz aleatoria
X = rnd.uniform(low=0.0, high=1.0, size=(3, 5)) # dimensiones 3x5
print(X)
Explanation: Jupyter Notebooks (libros de notas o cuadernos Jupyter)
Puedes ejecutar un... |
836 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
k-Nearest Neighbor (kNN) exercise
Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For ... | Python Code:
# Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
from __future__ import print_function
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplo... |
837 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Landice
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'sandbox-2', 'landice')
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: SANDBOX-2
Topic: Landice
Sub-Topics: Glac... |
838 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
solarposition.py tutorial
This tutorial needs your help to make it better!
Table of contents
Step1: SPA output
Step2: Speed tests
Step3: This numba test will only work properly if you hav... | Python Code:
import datetime
# scientific python add-ons
import numpy as np
import pandas as pd
# plotting stuff
# first line makes the plots appear in the notebook
%matplotlib inline
import matplotlib.pyplot as plt
# finally, we import the pvlib library
import pvlib
import pvlib
from pvlib.location import Location
Ex... |
839 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step 0 - hyperparams
Step1: Step 1 - collect data
Step2: (689, 682, 7)
(689, 682, 6)
(689,)
(689, 682)
Step3: Step 2 - Build model
Step4: targets
Tensor("data/strided_slice
Step5: Step ... | Python Code:
factors(689)
max_seq_len = 682
data_path = '../../../../Dropbox/data'
phae_path = data_path + '/price_hist_autoencoder'
npz_dates = phae_path + '/price_history_full_seqs_dates.npz'
assert path.isfile(npz_dates)
npz_train = phae_path + '/price_history_seqs_dates_normed_train.npz'
assert path.isfile(npz_trai... |
840 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Probability calibration of classifiers
When performing classification you often want to predict not only
the class label, but also the associated probability. This probability
gives you some... | Python Code:
print(__doc__)
# Author: Mathieu Blondel <mathieu@mblondel.org>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Balazs Kegl <balazs.kegl@gmail.com>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD Style.
import numpy as np
import matplotlib.pyplot ... |
841 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Authors.
Step1: Tensorflow Lite Gesture Classification Example Conversion Script
This guide shows how you can go about converting the model trained with Tensor... | Python Code:
#@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
# dist... |
842 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Loopless FBA
The goal of this procedure is identification of a thermodynamically consistent flux state without loops, as implied by the name.
Usually, the model has the following constraints... | Python Code:
from matplotlib.pylab import *
%matplotlib inline
import cobra.test
from cobra import Reaction, Metabolite, Model
from cobra.flux_analysis.loopless import construct_loopless_model
from cobra.solvers import get_solver_name
Explanation: Loopless FBA
The goal of this procedure is identification of a thermodyn... |
843 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Classification
Think Bayes, Second Edition
Copyright 2020 Allen B. Downey
License
Step1: Classification might be the most well-known application of Bayesian methods, made famous in the 1990... | Python Code:
# If we're running on Colab, install empiricaldist
# https://pypi.org/project/empiricaldist/
import sys
IN_COLAB = 'google.colab' in sys.modules
if IN_COLAB:
!pip install empiricaldist
# Get utils.py
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(... |
844 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<b>Acknowledgements
Step1: Parsing HTML with BeautifulSoup
Now that we've downloaded the HTML of the page, we next need to parse it. Let's say we want to extract all of the names, ages, and... | Python Code:
pet_pages = ["https://www.boulderhumane.org/animals/adoption/dogs",
"https://www.boulderhumane.org/animals/adoption/cats",
"https://www.boulderhumane.org/animals/adoption/adopt_other"]
r = requests.get(pet_pages[0])
html = r.text
print(html[:500]) # Print the first 500 character... |
845 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step7: Notebook to calculate the water permeation into packaging.
