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200 | <SYSTEM_TASK:>
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Description:
Tour of Free(mium) HERE APIs
A short "teaser" presentation rushing through a small subset of many free APIs made by HERE Technologies under the Freemiu... | <ASSISTANT_TASK:>
Python Code:
import random
import urllib
import utils
app_id = utils.app_id
app_code = utils.app_code
berlin_lat_lon = [52.5, 13.4]
here_berlin_addr = 'Invalidenstr. 116, 10115 Berlin, Germany'
Explanation: Tour of Free(mium) HERE APIs
A short "teaser" presentation rushing through a small subset of ... |
201 | <SYSTEM_TASK:>
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Description:
A Deeper Understanding of Deep Learning
Time for another mini-movie!
In this video, Dan introduces stochastic gradient descent (SGD) and back-propagati... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import YouTubeVideo
YouTubeVideo('kQmHaI5Jw1c', width=800, height=450)
Explanation: A Deeper Understanding of Deep Learning
Time for another mini-movie!
In this video, Dan introduces stochastic gradient descent (SGD) and back-propagation and explains how they are used... |
202 | <SYSTEM_TASK:>
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Description:
ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contribu... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncc', 'noresm2-mh', 'atmoschem')
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-MH
Topic: Atmoschem
Sub-Topi... |
203 | <SYSTEM_TASK:>
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Description:
Interferometry
The idea behind interferometry is that electromagnetic waves emitted from a certain point in the sky will cause characteristic interfere... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib import cm, colors
import numpy as np
from ipywidgets import interact
plt.rcParams['figure.figsize'] = 16, 8
import sys
sys.path.append('../..')
from crocodile.simulate import *
from crocodile.synthesis import *
from util.v... |
204 | <SYSTEM_TASK:>
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Description:
<table>
<tr align=left><td><img align=left src="./images/CC-BY.png">
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import scipy.linalg as la
import matplotlib.pyplot as plt
Explanation: <table>
<tr align=left><td><img align=left src="./images/CC-BY.png">
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the F... |
205 | <SYSTEM_TASK:>
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Description:
Copyright 2020 The TensorFlow Authors.
Step1: Tensorflow Lattice와 형상 제약 조건
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" ... | <ASSISTANT_TASK:>
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 writin... |
206 | <SYSTEM_TASK:>
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Description:
Project 1
Used Vehicle Price Prediction
Introduction
1.2 Million listings scraped from TrueCar.com - Price, Mileage, Make, Model dataset from Kaggle
St... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
data = pd.read_csv('https://github.com/albahnsen/PracticalMachineLearningClass/raw/master/datasets/dataTrain_carListings.zip')
data.head()
data.shape
data.Price.describe()
data.plot(kind='scatter', y='Price', x='Year')
data.plot(kind='scatter', y='Pr... |
207 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Automated Assessment of Car Damage Photos using SAS
Import the required packages including swat for accessing CAS actions
https
Step1: Set up the envi... | <ASSISTANT_TASK:>
Python Code:
# import the required packages
from swat import *
from pprint import pprint
import numpy as np
import matplotlib.pyplot as plt
import cv2
# define the function to display the processed image files.
def imageShow(session, casTable, imageId, nimages):
a = session.table.fetch(sastypes=F... |
208 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Machine Learning Engineer Nanodegree
Introduction and Foundations
Project 0
Step1: From a sample of the RMS Titanic data, we can see the various featu... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
# RMS Titanic data visualization code
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# Load the dataset
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)
# Print the first few ent... |
209 | <SYSTEM_TASK:>
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Description:
On this notebook the best models and input parameters will be searched for. The problem at hand is predicting the price of any stock symbol 14 days ahe... | <ASSISTANT_TASK:>
Python Code:
# Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
%matplotlib inline
%pylab inline
pylab.rcParams[... |
210 | <SYSTEM_TASK:>
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Description:
Introduction
Welcome to the Petals to the Metal competition! In this competition, you’re challenged to build a machine learning model that identifies t... | <ASSISTANT_TASK:>
Python Code:
from petal_helper import *
import tensorflow as tf
Explanation: Introduction
Welcome to the Petals to the Metal competition! In this competition, you’re challenged to build a machine learning model that identifies the type of flowers in a dataset of images.
In this tutorial notebook, you'... |
211 | <SYSTEM_TASK:>
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Description:
Collect all tweets from @realDonaldTrump
Step1: To sort tweets by favorites or retweets, need to convert unicode to integers
Step2: For some reason t... | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('..')
from twords.twords import Twords
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
# this pandas line makes the dataframe display all text in a line; useful for seeing entire tweets
pd.set_option('display.max_colwidth', -1)
twit = Two... |
212 | <SYSTEM_TASK:>
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Description:
Step3: Numerical Methods
For Numerical Relativity, we need to
evolve the spacetime (hyperbolic PDEs with "smooth" fields);
evolve the matter (hyperboli... | <ASSISTANT_TASK:>
Python Code:
def backward_differencing(f, x_i, dx):
Backward differencing of f at x_i with grid spacing dx.
f_i = f(x_i)
f_i_minus_1 = f(x_i - dx)
return (f_i - f_i_minus_1) / dx
def forward_differencing(f, x_i, dx):
Forward differencing of f at x_i with grid sp... |
213 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
beapy
beapy is a Python package for obtaining data from the API of the Bureau of Economic Analysis.
Step1: Methods for searching for data
getDataSetLi... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import urllib
import datetime
import matplotlib.pyplot as plt
%matplotlib inline
%load_ext autoreload
%autoreload 2
import beapy
apiKey = '3EDEAA66-4B2B-4926-83C9-FD2089747A5B'
bea = beapy.initialize(apiKey =apiKey)
Explanation: beapy
beapy is a Pyth... |
214 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Cartopy
Cartopy is a Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses.
