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13,500 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MNIST classification with Vowpal Wabbit
Neural Net
Step1: Train
I found some help with parameters here
Step2: Predict
-t
is for test file
-i
specifies the model file created earlier... | Python Code:
from __future__ import division
import re
import numpy as np
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
%matplotlib inline
#%qtconsole
Explanation: MNIST classification with Vowpal Wabbit
Neural Net
End of explanation
!rm train.vw.cache
!rm mnist_train_nn.model
!vw -d data... |
13,501 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Keras Toy 2D binary classification
Install Keras
https
Step1: Make the dataset
Step2: Make the classifier
Step3: Bonnus | Python Code:
import tensorflow as tf
tf.__version__
import keras
keras.__version__
import h5py
h5py.__version__
import pydot
pydot.__version__
Explanation: Keras Toy 2D binary classification
Install Keras
https://keras.io/#installation
Install dependencies
Install TensorFlow backend: https://www.tensorflow.org/install/... |
13,502 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ipynb for a 2-D CNN for classifying ECGs
Best results found so far used
Step9: Import and process data
Step10: Neural Network
Step11: Test accuracy of model(s)
20% of training data held b... | Python Code:
import tensorflow as tf
#import tensorflow.contrib.learn.python.learn as learn
import tflearn
import scipy as sp
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from random import shuffle, randint
from sklearn.utils import shuffle as mutualShuf
import os
import ... |
13,503 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Day 11
Step1: Functions for parsing the initial state
Map the floors to integers
Step2: Parse an item (microchip or generator)
Step3: Parse all items on a floor
Step4: Use these function... | Python Code:
with open("input/day11.txt", "r") as f:
inputLines = tuple(line.strip() for line in f)
import itertools
import re
Explanation: Day 11: Radioisotope Thermoelectric Generators
End of explanation
floors = {
"first" : 1,
"second" : 2,
"third" : 3,
"fourth" : 4,
}
Explanation: Functions fo... |
13,504 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tree-based Methods
Tree-based methods can be used to solve regression and classification problems.
Decision Trees
A decision tree is a tree structure that partition data points into regions... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
df = pd.read_csv('data/titanic-train.csv')
df.head()
df['Survival State'] = df['Survived'].apply(lambda x: 'Survived' if x == 1 else 'Died')
df['Survival State'].value_counts()
Explanation: Tree-... |
13,505 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Language modeling
Data
The large movie view dataset contains a collection of 50,000 reviews from IMDB. The dataset contains an even number of positive and negative reviews. The authors consi... | Python Code:
PATH='data/aclImdb/'
TRN_PATH = 'train/all/'
VAL_PATH = 'test/all/'
TRN = f'{PATH}{TRN_PATH}'
VAL = f'{PATH}{VAL_PATH}'
%ls {PATH}
Explanation: Language modeling
Data
The large movie view dataset contains a collection of 50,000 reviews from IMDB. The dataset contains an even number of positive and negative... |
13,506 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Overview
The notebook shows how the lime_image tools can be applied to a smaller dataset like mnist. The dataset is very low resolution and allows quite a bit of rapid-iteration.
Step2: Set... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from skimage.color import gray2rgb, rgb2gray, label2rgb # since the code wants color images
from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784')
# make each image color so lime_image works correctly
X_vec = np.stack([gray2rgb(iimg) f... |
13,507 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
We'll start with this image
Step1: And here it is now that we've blurred it | Python Code:
imgpath = 'images/original/image.bmp'
blurredpath = 'images/image_blurred.bmp'
img = Image.open(imgpath)
blurred = img.copy().filter(ImageFilter.BLUR)
blurred.save(blurredpath)
Explanation: We'll start with this image:
End of explanation
[red_flipped, green_flipped, blue_flipped] = compare_images(imgpath, ... |
13,508 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Create Dataframe
Step2: Create Functions To Process Data
Step3: Create A Pipeline Of Those Functions | Python Code:
import pandas as pd
Explanation: Title: Create A Pipeline In Pandas
Slug: pandas_create_pipeline
Summary: Create a pipeline in pandas.
Date: 2017-01-16 12:00
Category: Python
Tags: Data Wrangling
Authors: Chris Albon
Pandas' pipeline feature allows you to string together Python functions in order to bui... |
13,509 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2A.eco - Traitement automatique de la langue en Python - correction
Correction d'exercices liés au traitement automatique du langage naturel.