Step8: Define the simulation parameters
Step9: Loop through user events
Calculate the water flux between two points in tim... | Python Code:
import numpy as np
import pandas as pd
import argparse as ap
def mass_density_sat(T):
Mass of water in one cubic meter of air at one bar at temperature T
parameters:
T: float - Temperature (K)
returns float - mass of water in one cubic meter saturated air (kg/m^3)
r... |
846 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
An introduction to solving biological problems with Python
Session 2.3
Step1: open takes an optional second argument specifying the mode in which the file is opened, either for reading, wri... | Python Code:
path = "data/datafile.txt"
fileObj = open( path )
Explanation: An introduction to solving biological problems with Python
Session 2.3: Files
Using files
Reading from files
Exercises 2.3.1
Writing to files
Exercises 2.3.2
Data input and output (I/O)
So far, all that data we have been working with has been w... |
847 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Statistical Analysis of Data
Statistics are numbers that can be used to describe or summarize variable data points. For example, the expected value of a distribution a... | Python Code:
# 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
# distribute... |
848 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Graphical analysis of stresses
Step2: Mohr circle for 2D stresses
Step4: Mohr circle for 3D stresses | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from scipy.linalg import eigvalsh
from ipywidgets import interact
from IPython.display import display
from matplotlib import rcParams
%matplotlib notebook
rcParams['font.family'] = 'serif'
rcParams['font.size'] = 16
Explanation: Graphical analysis of stres... |
849 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
人生苦短，我用python
python第三课
推荐一个python数据结构可视化工具：http
Step1: json格式
mysql数据库基本操作
命令行操作
Step2: 数据库管理工具
sequelpro 链接：http
Step3: mysql数据类型 http
Step4: 修改数据
Step5: 删除操作
Step6: 使用python去操作数... | Python Code:
import json
data_1 = "{'a': 1, 'b': 2, 'c': 3}"
data_2 = '{"a": 1, "b": 2, "c": 3}'
j_data = json.loads(data_2)
type(j_data)
with open('/Users/wangyujie/Desktop/data.json', 'r') as f:
j_data = json.load(f)
print(j_data)
Explanation: 人生苦短，我用python
python第三课
推荐一个python数据结构可视化工具：http://www.pythontutor... |
850 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lab 08c
Step1: Next, let's load the data. Write the path to your ml-100k.csv file in the cell below
Step2: Execute the cell below to load the CSV data into a pandas data frame indexed by t... | Python Code:
%matplotlib inline
import pandas as pd
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import GridSearchCV, KFold, cross_val_predict
from sklearn.neighbors import KNeighborsRegressor
Explanation: Lab 08c: Recommender systems
Introduction
In this lab, you will build a simple mov... |
851 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Custom regression models
Like for univariate models, it is possible to create your own custom parametric survival models. Why might you want to do this?
Create new / extend AFT model... | Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
from lifelines.fitters import ParametricRegressionFitter
from autograd import numpy as np
from lifelines.datasets import load_rossi
class ExponentialAFTFitter(ParametricRegressionFitter):
# this class property is necessary, and shou... |
852 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Atmos
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify d... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csiro-bom', 'sandbox-2', 'atmos')
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: CSIRO-BOM
Source ID: SANDBOX-2
Topic: Atmos
Sub-Topics: Dynamical Core, ... |
853 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Train and valid set NLL trace
Step1: Visualising first layer weights
Quite nice features appear to have been learned with some kernels appearing to have been learned at various rotations. S... | Python Code:
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'... |
854 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The problem
Step1: Extract lon, lat variables from vgrid2 and u, v variables from vbaro.
The goal is to split the joint variables into individual CF compliant phenomena.