We test here a... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (16, 10)
import cartopy.crs as ccrs
Explanation: Cartopy
Cartopy is a Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses.
We test here a few ... |
215 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Calculate Political Opinion Models
Step1: Training the Base Model
Calculate the base model (from german wiki), that is later used as a base for traini... | <ASSISTANT_TASK:>
Python Code:
# import logging
# logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s')
# logging.root.level = logging.INFO
from os import path
from random import shuffle
from corputil import FileCorpus, ListCorpus
from corputil.utils import load_stopwords
from gensim.models.word2vec i... |
216 | <SYSTEM_TASK:>
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Description:
Supervised and Unsupervised learning example
We are going to try to be able to identify handwritten digits
These handwritten digits are contained in th... | <ASSISTANT_TASK:>
Python Code:
# Let's import the relevant packages first
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from sklearn import manifold
import gzip, cPickle
import pandas as pd
from sklearn.cluster import KMeans
from sklearn import metrics
Explanation: Supervised and Unsupervi... |
217 | <SYSTEM_TASK:>
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Description:
Data
The data can be downloaded from https
Step1: Check the total data sets agree
The files filename and filename1 were downloaded from, respectively,... | <ASSISTANT_TASK:>
Python Code:
import sys, os, csv, lzma
import open_cp.sources.chicago as chicago
filename = os.path.join("..", "..", "open_cp", "sources", "chicago.csv")
filename1 = os.path.join("..", "..", "open_cp", "sources", "chicago1.csv")
filename_all = os.path.join("..", "..", "open_cp", "sources", "chicago_al... |
218 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Symmetric random walk on uniform distribution
Step1: We genereate samples from following distribution
$$
P(x)= \begin{cases}1 / 21 & x \in{0,1,2, \ldo... | <ASSISTANT_TASK:>
Python Code:
# Example from section 29.4 & 29.6 (Fig 29.14 & 29.15) of https://www.inference.org.uk/itprnn/book.pdf
try:
import probml_utils as pml
except ModuleNotFoundError:
%pip install -qq git+https://github.com/probml/probml-utils.git
import probml_utils as pml
import matplotlib.pyplo... |
219 | <SYSTEM_TASK:>
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Description:
5. Squared Wheel and Other Reinventions
Many a times, we hear discussions about writing programs using Python in the "most Pythonic way." Sometimes it'... | <ASSISTANT_TASK:>
Python Code:
# Task: Concatenate a list of strings into a single string
# delimited by spaces.
list_of_words = ['the', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy', 'dog']
i = 0 # A counter to maintain the current position in the list
new_string = '' # String to hold the output
while i < l... |
220 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
AlexNet in Keras
In this notebook, we leverage an AlexNet-like deep, convolutional neural network to classify flowers into the 17 categories of the Oxf... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.random.seed(42)
Explanation: AlexNet in Keras
In this notebook, we leverage an AlexNet-like deep, convolutional neural network to classify flowers into the 17 categories of the Oxford Flowers data set. Derived from this earlier notebook.
Set seed for reproducibility
... |
221 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Single Particle Systems
Step1: Next, we construct the fermion basis using the constructor spinless_fermion_basis_1d. Since we are interested in a free... | <ASSISTANT_TASK:>
Python Code:
from quspin.operators import hamiltonian # Hamiltonians and operators
from quspin.basis import spinless_fermion_basis_1d # Hilbert space fermion basis
from quspin.tools.block_tools import block_diag_hamiltonian # block diagonalisation
import numpy as np # generic math functions
#
##### de... |
222 | <SYSTEM_TASK:>
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Description:
Outline
Glossary
7. Observing Systems
Previous
Step1: Import section specific modules
Step2: 7.7 Propagation effects <a id='instrum
Step3: Figure ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
Explanation: Outline
Glossary
7. Observing Systems
Previous: 7.6 Polarization and Antenna Feeds
Next: 7.8 Radio Frequency Interference (... |
223 | <SYSTEM_TASK:>
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Description:
Interact Exercise 2
Imports
Step1: Plotting with parameters
Write a plot_sin1(a, b) function that plots $sin(ax+b)$ over the interval $[0,4\pi]$.
Cust... | <ASSISTANT_TASK:>
Python Code:
%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 2
Imports
End of explanation
def plot_sine1(a, b):
f = plt.figure(figsize=(16,2))... |
224 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
XOR Network
Data generation
Step1: Xor cannot be solved with single layer of neural network
Step2: Notice that the error is far from zero.
Actually n... | <ASSISTANT_TASK:>
Python Code:
def create_examples(N, batch_size):
A = np.random.binomial(n=1, p=0.5, size=(batch_size, N))
B = np.random.binomial(n=1, p=0.5, size=(batch_size, N,))
X = np.zeros((batch_size, 2 *N,), dtype=np.float32)
X[:,:N], X[:,N:] = A, B
Y = (A ^ B).astype(np.float32)
return ... |
225 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Sensitivity, accuracy, errors, and repeatability
Linear approximation of nonlinear transfer function. The response of a temperature sensor is given as
... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
from math import log, exp
%matplotlib inline
# Evaluate beta for this sensor
T_0=273.15+20;
N=(1/273.15-1/293.15)-(1/298.15-1/293.15);
beta= log(3000/1000)/N;
R_0=1000/exp(beta*((1/298.15)-(1/293.15)));
## Results
print('Beta for this se... |
226 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Entity Extraction from Contract Documents
Problem Statement
Step1: Figure 1
Step2: Loading the data
Step3: Converting the rawtext into sentences
Ste... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
Image(filename='entity_extraction_process.png')
# Note: this image is taken from NLTK Book and requires citation
Explanation: Entity Extraction from Contract Documents
Problem Statement:
Many companies deal with the understanding of large amount text data... |
227 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
minimask mosaic example
Construct a mosaic of squares over the sky
Step1: Specify the location of the mask file to write
Step2: Construct a mask usin... | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import os
import numpy as np
import tempfile
import matplotlib.pyplot as pyplot
import logging
logging.basicConfig(level=logging.INFO)
import minimask.mask as mask
import minimask.healpix_projection as hp
import minimask.io.mosaic as mosaic
Explanation: minimask mosai... |
228 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
EEG source localization given electrode locations on an MRI
This tutorial explains how to compute the forward operator from EEG data
when the electrode... | <ASSISTANT_TASK:>
Python Code:
# Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD Style.