Step1: On télécharge les données textuelles néc... | Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
Explanation: 2A.eco - Traitement automatique de la langue en Python - correction
Correction d'exercices liés au traitement automatique du langage naturel.
End of explanation
import nltk
nltk.download('stopwords')
Explanation: On télécharge les... |
13,510 | 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', 'mpi-m', 'mpi-esm-1-2-lr', 'aerosol')
Explanation: ES-DOC CMIP6 Model Properties - Aerosol
MIP Era: CMIP6
Institute: MPI-M
Source ID: MPI-ESM-1-2-LR
Topic: Aerosol
Sub-Topics: Transpor... |
13,511 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SiPANN
We can also leverage the sipann compact model library.
SIPANN provides with a linear regression fit from mode solver simulations to compute the Sparameters.
Straight
Step1: Coupler r... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import gdsfactory as gf
import gdsfactory.simulation.sipann as gs
def pltAttr(x, y, title=None, legend="upper right", save=None):
if legend is not None:
plt.legend(loc=legend)
plt.xlabel(x)
plt.ylabel(y)
if title is not None:
... |
13,512 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
9. Audience Upload to GMP
GMP and Google Ads Connector is used to upload audience data to GMP (e.g. Google Analytics, Campaign Manager) or Google Ads in an automatic and reliable way.
Follow... | Python Code:
# Add custom utils module to Python environment
import os
import sys
sys.path.append(os.path.abspath(os.pardir))
from IPython import display
from utils import helpers
Explanation: 9. Audience Upload to GMP
GMP and Google Ads Connector is used to upload audience data to GMP (e.g. Google Analytics, Campaign ... |
13,513 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SciPy 2016 Scikit-learn Tutorial
Out-of-core Learning - Large Scale Text Classification for Sentiment Analysis
Scalability Issues
The sklearn.feature_extraction.text.CountVectorizer and skle... | Python Code:
from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer(min_df=1)
vectorizer.fit([
"The cat sat on the mat.",
])
vectorizer.vocabulary_
Explanation: SciPy 2016 Scikit-learn Tutorial
Out-of-core Learning - Large Scale Text Classification for Sentiment Analysis
Scalabilit... |
13,514 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Continuous Training with AutoML Vertex Pipelines with Batch Predictions
Learning Objectives
Step1: BigQuery Data
If you have not gone through the KFP Walkthrough lab, you will need to run t... | Python Code:
import os
from google.cloud import aiplatform
REGION = "us-central1"
PROJECT = !(gcloud config get-value project)
PROJECT = PROJECT[0]
os.environ["PROJECT"] = PROJECT
# Set `PATH` to include the directory containing KFP CLI
PATH = %env PATH
%env PATH=/home/jupyter/.local/bin:{PATH}
Explanation: Continuous ... |
13,515 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Weight Initialization
In this lesson, you'll learn how to find good initial weights for a neural network. Having good initial weights can place the neural network close to the optimal soluti... | Python Code:
%matplotlib inline
import tensorflow as tf
import helper
from tensorflow.examples.tutorials.mnist import input_data
print('Getting MNIST Dataset...')
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
print('Data Extracted.')
Explanation: Weight Initialization
In this lesson, you'll learn how t... |
13,516 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python example using Spark SQL over Cloudant as a source
This sample notebook is written in Python and expects the Python 2.7.5 runtime. Make sure the kernel is started and you are connect t... | Python Code:
# Import Python stuff
import pprint
from collections import Counter
# Import PySpark stuff
from pyspark.sql import *
from pyspark.sql.functions import udf, asc, desc
from pyspark import SparkContext, SparkConf
from pyspark.sql.types import IntegerType
Explanation: Python example using S... |
13,517 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This kind of neural network is used in a ... | 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 handwritten numbers 0-... |
13,518 | 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 ... |
13,519 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
Is it possible to delete or insert a step in a sklearn.pipeline.Pipeline object? | Problem:
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures
estimators = [('reduce_poly', PolynomialFeatures()), ('dim_svm', PCA()), ('sVm_233', SVC())]
clf = Pipeline(estimat... |
13,520 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to Theano
For a Theano tutorial please see
Step1: Now you can invoke f and pass the input values, i.e. f(1,1), f(10,-3) and the result for this operation is returned.