Step2: Using iris ... | Python Code:
from netCDF4 import Dataset
#url = ('http://geoport.whoi.edu/thredds/dodsC/usgs/data2/rsignell/gdrive/'
# 'nsf-alpha/Data/MIT_MSEAS/MSEAS_Tides_20160317/mseas_tides_2015071612_2015081612_01h.nc')
url = ('/usgs/data2/rsignell/gdrive/'
'nsf-alpha/Data/MIT_MSEAS/MSEAS_Tides_20160317/mseas_tides_2... |
855 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Another language
NOTE before starting
To change the notebook which is launched by default by the "programming with Jupyter" tab in puppet-master interface
Go to My Documents/ Poppy source-c... | Python Code:
from pypot.creatures import PoppyErgoJr
poppy = PoppyErgoJr(use_http=True, use_snap=True)
# If you want to use another robot (humanoid, torso, ...) adapt this code
#from pypot.creatures import PoppyTorso
#poppy = PoppyTorso(use_http=True, use_snap=True)
# If you want to use the robot with the camera unplug... |
856 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
4. PyNGL basics
PyNGL is a Python language module for creating 2D high performance visualizations of scientific data. It is based on NCL graphics but still not as extensive as NCL's last ver... | Python Code:
import Ngl
Explanation: 4. PyNGL basics
PyNGL is a Python language module for creating 2D high performance visualizations of scientific data. It is based on NCL graphics but still not as extensive as NCL's last version 6.6.2.
The aim of this notebook is to give you an introduction to PyNGL, read your data ... |
857 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<p><font size="6"><b> CASE - air quality data of European monitoring stations (AirBase)</b></font></p>
© 2021, Joris Van den Bossche and Stijn Van Hoey (jorisv... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
Explanation: <p><font size="6"><b> CASE - air quality data of European monitoring stations (AirBase)</b></font></p>
© 2021, Joris Van den Bossche and Stijn Van Hoey (jorisvandenb... |
858 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
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 emb... | Python Code:
import time
import numpy as np
import tensorflow as tf
import random
from collections import Counter
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... |
859 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
$$
\def\CC{\bf C}
\def\QQ{\bf Q}
\def\RR{\bf R}
\def\ZZ{\bf Z}
\def\NN{\bf N}
$$
Exemples (def + while + for + if)
Step1: On a vu dans les chapitres précédents comment définir des fonctions... | Python Code:
from __future__ import division, print_function # Python 3
Explanation: $$
\def\CC{\bf C}
\def\QQ{\bf Q}
\def\RR{\bf R}
\def\ZZ{\bf Z}
\def\NN{\bf N}
$$
Exemples (def + while + for + if)
End of explanation
def syracuse(n):
while n != 1:
print(n, end=' ')
if n % 2 == 0:
n =... |
860 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Содержание<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Получение-среза" data-toc-modified-id="Получение-среза-1">Получение среза</a></span... | Python Code:
lst = [1, 2, 3, 4, 5, 6, 7, 8]
print(lst[::])
Explanation: <h1>Содержание<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Получение-среза" data-toc-modified-id="Получение-среза-1">Получение среза</a></span><ul class="toc-item"><li><span><a href="#Без-параметров" ... |
861 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tweaking the cellpy file format
A cellpy file is a hdf5-type file.
From v.5 it contains five top-level directories.
```python
from cellreader.py
raw_dir = prms._cellpyfile_raw
step_dir = prm... | Python Code:
%load_ext autoreload
%autoreload 2
from pathlib import Path
from pprint import pprint
import pandas as pd
import cellpy
Explanation: Tweaking the cellpy file format
A cellpy file is a hdf5-type file.
From v.5 it contains five top-level directories.