import os.path as op
import nibabel
from nilearn.plotting import plot_glass_brain
import numpy as np
import mne
from mne.channels import compute_native_head_t, read_custom_montage
from mne.viz import plot_alignment
E... |
229 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Demo
Strategic Redesign für das Projekt „Spring Petclinic“
Auslastungsdaten vom Produktivbetrieb
Datenquelle
Step1: Berechnung wesentlicher Metriken f... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
coverage = pd.read_csv("../dataset/jacoco_production_coverage_spring_petclinic.csv")
coverage.head()
Explanation: Demo
Strategic Redesign für das Projekt „Spring Petclinic“
Auslastungsdaten vom Produktivbetrieb
Datenquelle: Gemessen wurde der Anwendungsbetrieb der Soft... |
230 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Метод главных компонент
В данном задании вам будет предложено ознакомиться с подходом, который переоткрывался в самых разных областях, имеет множество ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib
from matplotlib import pyplot as plt
import matplotlib.patches as mpatches
matplotlib.style.use('ggplot')
%matplotlib inline
Explanation: Метод главных компонент
В данном задании вам будет предложено ознакомиться с подходом, который... |
231 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<h1> Repeatable splitting </h1>
In this notebook, we will explore the impact of different ways of creating machine learning datasets.
<p>
Repeatability... | <ASSISTANT_TASK:>
Python Code:
from google.cloud import bigquery
Explanation: <h1> Repeatable splitting </h1>
In this notebook, we will explore the impact of different ways of creating machine learning datasets.
<p>
Repeatability is important in machine learning. If you do the same thing now and 5 minutes from now and ... |
232 | <SYSTEM_TASK:>
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Description:
2018 NUS-MIT Datathon Tutorial
Step1: Next, we need to authenticate ourselves to Google Cloud Platform. If you are running the code cell below for the... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import os
import pandas as pd
import random
import tensorflow as tf
from google.colab import auth
from google.cloud import storage
from io import BytesIO
# The next import is used to print out pretty pandas dataframes
from IPython.display import display, HTML
from PIL i... |
233 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Below I use my stemming/tokenizing and tokenizing functions to iterate over the list of synopses to create two vocabularies
Step1: Tf-idf and document... | <ASSISTANT_TASK:>
Python Code:
df = pd.read_csv('../data/wiki/wiki.csv.gz', encoding='utf8', index_col=None)
df['text'] = df.text.str[:3000]
totalvocab_stemmed = []
totalvocab_tokenized = []
for doc_text in df.text:
allwords_stemmed = tokenize_and_stem(doc_text) #for each item in 'synopses', tokenize/stem
total... |
234 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Übungsblatt 7
Präsenzaufgaben
Aufgabe 1 CFG
Step3: Aufgabe 2 CFG
Step5: Hausaufgaben
Aufgabe 7 &nb... | <ASSISTANT_TASK:>
Python Code:
grammar =
S -> NP VP
NP -> DET NOM
NOM -> ADJ NOM | N
ADJ -> "schöne" | "kluge" | "dicke"
DET -> "der" | "die" | "das"
N -> "Mann" | "Frau" | "Buch"
VP -> V NP NP | V NP | V
V -> "gibt" | "schenkt" | "schläft" | "gefällt" | "kennt"
import nltk
from IPython.display import display
import s... |
235 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Vertex AI Pipelines
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Install the latest GA version of google-clou... | <ASSISTANT_TASK:>
Python Code:
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 AI Pipelines: model train, upload, and deploy using google-clou... |
236 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Python fundamentals
A quick introduction to the Python programming language and Jupyter notebooks. (We're using Python 3, not Python 2.)