Step2: P... | Python Code:
import theano
import theano.tensor as T
x = T.dscalar('x') #First input variable to the compute graph
y = T.dscalar('y') #Second input variable to the compute graph
z = 3*x + x*y + 3*y #Our formula we like to compute
#Compile for the output z, given the inputs x and y
f = theano.function(inputs=[x,y], outp... |
13,521 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Ocean
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', 'cas', 'sandbox-2', 'ocean')
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: CAS
Source ID: SANDBOX-2
Topic: Ocean
Sub-Topics: Timestepping Framework, Adve... |
13,522 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Heads-up
The following code models an inverted pendulum, and uses a GP model to determine the safe region of attraction (ROA). The following is inteded to illustrate the algorithm, not to be... | Python Code:
n = 2
m = 1
# 'Wrong' model parameters
mass = 0.1
friction = 0.
length = 0.5
gravity = 9.81
inertia = mass * length ** 2
# True model parameters
true_mass = 0.15
true_friction = 0.05
true_length = length
true_inertia = true_mass * true_length ** 2
# Input saturation
x_max = np.deg2rad(30)
u_max = gravity *... |
13,523 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Seaice
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', 'mpi-m', 'sandbox-3', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: MPI-M
Source ID: SANDBOX-3
Topic: Seaice
Sub-Topics: Dynamics, Thermodynam... |
13,524 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
More SQL
Let's grab a fairly large dataset, load it into a database, and work with it.
Getting your data
Capital Bikeshare trip data is a fun source of transactional data. We can work with ... | Python Code:
!wget https://www.capitalbikeshare.com/assets/files/trip-history-data/2013-Q1-Trips-History-Data.zip
Explanation: More SQL
Let's grab a fairly large dataset, load it into a database, and work with it.
Getting your data
Capital Bikeshare trip data is a fun source of transactional data. We can work with one... |
13,525 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Preprocessing functional near-infrared spectroscopy (fNIRS) data
This tutorial covers how to convert functional near-infrared spectroscopy
(fNIRS) data from raw measurements to relative oxyh... | Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
from itertools import compress
import mne
fnirs_data_folder = mne.datasets.fnirs_motor.data_path()
fnirs_cw_amplitude_dir = os.path.join(fnirs_data_folder, 'Participant-1')
raw_intensity = mne.io.read_raw_nirx(fnirs_cw_amplitude_dir, verbose=True... |
13,526 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Automated Clustering of Similar Amendments
The Italian Senate is clogged by computer-generated amendments. This notebook aims to cluster similar amendments in an automated fashion, so that t... | Python Code:
import os
import re
from itertools import combinations
import xml.etree.ElementTree as ET
from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram, linkage
%matplotlib inline
DATA_FOLDER = 'data/cirinna'
NAMESPACE = {'an': 'http://docs.oasis-open.org/legaldocml/ns/akn/3.0/CSD03'}... |
13,527 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A recursive neural network that decides how many times to run itself
Produces variable-length outputs for static-length inputs.