```python
from cellreader.py
raw_dir = prms._cellpyfile_ra... |
862 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Idea
Результат (ожидаемый)
обучение происходит на своем родном канале на симулированных данных
учитываются различия симуляции и данных (см. ниже алгоритм)
оценка качества (как и калибровка) ... | Python Code:
%pylab inline
figsize(8, 6)
import sys
sys.path.insert(0, "../")
Explanation: Idea
Результат (ожидаемый)
обучение происходит на своем родном канале на симулированных данных
учитываются различия симуляции и данных (см. ниже алгоритм)
оценка качества (как и калибровка) будут несмещенными
качество лучше, чем ... |
863 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
23-10-2017
<center> <h1> Computational Quantum Dynamics </h1> </center>
Exercise 1
3. Eigen values of random Hermitian matrices.
(a) Write a Python script that generates a complex random ... | Python Code:
import numpy as np
from numpy import linalg as LA
import matplotlib.pyplot as plt
from math import factorial
from itertools import combinations_with_replacement
from scipy.integrate import quad
%matplotlib inline
def generate_random_hermitian(N):
A = np.random.normal(size=(N, N))
H = (np.tril(A) ... |
864 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
阅读笔记
作者：方跃文
Email
Step1: 由于这个是逗号隔开的文本，我们可以方便地使用 read_csv 来读入数据
Step2: 上面这个例子中是已经包含了header部分了，但并不是所有数据都自带header，例如
Step3: df1中我没有指明是header=None，所以原始文件中都第一行被错误地用作了header。为了
避免这种错误，需要明确指明... | Python Code:
!cat chapter06/ex1.csv
Explanation: 阅读笔记
作者：方跃文
Email: fyuewen@gmail.com
** 时间：始于2018年3月6日， 结束写作于 2018年7月27日, ２018年11月复习一次。
第六章 数据加载、存储和文件格式
数据如果不能导入和导出，那么本书中介绍的工具自然也就没有用武之地了。在我们的日常生活和研究中，有许多文件输入和输出的应用场景。
输入和输出通常划分为几个大类：读取文本文件和其他更高效的磁盘存储格式，加载数据库中的数据，利用web api操作网络资源。
读写文本格式的数据
pandas 提供了一些用于将表格型数据读取为 Da... |
865 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Language Translation
In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset o... | Python Code:
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 ... |
866 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Comparing Lump Sum vs. Dollar Cost Averaging (DCA) investing
View Notebook as HTML
View Notebook on GitHub
View Notebook on Blog
The topic of investing all at once versus spreading it over t... | Python Code:
import pandas as pd
import pandas_datareader.data as web
import datetime
pd.set_option('display.width', 200) # Displaying more columns in one row
# Data date range, Google provides up to 4000 entries in one call
start = datetime.datetime(2000, 2, 10)
end = datetime.datetime(2016, 1, 9)
spy = web.DataReade... |
867 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Nonlinear elasticity
Step1: In this chapter we investigate a nonlinear model of elastic strain in heterogeneous materials. This system is equivalent to the $p$-system of gas dynamics, alth... | Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import matplotlib as mpl
mpl.rcParams['font.size'] = 8
figsize =(8,4)
mpl.rcParams['figure.figsize'] = figsize
import numpy as np
from scipy.optimize import fsolve
import matplotlib.pyplot as plt
from utils import riemann_tools
from ipywidgets ... |
868 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
according to the histogram, the ratios are mainly under 0.2.
Step1: Improved app
Step2: improved pattern
Step3: woren pattern | Python Code:
index = ratio>0.05#get the index of ratio larger than 0.05
appfilter = app.loc[index]#filter the apps which number of current rating over number of overall rating larger than 0.1
#use histogram to show the range of current_rating-overall_rating
plt.hist(appfilter['current_rating']-appfilter['overall_rating... |
869 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
k-近邻算法
优点： 精度高、对异常值不敏感、无数据输入假定
缺点： 计算复杂度高、空间复杂度高、无法给出数据的内在含义
适用数据范围： 数值型和标称型
k-近邻算法更适用于数据集很大很全的情况？
算法思想及过程
对要分类的样本，在已有样本中寻找最近邻的 K 个样本，以这 K 个样本的分类标签中出现次数最多的标签作为待分类样本的分类标签。
寻找最近邻时，通过距离(欧几里得距离)... | Python Code:
from numpy import *
import operator
def createDataSet():
group = array([[1.0, 1.1],[1.0, 1.0],[0, 0],[0, 0.1]])
labels = ['A', 'A', 'B', 'B']
return group, labels
Explanation: k-近邻算法
优点： 精度高、对异常值不敏感、无数据输入假定
缺点： 计算复杂度高、空间复杂度高、无法给出数据的内在含义
适用数据范围： 数值型和标称型
k-近邻算法更适用于数据集很大很全的情况？
算法思想及过程
对要分类的样本，在已有样本中寻找最近邻的 ... |
870 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Inefficient photon detection
Step1: Direct photo-detection
Here we follow an example from Wiseman and Milburn, Quantum measurement and control, section. 4.8.1.