Basic data typ... | <ASSISTANT_TASK:>
Python Code:
# variable assignment
# https://www.digitalocean.com/community/tutorials/how-to-use-variables-in-python-3
# strings -- enclose in single or double quotes, just make sure they match
# numbers
# the print function
# booleans
Explanation: Python fundamentals
A quick introduction to the Pytho... |
237 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Exploring US Traffic Fatality Data
By Ben Van Dyke
The National Highway Traffic Safety Administration maintains a fatality dataset called the Fatality ... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import StringIO
import zipfile
import urllib
from __future__ import division, print_function
matplotlib.style.use('fivethirtyeight')
%matplotlib inline
# Download and extract the 2015 FARS file
output... |
238 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Iris Project
Data Exploration and Analysis
Step1: Iris Data from Seaborn
Step2: Visualisation
Step3: scikit-learn
url = http
Step4: Key points
Data... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(color_codes=True)
%matplotlib inline
df = pd.read_csv('iris.data')
df.head()
df = pd.read_csv('iris.data', header=-1)
df.head()
col_name = ['sepal length', 'sepal width', 'petal length',... |
239 | <SYSTEM_TASK:>
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Description:
Train / Test
We'll start by creating some data set that we want to build a model for (in this case a polynomial regression)
Step1: Now we'll split the... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from pylab import *
np.random.seed(2)
pageSpeeds = np.random.normal(3.0, 1.0, 100)
purchaseAmount = np.random.normal(50.0, 30.0, 100) / pageSpeeds
scatter(pageSpeeds, purchaseAmount)
Explanation: Train / Test
We'll start by creating some data set that... |
240 | <SYSTEM_TASK:>
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Description:
Copyright 2018 The TensorFlow Probability Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: コピュラ入門
<table class="tfo-not... | <ASSISTANT_TASK:>
Python Code:
#@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 l... |
241 | <SYSTEM_TASK:>
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Description:
Populate local MDCS instance with student data and metadata
Import MDCS API tool module
Step1: Host and user information
Step2: List of file prefixes... | <ASSISTANT_TASK:>
Python Code:
import mdcs
Explanation: Populate local MDCS instance with student data and metadata
Import MDCS API tool module
End of explanation
user='admin'
pswd='admin'
host='http://127.0.0.1:8000'
template_name='DiffusionDemo'
Explanation: Host and user information
End of explanation
name_list=[
... |
242 | <SYSTEM_TASK:>
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Description:
Introduction
Step1: Import raw data
The user needs to specify the directories containing the data of interest. Each sample type should have a key whic... | <ASSISTANT_TASK:>
Python Code:
import deltascope as ds
import deltascope.alignment as ut
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import normalize
from scipy.optimize import minimize
import os
import tqdm
import json
import datetime
Explanation: Introduction: Lan... |
243 | <SYSTEM_TASK:>
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Description:
In this post, we'll use data from a tensile test to build a stress strain curve with Python and Matplotlib.
A tensile test is a type of mechanical test... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
print("NumPy version:",np.__version__)
print("Pandas version:",pd.__version__)
Explanation: In this post, we'll use data from a tensile test to build a stress strain curve with Python and Matplotlib.... |
244 | <SYSTEM_TASK:>
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Description:
An introduction to solving biological problems with Python
Session 1.3
Step1: You can of course use variables in tuples and other data structures
Step... | <ASSISTANT_TASK:>
Python Code:
a = (123, 54, 92) # tuple of 4 integers
b = () # empty tuple
c = ("Ala",) # tuple of a single string (note the trailing ",")
d = (2, 3, False, "Arg", None) # a tuple of mixed types
print(a)
print(b)
print(c)
print(d)
Explanation: An introduction to solving biological problems with Python
... |
245 | <SYSTEM_TASK:>
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Description:
Chemical-Disease Relation (CDR) Tutorial
In this example, we'll be writing an application to extract mentions of chemical-induced-disease relationships... | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
from snorkel import SnorkelSession
session = SnorkelSession()
from snorkel.models import candidate_subclass
ChemicalDisease = candidate_subclass('ChemicalDisease', ['chemical', 'disease'])
train_cands = session.query(ChemicalDisease).f... |
246 | <SYSTEM_TASK:>
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Description:
title
Step1: 对于变量x来说，不需要知道它是字符串还是列表，就可以调用它的count方法—不用管它是什么类型（只要提供一个字符作为参数即可）。
任何不知道对象到底是什么类型，但是又要对对象“做点什么”的时候，都会用到多态。这不仅限于方法，很多内建运算符和函数都有多态的性质：
Step... | <ASSISTANT_TASK:>
Python Code:
'abc'.count('a')
[1,2,'a'].count('a')
Explanation: title: 对象
create: 2016.12.7
modified: 2016.12.7
tags: python
多态
封装
方法
5
[TOC]
之前介绍了Python主要的内建对象类型（数字int、字符串str、列表list、元组tuple和字典dict），以及内建函数和标准库的用法，还有自定义函数的方式。接下来将介绍如何创建自己的对象？
为什么要自定义对象呢？使用字典、序列、数字和字符串来创建函数，完成这项... |
247 | <SYSTEM_TASK:>
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Description:
NYGC 30x HighCov samples Hail Table
Step1: Phased genotypes
Creating MTs for the phased data is straightforward, as multiallelic variants were split d... | <ASSISTANT_TASK:>
Python Code:
ht_samples = hl.import_table(
"gs://hail-datasets-tmp/1000_Genomes_NYGC_30x/1000_Genomes_NYGC_30x_samples_ped_population.txt.bgz",
delimiter="\s+",
impute=True
)
ht_samples = ht_samples.annotate(
FatherID = hl.if_else(ht_samples.FatherID == "0",
... |
248 | <SYSTEM_TASK:>
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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 im... | <ASSISTANT_TASK:>
Python Code:
import time
import numpy as np
import tensorflow as tf
import utils
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for u... |
249 | <SYSTEM_TASK:>
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Description:
Building a LAS file from scratch
Step1: Step 1
Create some fake data, and make some of the values at the bottom NULL (numpy.nan). Note that of course ... | <ASSISTANT_TASK:>
Python Code:
import lasio
import datetime
import numpy
import os
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Building a LAS file from scratch
End of explanation
depths = numpy.arange(10, 50, 0.5)
fake_curve = numpy.random.random(len(depths))
fake_curve[-10:] = numpy.nan # Add som... |
250 | <SYSTEM_TASK:>
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Description:
Homework 1
Step1: If you've set up your environment properly, this cell should run without problems
Step2: Now, run this cell to log into OkPy.
This ... | <ASSISTANT_TASK:>
Python Code:
!pip install -U okpy
Explanation: Homework 1: Setup and (Re-)Introduction to Python
Course Policies
Here are some important course policies. These are also located at
http://www.ds100.org/sp17/.
Tentative Grading
There will be 7 challenging homework assignments. Homeworks must be complete... |
251 | <SYSTEM_TASK:>
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Description:
Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided som... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've ... |
252 | <SYSTEM_TASK:>
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Description:
Welcome to the Tutorial!