Step1: The neural network accepts an input vector of length 2... | Python Code:
import numpy as np
X = np.array([[0,0],[0,1],[1,0],[1,1]])
y = np.array([[0],[0,0],[0,0,0],[0,0,0,0]])
def sigmoid(x):
return np.matrix(1.0 / (1.0 + np.exp(-x)))
def relu(x):
alpha = 0.01
return np.maximum(x, (alpha * x))
#initialize random weights
numIn, numHid, numOut = 2, 3, 2
theta1 = np.ar... |
13,528 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Understanding recurrent neural networks
This notebook contains the code samples found in Chapter 6, Section 2 of Deep Learning with Python. Note that the original text features far more cont... | Python Code:
from keras.layers import SimpleRNN
Explanation: Understanding recurrent neural networks
This notebook contains the code samples found in Chapter 6, Section 2 of Deep Learning with Python. Note that the original text features far more content, in particular further explanations and figures: in this notebook... |
13,529 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Text Classification using TensorFlow and Google Cloud - Part 1
This bigquery-public-data
Step1: Importing libraries
Step2: 1. Source Query
Step3: 2. Raw metadata
Step4: 3. Preprocessing ... | Python Code:
import os
class Params:
pass
# Set to run on GCP
Params.GCP_PROJECT_ID = 'ksalama-gcp-playground'
Params.REGION = 'europe-west1'
Params.BUCKET = 'ksalama-gcs-cloudml'
Params.PLATFORM = 'local' # local | GCP
Params.DATA_DIR = 'data/news' if Params.PLATFORM == 'local' else 'gs://{}/data/news'.format(Par... |
13,530 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Unterricht zur Kammerprüfung
Step1: Sommer_2014
Step2: Frage 1
Erstellen Sie eine SQL-Abfrage, die alle Artikel auflistet, deren Artikelbezeichnungen die Zeichenketten "Schmerzmittel" oder... | Python Code:
%load_ext sql
Explanation: Unterricht zur Kammerprüfung
End of explanation
%sql mysql://steinam:steinam@localhost/sommer_2014
Explanation: Sommer_2014
End of explanation
%%sql
select * from artikel
where Art_Bezeichnung like '%Schmerzmittel%' or
Art_Bezeichnung like '%schmerzmittel%';
Explanation... |
13,531 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Turning transaction data into dimensional data
Let's grab a fairly large dataset, load it into a database, and create a simple dimensional model with one fact table and one dimension from it... | Python Code:
!wget https://www.capitalbikeshare.com/assets/files/trip-history-data/2013-Q1-Trips-History-Data.zip
!wget https://www.capitalbikeshare.com/assets/files/trip-history-data/2013-Q2-Trips-History-Data.zip
!wget https://www.capitalbikeshare.com/assets/files/trip-history-data/2013-Q3-Trips-History-Data.zip
!wge... |
13,532 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Self-Driving Car Engineer Nanodegree
Deep Learning
Project
Step1: Step 1
Step2: 3. Include an exploratory visualization of the dataset
Visualize the German Traffic Signs Dataset using the ... | Python Code:
# Load pickled data
import pickle
from keras.datasets import cifar10
from sklearn.model_selection import train_test_split
(X_train_temp, y_train_temp), (X_test, y_test) = cifar10.load_data()
# y_train.shape is 2d, (50000, 1). While Keras is smart enough to handle this
# it's a good idea to flatten the arra... |
13,533 | Given the following text description, write Python code to implement the functionality described.
Description:
Find sum of digits in factorial of a number
Function to multiply x with large number stored in vector v . Result is stored in v . ; Calculate res + prev carry ; updation at ith position ; Returns sum of digits... | Python Code:
def multiply(v , x ) :
carry = 0
size = len(v )
for i in range(size ) :
res = carry + v[i ] * x
v[i ] = res % 10
carry = res // 10
while(carry != 0 ) :
v . append(carry % 10 )
carry //= 10
def findSumOfDigits(n ) :
for i in range(1 , n + 1 ) :
multiply(v , i )
sum = 0... |
13,534 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to LAPM
LAPM is a python package for the analysis of linear autonomous pool (compartmental) models. It can be used to obtain a large set of different system-level diagnostics of... | Python Code:
from sympy import *
from LAPM import *
from LAPM.linear_autonomous_pool_model import LinearAutonomousPoolModel
Explanation: Introduction to LAPM
LAPM is a python package for the analysis of linear autonomous pool (compartmental) models. It can be used to obtain a large set of different system-level diagnos... |
13,535 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Convolutional Networks
So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected net... | Python Code:
# As usual, a bit of setup
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.cnn import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient
from cs231n.layers... |
13,536 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Overview of artifact detection
This tutorial covers the basics of artifact detection, and introduces the
artifact detection tools available in MNE-Python.
We begin as always by importing the... | Python Code:
import os
import numpy as np
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
raw.crop(0, 60).load_data() # j... |
13,537 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
An object-oriented, containerized model of music notation
Abjad extends the Python programming language with an object-oriented, containerized model of common practice music notation. Let's ... | Python Code:
note = abjad.Note("d''2.")
abjad.show(note)
Explanation: An object-oriented, containerized model of music notation
Abjad extends the Python programming language with an object-oriented, containerized model of common practice music notation. Let's explore the notes, rests and chords that make up the simples... |
13,538 | Given the following text description, write Python code to implement the functionality described.