Consider cavity that leaks ph... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from qutip import *
from qutip.expect import expect_rho_vec
from matplotlib import rcParams
rcParams['font.family'] = 'STIXGeneral'
rcParams['mathtext.fontset'] = 'stix'
rcParams['font.size'] = '14'
Explanation: Inefficient photon detect... |
871 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sum of amounts in the TN bucket of the test dataset.
Step1: Sum of amounts in the FN bucket of the test dataset. | Python Code:
df.Amount.values[training_size:][(y_test == 0) & (y_test_predict == 0)].sum()
Explanation: Sum of amounts in the TN bucket of the test dataset.
End of explanation
df.Amount.values[training_size:][(y_test == 1) & (y_test_predict == 0)].sum()
100 * 8336.05/7224977.58
Explanation: Sum of amounts in the FN buc... |
872 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
rotate_error_ellipse
誤差楕円の回転がなぜ、共分散行列の両側から回転行列をかけているのかよくわからない。
なので実際に回してみるコードを書いてみた。
Step1: 共分散行列を回転させる部分を以下に抜き出してみた。
Step2: 試しに左側だけ掛けてみる。
虚数解が出たらしい。傾き22.5度の直線を軸に90度回転しているっぽい。
前後にも飛び出している状... | Python Code:
%matplotlib inline
import numpy as np
import math
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
class Error_ellipse:
def __init__(self, sigma_x = 1.0, sigma_y = 1.0, cov_xy = 0.0, mu_x = 0.0, mu_y = 0.0):
self.cov = np.array([[sigma_x, cov_xy], [cov_xy, sigma_y]]) # ... |
873 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Usable Data Map (UDM2) Cloud Detection
In this guide, you'll learn about Planet's automatic detection of pixels which are cloudy or otherwise obscured, so that you can make more intelligent ... | Python Code:
from planet import api
import time
import os
import rasterio
from rasterio.plot import show
client = api.ClientV1()
# build a filter for the AOI
filter = api.filters.range_filter("clear_percent", gte=90)
# show the structure of the filter
print(filter)
# we are requesting PlanetScope 4 Band imagery
item_ty... |
874 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Inspired by R P Herrold's challenge.
Some ways of splitting data in Python follow.
Step1: Tuple unpacking is nice and in this case
enables
DRY
(aka Single Source of Truth)
code. Compare
... | Python Code:
MONTH_NDAYS = '''
0:31
1:29
2:31
3:30
4:31
5:30
6:31
7:31
8:30
9:31
10:30
11:31
'''.split()
MONTH_NDAYS
for month_n_days in MONTH_NDAYS:
month, n_days = map(int, month_n_days.split(':'))
print(f'{month} has {n_days}')
Explanation: Inspired by R P Her... |
875 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
数据抓取
抓取历届政府工作报告
王成军
wangchengjun@nju.edu.cn
计算传播网 http
Step1: Inspect
<td width="274" class="bl">· <a href="./d12qgrdzfbg/201603/t20160318_369509.html" target="_blank" title="2016年政府工... | Python Code:
import requests
from bs4 import BeautifulSoup
from IPython.display import display_html, HTML
HTML('<iframe src=http://www.hprc.org.cn/wxzl/wxysl/lczf/ width=1000 height=500></iframe>')
# the webpage we would like to crawl
Explanation: 数据抓取
抓取历届政府工作报告
王成军
wangchengjun@nju.edu.cn
计算传播网 http://computational-... |
876 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
이 노트북은 Hell, Tensorflow! 의 내용을 참고로 하여 노트북으로 재 정돈한 것입니다.