First I'll introduce the theory behind neural nets. then we will implement one from scratch in numpy, (which is installed on t... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
my_vector = np.asarray([1,2,3])
my_matrix = np.asarray([[1,2,3],[10,10,10]])
print(my_matrix*my_vector)
Explanation: Welcome to the Tutorial!
First I'll introduce the theory behind neural nets. then we will implement one from scratch in numpy, (which is installed on the... |
253 | <SYSTEM_TASK:>
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Description:
Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
Th... | <ASSISTANT_TASK:>
Python Code:
# Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes hand... |
254 | <SYSTEM_TASK:>
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Description:
Algorithms Exercise 3
Imports
Step2: Character counting and entropy
Write a function char_probs that takes a string and computes the probabilities of ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact
Explanation: Algorithms Exercise 3
Imports
End of explanation
def char_probs(s):
Find the probabilities of the unique characters in the string s.
Parameters
... |
255 | <SYSTEM_TASK:>
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Description:
Example H SAF Metop ASCAT SSM DR products
The following example shows how to read and plot H SAF Metop ASCAT SSM data record products using the test da... | <ASSISTANT_TASK:>
Python Code:
import os
import matplotlib.pyplot as plt
from ascat.h_saf import AscatSsmDataRecord
test_data_path = os.path.join('..', 'tests','ascat_test_data', 'hsaf')
h109_path = os.path.join(test_data_path, 'h109')
h110_path = os.path.join(test_data_path, 'h110')
h111_path = os.path.join(test_data_... |
256 | <SYSTEM_TASK:>
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Description:
GEE nested covariance structure simulation study
This notebook is a simulation study that illustrates and evaluates the performance of the GEE nested c... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import statsmodels.api as sm
Explanation: GEE nested covariance structure simulation study
This notebook is a simulation study that illustrates and evaluates the performance of the GEE nested covariance structure.
A nested covariance structure is bas... |
257 | <SYSTEM_TASK:>
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Description:
This notebook counts the number of evolutions and reversions in a binary trait on a large phylogenetic tree.
Import the dependencies - Dendropy and Pan... | <ASSISTANT_TASK:>
Python Code:
import dendropy
import pandas as pd
Explanation: This notebook counts the number of evolutions and reversions in a binary trait on a large phylogenetic tree.
Import the dependencies - Dendropy and Pandas
End of explanation
data = pd.read_csv('../Data/PyronParityData.csv', index_col=0, hea... |
258 | <SYSTEM_TASK:>
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Description:
CS231n Python Tutorial With Jupyter Notebook
This tutorial was originally written by Justin Johnson for cs231n and adapted as a Jupyter notebook for cs... | <ASSISTANT_TASK:>
Python Code:
x = 1
print(x)
Explanation: CS231n Python Tutorial With Jupyter Notebook
This tutorial was originally written by Justin Johnson for cs231n and adapted as a Jupyter notebook for cs228 by Volodymyr Kuleshov and Isaac Caswell.
This current version has been adapted as a Jupyter notebook with ... |
259 | <SYSTEM_TASK:>
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Description:
Hyperparameter tuning
Learning Objectives
1. Learn how to use cloudml-hypertune to report the results for Cloud hyperparameter tuning trial runs
2. Lea... | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
PROJECT = "<YOUR PROJECT>"
BUCKET = "<YOUR BUCKET>"
REGION = "<YOUR REGION>"
TFVERSION = "2.3.0" # TF version for AI Platform to use
import os
os.environ["PROJECT"] = PROJECT
os.environ["BUCKET"] = BUCKET
o... |
260 | <SYSTEM_TASK:>
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Description:
Step1: TV Script Generation
In this project, you'll generate your own Simpsons TV scripts using RNNs. You'll be using part of the Simpsons dataset of ... | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
Explanation: TV Script Generation
In this project, you'll generate your own ... |
261 | <SYSTEM_TASK:>
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Description:
Output Containers and Layout Managers
Output containers are objects that hold a collection of other objects, and displays all its contents, even when t... | <ASSISTANT_TASK:>
Python Code:
from beakerx import *
o = OutputContainer()
o.addItem("simplest example")
o.addItem([2, 3, 5, 7])
o.addItem(HTML("<h1>title</h1>"))
o.addItem(None)
o
rates = pd.read_csv('../resources/data/interest-rates.csv')
c = Color(120, 120, 120, 100)
plot1 = Plot(initWidth= 300, initHeight= 400)
pl... |
262 | <SYSTEM_TASK:>
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Description:
Lesson 3
Step1: Loops - Repeat with "while" and Iterate with "for"
While loop
A loop statement allows us to execute a statement or group of statements... | <ASSISTANT_TASK:>
Python Code:
from random import randint #From the random library we import the randint function.