Description:
Compress a Binary Tree from top to bottom with overlapping condition
Structure of a node of th tree ; Function to compress all the nodes on the same vertical line ; Stores node by compressing all nodes on the ... | Python Code:
class TreeNode :
def __init__(self , val = ' ' , left = None , right = None ) :
self . val = val
self . left = left
self . right = right
def evalComp(arr ) :
ans = 0
getBit = 1
for i in range(32 ) :
S = 0
NS = 0
for j in arr :
if getBit & j :
S += 1
else :
NS += 1... |
13,539 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Business Feasibility Overview
The purpose of this notebook is to analyze the feasibility of a business based on its intrinsic probabilities of loss/gain and return on investment in the cases... | Python Code:
# Numpy
import numpy as np
# Scipy
from scipy import stats
from scipy import linspace
# Plotly
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly.graph_objs as go
init_notebook_mode(connected=True) # Offline plotting
Explanation: Business Feasibility Overview
The pu... |
13,540 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
"Spatial Clustering" - the Galaxy Correlation Function
The degree to which objects positions are correlated with each other - "clustered" - is of great interest in astronomy.
We expect gala... | Python Code:
%load_ext autoreload
%autoreload 2
from __future__ import print_function
import numpy as np
import SDSS
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import copy
# We want to select galaxies, and then are only interested in their positions on the sky.
data = pd.read_csv("downloads/... |
13,541 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The rise of Newsletter Spam
Step1: Connect to the Gmail API
To get our emails, we will use the Gmail API. To to this we first need to enable the Gmail api and download a credential file. In... | Python Code:
import httplib2
import os
import base64
import numpy as np
import pandas as pd
import datetime
import logging
import time
import matplotlib.pyplot as plt
import seaborn as sns
from typing import Union, Any, List, Optional, cast
from googleapiclient.discovery import build
from google_auth_oauthlib.flow impo... |
13,542 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ISWC 2017 RSP Demo
Ensure you have the latest version of rsplib
Step1: A simple Experiment Using CITYBENCH Streams
Step2: Deploy The Experiment, i.e. register streams, queries and observer... | Python Code:
!pip install rsplib --upgrade
from rsplib.processing import execute, deploy
from rsplib.processing.consumer.query import *
from rsplib.experiments import Experiment, ExperimentExecution, Report
Explanation: ISWC 2017 RSP Demo
Ensure you have the latest version of rsplib
End of explanation
#create the exper... |
13,543 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Seaice
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', 'fio-ronm', 'sandbox-2', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: FIO-RONM
Source ID: SANDBOX-2
Topic: Seaice
Sub-Topics: Dynamics, Therm... |
13,544 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using a Pre-trained PyTorch Model for Inference
In this demo, we will use a pre-trained model to perform inference on a single image.
There are 3 components to this demo
Step1: Model
Step2... | Python Code:
import torch
import torchvision
import torchvision.transforms as transforms
import timm
from einops import rearrange
from PIL import Image
Explanation: Using a Pre-trained PyTorch Model for Inference
In this demo, we will use a pre-trained model to perform inference on a single image.
There are 3 compon... |
13,545 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to OpenFermion
A codealong of openfermion_tutorial.ipynb
Wayne H Nixalo – 2018/6/27
<div class="alert alert-info">
Note that all the examples below must be run sequentially with... | Python Code:
from openfermion.ops import FermionOperator
my_term = FermionOperator(((3,1), (1,0)))
print(my_term)
my_term = FermionOperator('3^ 1')
print(my_term)
Explanation: Introduction to OpenFermion
A codealong of openfermion_tutorial.ipynb
Wayne H Nixalo – 2018/6/27
<div class="alert alert-info">
Note that all th... |
13,546 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
第二章 探索数据
加载CSV
通常NumPy, pandas和matplotlib是用来进行数据分析的常用包
Step1: 创建一个变量url，指向一个csv文件。然后通过read_csv()函数来加载它。
Step2: 变量df包含了一个DataFrame对象，一种二维表的pandas数据结构。 接下来就调用head(n)方法来显示前n列的数据吧。notebook会将其显... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: 第二章 探索数据
加载CSV
通常NumPy, pandas和matplotlib是用来进行数据分析的常用包
End of explanation
url = 'http://aima.cs.berkeley.edu/data/iris.csv'
df = pd.read_csv(url,delimiter=',')
Explanation: 创建一个变量url，指向一个csv文件。然后通过read_cs... |
13,547 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Vertex SDK
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Restart the kernel
Once you've installed the additional packages, you need to restart the no... | 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 SDK: Custom training image classification model for online prediction with explain... |
13,548 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plot Trajectories with Pictures
take dimension (e.g. red) that I've trained the nn features to classify and plot sequences in that dimension.