Step1: 텐서플로우의 디폴트 그래프는 직접 접근을 할 수 없고 get_default_graph 메소드를 이용합니다.
Step2: 초기에는 디폴트 그래프에 아무런 연산도 들어 있지 않고 비어 있습니다.
Step3: 실수 1.0 값을 ... | Python Code:
%load_ext watermark
%watermark -vm -p tensorflow,numpy,scikit-learn
import tensorflow as tf
Explanation: 이 노트북은 Hell, Tensorflow! 의 내용을 참고로 하여 노트북으로 재 정돈한 것입니다.
End of explanation
graph = tf.get_default_graph()
Explanation: 텐서플로우의 디폴트 그래프는 직접 접근을 할 수 없고 get_default_graph 메소드를 이용합니다.
End of explanation
grap... |
877 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fitting Models Exercise 1
Imports
Step1: Fitting a quadratic curve
For this problem we are going to work with the following model
Step2: First, generate a dataset using this model using th... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
Explanation: Fitting Models Exercise 1
Imports
End of explanation
a_true = 0.5
b_true = 2.0
c_true = -4.0
Explanation: Fitting a quadratic curve
For this problem we are going to work with the following model:... |
878 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Numpy Exercise 2
Imports
Step1: Factorial
Write a function that computes the factorial of small numbers using np.arange and np.cumprod.
Step2: Write a function that computes the factorial ... | Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
Explanation: Numpy Exercise 2
Imports
End of explanation
def np_fact(n):
# if n == 0:
# return 1
# elif n == 1:
# return 1
# elif n == 10:
# return 3628800
if n==0:
r... |
879 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
NLP Workshop
Author
Step1: Load A Text Dataset
Collection of TED talk transcripts from the ted.com website
Step2: Storage and File types for text
Common text file types for text include .j... | Python Code:
%pwd
# make sure we're running our script from the right place;
# imports like "filename" are relative to where we're running ipython
Explanation: NLP Workshop
Author: Clare Corthell, Luminant Data
Conference: Talking Machines, Manila
Date: 18 February 2016
Description: Much of human knowledge is “locked... |
880 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
这个分析笔记由Jake Vanderplas编辑汇总。 源代码和license文件在GitHub。 中文翻译由派兰数据在派兰大数据分析平台上完成。 源代码在GitHub上。
深度探索监督学习：支持向量机
之前我们已经介绍了监督学习。监督学习中有很多算法，在这里我们深入探索其中一种最强大的也最有趣的算法之一：支持向量机（Support Vector Machines，SVMs）.... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
# 使用seaborn的一些默认配置
import seaborn as sns; sns.set()
Explanation: 这个分析笔记由Jake Vanderplas编辑汇总。 源代码和license文件在GitHub。 中文翻译由派兰数据在派兰大数据分析平台上完成。 源代码在GitHub上。
深度探索监督学习：支持向量机
之前我们已经介绍了监督学习。监督学习中有很多算法，在这里我们深入探索其中一种最强大的也最有趣... |
881 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Downloading public data
Something you may want to do in the future is compare your results to papers that came before you. Today we'll go through how to find these data and how to analyze th... | Python Code:
# Alphabetical order is standard
# We're doing "import superlongname as abbrev" for our laziness - this way we don't have to type out the whole thing each time.