def dice(user_input):
'''This function mimics a dice. It generates a random number between 1 and 6 and
sees if the value given by the user equals to the dice value'''
dice_value = randin... |
263 | <SYSTEM_TASK:>
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Description:
Traffic flow with an on-ramp
In this chapter we return to the LWR traffic model that we investigated in two earlier chapters. The LWR model involves a... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from clawpack import pyclaw
from clawpack import riemann
from ipywidgets import interact
from ipywidgets import widgets
from exact_solvers import traffic_ramps
from utils import riemann_tools
def c(rho, xi):
return ... |
264 | <SYSTEM_TASK:>
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Description:
Title
Step1: Make a dataframe
Step2: Add a new column for elderly | <ASSISTANT_TASK:>
Python Code:
# Import required modules
import pandas as pd
import numpy as np
Explanation: Title: Create a Column Based on a Conditional in pandas
Slug: pandas_create_column_using_conditional
Summary: Create a Column Based on a Conditional in pandas
Date: 2016-05-01 12:00
Category: Python
Tags: Data W... |
265 | <SYSTEM_TASK:>
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Description:
Reading µs-ALEX data from Photon-HDF5 with h5py
In this notebook we show how to read a µs-ALEX smFRET measurement stored in
Photon-HDF5 format
using py... | <ASSISTANT_TASK:>
Python Code:
from __future__ import division, print_function # only needed on py2
%matplotlib inline
import numpy as np
import h5py
import matplotlib.pyplot as plt
Explanation: Reading µs-ALEX data from Photon-HDF5 with h5py
In this notebook we show how to read a µs-ALEX smFRET measurement stored in
... |
266 | <SYSTEM_TASK:>
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Description:
English Wikipedia page views, 2008 - 2017
For this assignment, your job is to analyze traffic on English Wikipedia over time, and then document your pr... | <ASSISTANT_TASK:>
Python Code:
#current
import requests
endpoint = 'https://wikimedia.org/api/rest_v1/metrics/pageviews/aggregate/{project}/{access}/{agent}/{granularity}/{start}/{end}'
headers={'User-Agent' : 'https://github.com/your_github_username', 'From' : 'abhiv@uw.edu'}
params = {'project' : 'en.wikipedia.org',
... |
267 | <SYSTEM_TASK:>
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Description:
<a href="https
Step1: Manipulating data without using TFDS
We first illustrate how to make streams of minibatches using vanilla numpy code. TFDS will ... | <ASSISTANT_TASK:>
Python Code:
# Standard Python libraries
from __future__ import absolute_import, division, print_function, unicode_literals
from typing import Any, Iterator, Mapping, NamedTuple, Sequence, Tuple
import os
import time
import numpy as np
import glob
import matplotlib.pyplot as plt
import PIL
import imag... |
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Description:
Day 6 - pre-class assignment
Goals for today's pre-class assignment
Use the NumPy module to manipulate arrays of data
Write a program using if statemen... | <ASSISTANT_TASK:>
Python Code:
# Imports the functionality that we need to display YouTube videos in
# a Jupyter Notebook.
# You need to run this cell before you run ANY of the YouTube videos.
from IPython.display import YouTubeVideo
# Don't forget to watch the video in full-screen mode!
YouTubeVideo("BTXyE3KLIOs"... |
269 |
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Problem:
LightGBM Custom Loss Function
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Python Code::
import LightGBM as lgb
def custom_loss(y_pred, data):
y_true = data.get_label()
error = y_pred-y_true
#1st derivative of loss function
grad = 2 * error
#2nd derivative of loss function
hess = 0 * error + 2
return grad, hess
params = {"learning_rate" ... |
270 | <SYSTEM_TASK:>
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Description:
What is a dataset?
A dataset is a collection of information (or data) that can be used by a computer. A dataset typically has some number of examples, ... | <ASSISTANT_TASK:>
Python Code:
# Print figures in the notebook
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import datasets # Import datasets from scikit-learn
# Import patch for drawing rectangles in the legend
from matplotlib.patches ... |
271 | <SYSTEM_TASK:>
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Description:
PMOD Grove PIR Motion Sensor
This example shows how to use the Grove PIR motion sensor.
This demo requires the Grove PIR motion sensor and PYNQ Grove A... | <ASSISTANT_TASK:>
Python Code:
from time import sleep
from pynq import Overlay
from pynq.board import LED
from pynq.iop import Grove_PIR
from pynq.iop import PMODA
from pynq.iop import PMOD_GROVE_G1
ol1 = Overlay("base.bit")
ol1.download()
pir = Grove_PIR(PMODA,PMOD_GROVE_G1)
Explanation: PMOD Grove PIR Motion Sensor
T... |
272 | <SYSTEM_TASK:>
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Description:
True Values
The "true" values can be computed analytically in this case, so we did so.
We can also compute the distribution for weighting the errors.
S... | <ASSISTANT_TASK:>
Python Code:
num_states = 8
gamma = 0.9
true_values = gamma**np.arange(num_states)[::-1]
d_pi = np.ones(num_states)/num_states
D_pi = np.diag(d_pi)
print("True values:")
print(true_values)
print("On-policy distribution:")
print(d_pi)
def compute_value_dct(theta_lst, features):
return [{s: np.dot(t... |
273 | <SYSTEM_TASK:>
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Description:
Constraint Satisfaction Problems (CSPs)
This IPy notebook acts as supporting material for topics covered in Chapter 6 Constraint Satisfaction Problems ... | <ASSISTANT_TASK:>
Python Code:
from csp import *
Explanation: Constraint Satisfaction Problems (CSPs)
This IPy notebook acts as supporting material for topics covered in Chapter 6 Constraint Satisfaction Problems of the book Artificial Intelligence: A Modern Approach. We make use of the implementations in csp.py module... |
274 | <SYSTEM_TASK:>
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Description:
The train_y_nll, valid_y_nll and valid_objective show massive overfitting.
Step1: Hard to see whether it is still learning... | <ASSISTANT_TASK:>
Python Code:
m.layer_names
channel = m.monitor.channels["valid_y_nll"]
hl.Curve(zip(channel.epoch_record, channel.val_record),label="valid_y_nll")
channel = m.monitor.channels["valid_y_nll"]
plt.plot(channel.epoch_record, channel.val_record)
Explanation: The train_y_nll, valid_y_nll and valid_objectiv... |
275 | <SYSTEM_TASK:>
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Description:
The MIT License (MIT)<br>
Copyright (c) 2017 Massachusetts Institute of Technology<br>
Author
Step1: Specify list of dates
Step2: Enter Research Data... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.dpi'] = 150
from getpass import getpass
import pandas as pd
from skdaccess.framework.param_class import *
from skdaccess.geo.era_interim.cache import DataFetcher as EDF
Explanation: The MIT License (MIT)<br>
Copyright... |
276 | <SYSTEM_TASK:>
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Description:
<h1>2b. Machine Learning using tf.estimator </h1>
In this notebook, we will create a machine learning model using tf.estimator and evaluate its perform... | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.6
import tensorflow as tf
import pandas as pd
import numpy as np
import shutil
print(tf.__version__)
Explanation: <h1>2b. Machine Lear... |
277 | <SYSTEM_TASK:>
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Description:
Scientific Python Quickstart
John Stachurski
ANU
This is a fast-paced, hands-on introduction to scientific computing with Python, contained in a Jupyte... | <ASSISTANT_TASK:>
Python Code:
import sys
print(sys.version)
Explanation: Scientific Python Quickstart
John Stachurski
ANU
This is a fast-paced, hands-on introduction to scientific computing with Python, contained in a Jupyter notebook. The main focus will be on introducing Python's four most important scientific libr... |
278 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
DDM for All Control Data
Parse all control data to a single CSV, then check if subject data can fit a hierarchical drift decision model for different s... | <ASSISTANT_TASK:>
Python Code:
def parse_condition(stim_num):
if stim_num == '1':
return 'SS'
if stim_num == '2':
return 'CP'
if stim_num == '3':
return 'CS'
if stim_num == '4':
return 'US'
import csv
import glob
csv_dir = '../data/pilot_subjects/'
subjects = []
for csv_... |
279 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Exceptions
An exception is an event, which occurs during the execution of a program, that disrupts the normal flow of the program's instructions.
You'v... | <ASSISTANT_TASK:>
Python Code:
def divide1(numerator, denominator):
try:
result = numerator/denominator
print("result = %f" % result)
except:
print("You can't divide by 0!!")
divide1(1.0, 2)
divide1(1.0, 0)
divide1("x", 2)
Explanation: Exceptions
An exception is an event, which occurs during t... |
280 | <SYSTEM_TASK:>
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<END_TASK>
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Description:
Divide a sorted array in K parts with sum of difference of max and min minimized in each part
Function to find the minimum sum of differences possible for the given array... | <ASSISTANT_TASK:>
Python Code:
def calculate_minimum_split(a , k ) :
p =[]
n = len(a )
for i in range(1 , n ) :
p . append(a[i ] - a[i - 1 ] )
p . sort(reverse = True )
min_sum = sum(p[: k - 1 ] )
res = a[n - 1 ] - a[0 ] - min_sum
return res
/ * Driver code * /
if __name__== "__main __":
ar... |
281 | <SYSTEM_TASK:>
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Description:
In this exercise, you will leverage what you've learned to tune a machine learning model with cross-validation.
Setup
The questions below will give you... | <ASSISTANT_TASK:>
Python Code:
# Set up code checking
import os
if not os.path.exists("../input/train.csv"):
os.symlink("../input/home-data-for-ml-course/train.csv", "../input/train.csv")
os.symlink("../input/home-data-for-ml-course/test.csv", "../input/test.csv")
from learntools.core import binder
binder.bi... |
282 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Tarea 04
Step1: Lectura y grafica de los datos de 'TSI2.tx'
Step2: Transformada de fourier de los datos
Step3: Análisis
De la grafica de amplitudes,... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib
import pylab as plt
import scipy.misc as pim
from scipy import stats
% matplotlib inline
font = {'weight' : 'bold',
'size' : 12}
matplotlib.rc('font', **font)
Explanation: Tarea 04: Análisis de Fourier - ciclos solares
Alejando Mesa y Yennife... |
283 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
version 1.0.2
+
Introduction to Machine Learning with Apache Spark
Predicting Movie Ratings
One of the most common uses of big data is to predict wha... | <ASSISTANT_TASK:>
Python Code:
import sys
import os
from test_helper import Test
baseDir = os.path.join('data')
inputPath = os.path.join('cs100', 'lab4', 'small')
ratingsFilename = os.path.join(baseDir, inputPath, 'ratings.dat.gz')
moviesFilename = os.path.join(baseDir, inputPath, 'movies.dat')
Explanation: version 1.0... |
284 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
ES-DOC CMIP6 Model Properties - Land
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cmcc', 'cmcc-cm2-vhr4', 'land')
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: CMCC
Source ID: CMCC-CM2-VHR4
Topic: Land
Sub-Topics: Soi... |
285 | <SYSTEM_TASK:>
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Description:
<a href="https
Step1: Problem 0 - Accessing the GPU
0a) In order to run Numba functions using the GPU, we have to do a couple of things. First, go to ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import math
from numba import vectorize, cuda
from matplotlib import pyplot as plt
%matplotlib inline
Explanation: <a href="https://colab.research.google.com/github/evaneschneider/parallel-programming/blob/master/gpu_intro_solutions.ipynb" target="_parent"><img src="htt... |
286 | <SYSTEM_TASK:>
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Description:
<p><font size="6"><b>Jupyter notebook INTRODUCTION </b></font></p>
DS Data manipulation, analysis and visualisation in Python
December, 2017
© 2016, Jo... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
Image(url='http://python.org/images/python-logo.gif')
Explanation: <p><font size="6"><b>Jupyter notebook INTRODUCTION </b></font></p>
DS Data manipulation, analysis and visualisation in Python
December, 2017
© 2016, Joris Van den Bossche and Stijn Van Hoe... |
287 | <SYSTEM_TASK:>
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<END_TASK>
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Description:
Memory-efficient embeddings for recommendation systems
Author
Step1: Prepare the data
Download and process data
Step2: Create train and eval data spl... | <ASSISTANT_TASK:>
Python Code:
import os
import math
from zipfile import ZipFile
from urllib.request import urlretrieve
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers import StringLookup
import matplotlib.pyplo... |
288 | <SYSTEM_TASK:>
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Description:
Unit Tests
Overview and Principles
Testing is the process by which you exercise your code to determine if it performs as expected. The code you are tes... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
# Code Under Test
def entropy(ps):
items = ps * np.log(ps)
if any(np.isnan(items)):
raise ValueError("Cannot compute log of ps!")