# Python plotting library
import matplotlib.pyplot as plt
# Numerical python library (pronounced "num-pie")
import numpy as np
# Dataframes in Pyt... |
882 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Evaluation, Cross-Validation, and Model Selection
By Heiko Strathmann - heiko.strathmann@gmail.com - http
Step1: Types of splitting strategies
As said earlier Cross-validation is based upon... | Python Code:
%pylab inline
%matplotlib inline
# include all Shogun classes
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from shogun import *
import shogun as sg
# generate some ultra easy training data
gray()
n=20
title('Toy data for binary classification')
X=hstack((randn(2,n), randn(2,n)+1)... |
883 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
01 - Example - Eliminating Outliers
This notebook presents how to eliminate the diagnosed outliers (in the previous Learning Unit).
By
Step1: Load the dataset that will be used
Step2: Let ... | Python Code:
import pandas as pd
import numpy as np
% matplotlib inline
from matplotlib import pyplot as plt
Explanation: 01 - Example - Eliminating Outliers
This notebook presents how to eliminate the diagnosed outliers (in the previous Learning Unit).
By: Hugo Lopes
Learning Unit 08
Some inital imports:
End of expla... |
884 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Size encodings tutorial
See the examples below for common ways to map data to node size in Graphistry.
Size refers to point radius. This tutorial covers two kinds of size controls
Step1: De... | Python Code:
# ! pip install --user graphistry
import graphistry
# To specify Graphistry account & server, use:
# graphistry.register(api=3, username='...', password='...', protocol='https', server='hub.graphistry.com')
# For more options, see https://github.com/graphistry/pygraphistry#configure
graphistry.__version__
... |
885 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Import packages
Importing the necessary packages, including the standard TFX component classes
Step2: Pima Indians Diabetes example pipeline
Download Example Data
We ... | Python Code:
!pip install -U tfx
# getting the code directly from the repo
x = !pwd
if 'feature_selection' not in str(x):
!git clone -b main https://github.com/deutranium/tfx-addons.git
%cd tfx-addons/tfx_addons/feature_selection
Explanation: <a href="https://colab.research.google.com/github/deutranium/tfx-addons/... |
886 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introducing PyTorch
written by Gene Kogan, updated by Sebastian Quinard
In the next cell, we introduce PyTorch, which is an open-source framework which impelments machine learning methodolog... | Python Code:
import numpy as np
from sklearn.datasets import load_iris
iris = load_iris()
data, labels = iris.data[:,0:3], iris.data[:,3]
Explanation: Introducing PyTorch
written by Gene Kogan, updated by Sebastian Quinard
In the next cell, we introduce PyTorch, which is an open-source framework which impelments machin... |
887 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
What do I want?
Previously in ../catalog_only_classifier/classifier_comparison.ipynb I got a Random Forest-predicted probability of each galaxy being a low-z dwarf. Now I want to select a su... | Python Code:
# give access to importing dwarfz
import os, sys
dwarfz_package_dir = os.getcwd().split("dwarfz")[0]
if dwarfz_package_dir not in sys.path:
sys.path.insert(0, dwarfz_package_dir)
import dwarfz
# back to regular import statements
%matplotlib inline
from matplotlib import pyplot as plt
import seabor... |
888 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Landice
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-mh', 'landice')
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-MH
Topic: Landice
Sub-Topics: Glaciers, Ice.
Prop... |
889 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pandas convert JSON into a DataFrame
This is a notebook for the medium article How to convert JSON into a Pandas DataFrame?
Please check out article for instructions
License
Step1: 1. Readi... | Python Code:
import pandas as pd
Explanation: Pandas convert JSON into a DataFrame
This is a notebook for the medium article How to convert JSON into a Pandas DataFrame?
Please check out article for instructions
License: BSD 2-Clause
End of explanation
df = pd.read_json('data/simple.json')
df
df.info()
Explanation: 1. ... |
890 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Aerosol
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mri', 'mri-esm2-0', 'aerosol')
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MRI
Source ID: MRI-ESM2-0
Topic: Aerosol
Sub-Topics: Transport, Emissions... |
891 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
There is an outlier
Step1: Expenses Data Distribution
Step2: We can see that there are three employees spend considerably more than others.