return -np.sum(items)
np.isnan([.1, .9])
# Smoke test
entropy([0.5, 0.5])
Explanation: Unit Tests
Overview and Principle... |
289 | <SYSTEM_TASK:>
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Description:
In this document I would like to go through some functional idioms in Python involving the use of iterators and highlight some parallels with the equiv... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from random import random, randint, choice
from itertools import cycle, ifilter, imap, islice, izip, starmap, tee
from collections import defaultdict
from operator import add, mul
from pymonad.Maybe import *
from pymona... |
290 | <SYSTEM_TASK:>
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Description:
Simulating data and power analysis
Tom Ellis, August 2017
Before committing to the time and cost of genotyping samples for a paternity study, it is alw... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import faps as fp
import matplotlib.pylab as plt
import pandas as pd
from time import time, localtime, asctime
np.random.seed(37)
allele_freqs = np.random.uniform(0.2, 0.5, 50)
adults = fp.make_parents(10, allele_freqs, family_name='adult')
Explanation: Simulating data... |
291 | <SYSTEM_TASK:>
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Description:
Multigroup Mode Part II
Step1: We will begin by creating three materials for the fuel, water, and cladding of the fuel pins.
Step2: With our three ma... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import os
import openmc
%matplotlib inline
Explanation: Multigroup Mode Part II: MGXS Library Generation with OpenMC
The previous Notebook in this series used multi-group mode to perform a calculation with previously defined cross section... |
292 | <SYSTEM_TASK:>
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<USER_TASK:>
Description:
PDF Analysis Tutorial
Introduction
This tutorial demonstrates how to acquire a multidimensional pair distribution function (PDF) from both a flat field... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import hyperspy.api as hs
import pyxem as pxm
import numpy as np
Explanation: PDF Analysis Tutorial
Introduction
This tutorial demonstrates how to acquire a multidimensional pair distribution function (PDF) from both a flat field electron diffraction pattern and a scann... |
293 | <SYSTEM_TASK:>
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Description:
Generador de descuentos
Objetivos
Incentivar nuevas compras del cliente en el establecimiento
Fomentar el consumo de otros productos
Fomentar el consum... | <ASSISTANT_TASK:>
Python Code:
import re
from pyknow import *
Explanation: Generador de descuentos
Objetivos
Incentivar nuevas compras del cliente en el establecimiento
Fomentar el consumo de otros productos
Fomentar el consumo de productos con más margen de beneficio
Entradas y Salidas
Entrada: Lista de artículos que ... |
294 | <SYSTEM_TASK:>
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Description:
Advanced
Step1: Let's get started with some basic imports.
Step2: And then we'll build a synthetic "dataset" and initialize a new bundle with those d... | <ASSISTANT_TASK:>
Python Code:
#!pip install -I "phoebe>=2.3,<2.4"
Explanation: Advanced: mask_phases and mask_enabled
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).
End of explanation
import phoebe
from phoebe... |
295 | <SYSTEM_TASK:>
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Description:
<img style='float
Step1: Connect to server
Step2: <hr> Random binary network
A random graph where every node has the same degree, with default stylin... | <ASSISTANT_TASK:>
Python Code:
import os
from lightning import Lightning
from numpy import random, asarray, linspace, corrcoef
from colorsys import hsv_to_rgb
from sklearn import datasets
import networkx as nx
Explanation: <img style='float: left' src="http://lightning-viz.github.io/images/logo.png"> <br> <br> &n... |
296 | <SYSTEM_TASK:>
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Description:
Copyright 2020 The TensorFlow Authors.
Step1: Weight clustering in Keras example
<table class="tfo-notebook-buttons" align="left">
<td>
<a targe... | <ASSISTANT_TASK:>
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 writin... |
297 | <SYSTEM_TASK:>
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Description:
Reading BEM surfaces from a forward solution
Plot BEM surfaces used for forward solution generation.
Step1: Show result | <ASSISTANT_TASK:>
Python Code:
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname = data_path + '/subjects/sample/bem/sample-5120-5120-5120-bem-sol.fif'
surfaces = mne.read_bem_... |
298 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
==================================================
Background on Independent Component Analysis (ICA)
=================================================... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
from sklearn.decomposition import FastICA, PCA
np.random.seed(0) # set seed for reproducible results
n_samples = 2000
time = np.linspace(0, 8, n_samples)
s1 = np.sin(2 * time) # Signal 1 : sinusoidal signal
s2 =... |
299 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
模擬瀏覽器來抓
selenium (https
Step1: 找出圖片的網址
Step2: Q
直接用 urlopen 來找找看圖片網址
有了網址, 可以用 urllib 抓下來。 也可以用 spynner 來抓。
Step3: 如何換頁?
Step4: 開始回圈來抓圖吧
Step5: 已經... | <ASSISTANT_TASK:>
Python Code:
import spynner
import os, sys
from IPython.display import display, Image
# 用 spynner 打開瀏覽器
browser = spynner.Browser(debug_level=spynner.ERROR, debug_stream=sys.stderr)
browser.show() # 告訴 browser，要它之後不要隱身
# 在 ?????? 填入適當網址
base_url = 'http://v.??????.com/online/comic-7340.html?ch='
bo... |
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