Step3: Salary Data Distribution
Step4: There a... | Python Code:
del data_dict['TOTAL']
df = pandas.DataFrame.from_dict(data_dict, orient='index')
df.head()
print "Dataset size: %d rows x %d columns"%df.shape
df.dtypes
print "Feature | Missing values"
print "---|---"
for column in df.columns:
if column != 'poi':
print "%s | %d"%(column,(df[column] == 'NaN').... |
892 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Reading Annotations from a GO Association File (GAF)
Download a GAF file
Load the GAF file into the GafReader
Get Annotations
Bonus
Step1: 2) Load the GAF file into the GafReader
Step2: 3)... | Python Code:
import os
if not os.path.exists('goa_human.gaf.gz'):
!wget http://current.geneontology.org/annotations/goa_human.gaf.gz
!gunzip goa_human.gaf.gz
Explanation: Reading Annotations from a GO Association File (GAF)
Download a GAF file
Load the GAF file into the GafReader
Get Annotations
Bonus: Each lin... |
893 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Stage 1
Step1: Stage 2
Step2: Stage 3
Step3: Stage 4 | Python Code:
import pytextrank
import sys
path_stage0 = "dat/mih.json"
path_stage1 = "o1.json"
with open(path_stage1, 'w') as f:
for graf in pytextrank.parse_doc(pytextrank.json_iter(path_stage0)):
f.write("%s\n" % pytextrank.pretty_print(graf._asdict()))
# to view output in this notebook
pr... |
894 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Language Translation
In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset o... | Python Code:
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 ... |
895 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to NLTK
We have seen how to do some basic text processing in Python, now we introduce an open source framework for natural language processing that can further help to work with... | Python Code:
sampleText1 = "The Elephant's 4 legs: THE Pub! You can't believe it or can you, the believer?"
sampleText2 = "Pierre Vinken, 61 years old, will join the board as a nonexecutive director Nov. 29."
Explanation: Introduction to NLTK
We have seen how to do some basic text processing in Python, now we introduce... |
896 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bungee Dunk
Modeling and Simulation in Python
Copyright 2021 Allen Downey
License
Step1: Suppose you want to set the world record for the highest "bungee dunk", as shown in this video. Sin... | Python Code:
# 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/... |
897 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Loading, saving and exporting data
Pymrio includes several functions for data reading and storing. This section presents the methods to use for saving and loading data already in a pymrio co... | Python Code:
import pymrio
import os
io = pymrio.load_test().calc_all()
Explanation: Loading, saving and exporting data
Pymrio includes several functions for data reading and storing. This section presents the methods to use for saving and loading data already in a pymrio compatible format. For parsing raw MRIO data se... |
898 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Word Embeddings
Learning Objectives
You will learn
Step1: This notebook uses TF2.x.
Please check your tensorflow version using the cell below.
Step2: Download the IMDb Dataset
You will use... | Python Code:
# Use the chown command to change the ownership of repository to user.
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
import io
import os
import re
import shutil
import string
import tensorflow as tf
from datetime import datetime
from tensorflow.keras import Model, Sequential
from tenso... |
899 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Optically pumped magnetometer (OPM) data
In this dataset, electrical median nerve stimulation was delivered to the
left wrist of the subject. Somatosensory evoked fields were measured using
... | Python Code:
import os.path as op
import numpy as np
import mne
data_path = mne.datasets.opm.data_path()
subject = 'OPM_sample'
subjects_dir = op.join(data_path, 'subjects')
raw_fname = op.join(data_path, 'MEG', 'OPM', 'OPM_SEF_raw.fif')
bem_fname = op.join(subjects_dir, subject, 'bem',
subject + '-... |
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