use sequences that have images
Step1: Plot Tra... | Python Code:
# our lib
from lib.resnet50 import ResNet50
from lib.imagenet_utils import preprocess_input, decode_predictions
#keras
from keras.preprocessing import image
from keras.models import Model
import glob
def preprocess_img(img_path):
img = image.load_img(img_path, target_size=(224, 224))
x = image.img... |
13,549 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Shooting victims by block
Which Chicago block has the most shooting victims so far this year?
Fetch the data from NewsroomDB
NewsroomDB is the Tribune's proprietary database for tracking dat... | Python Code:
import os
import requests
def get_table_url(table_name, base_url=os.environ['NEWSROOMDB_URL']):
return '{}table/json/{}'.format(os.environ['NEWSROOMDB_URL'], table_name)
def get_table_data(table_name):
url = get_table_url(table_name)
try:
r = requests.get(url)
return r.json... |
13,550 | Given the following text description, write Python code to implement the functionality described.
Description:
Return True if all numbers in the list l are below threshold t.
This is how the function will work:
below_threshold([1, 2, 4, 10], 100)
True
This is how the function will work:
below_threshold(... | Python Code:
def below_threshold(l: list, t: int):
for e in l:
if e >= t:
return False
return True |
13,551 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture 18
Wednesday, November 8th, 2017
Databases with SQlite
SQLite Exercises
Today you will work with the candidates and contributors datasets to create a database in Python using SQLite.... | Python Code:
import sqlite3
Explanation: Lecture 18
Wednesday, November 8th, 2017
Databases with SQlite
SQLite Exercises
Today you will work with the candidates and contributors datasets to create a database in Python using SQLite.
The exercises will consist of a sequence of steps to help illustrate basic commands.
<a ... |
13,552 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Review from the previous lecture
In yesterday's Lecture 2, you learned how to use the numpy module, how to make your own functions, and how to import and export data. Below is a quick review... | Python Code:
import numpy as np
Explanation: Review from the previous lecture
In yesterday's Lecture 2, you learned how to use the numpy module, how to make your own functions, and how to import and export data. Below is a quick review before we move on to Lecture 3.
Remember, to use the numpy module, first it must be ... |
13,553 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
Several pieces of the puzzle have come together lately to really demonstrate the power of the scientific python software packages to handle complex dynamic and controls problems... | Python Code:
from IPython.display import SVG
SVG(filename='n-pendulum-with-cart.svg')
Explanation: Introduction
Several pieces of the puzzle have come together lately to really demonstrate the power of the scientific python software packages to handle complex dynamic and controls problems (i.e. IPython notebooks, matpl... |
13,554 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Linear Programming with OR-Tools
In this notebook, we do some basic LP solving with Google's OR-Tools. Problems used will be examples in Hamdy Taha's Operations Research
Step1: Reddy Mikks ... | Python Code:
from ortools.linear_solver import pywraplp
Explanation: Linear Programming with OR-Tools
In this notebook, we do some basic LP solving with Google's OR-Tools. Problems used will be examples in Hamdy Taha's Operations Research: An Introduction, 9th Edition, which I have in paperback.
End of explanation
redd... |
13,555 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Training and Serving with TFX and Vertex Pipelines
Learning objectives
Prepare example data.
Create a pipeline.
Run the pipeline on Vertex Pipelines.
Test with a prediction request.
Introduc... | Python Code:
# Use the latest version of pip.
!pip install --upgrade pip
!pip install --upgrade "tfx[kfp]<2"
Explanation: Training and Serving with TFX and Vertex Pipelines
Learning objectives
Prepare example data.
Create a pipeline.
Run the pipeline on Vertex Pipelines.
Test with a prediction request.
Introduction
In ... |
13,556 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<p style="text-align
Step2: 1. Implementar o algoritmo K-means
Nesta etapa você irá implementar as funções que compõe o algoritmo do KMeans uma a uma. É importante entender e ler a document... | Python Code:
# import libraries
# linear algebra
import numpy as np
# data processing
import pandas as pd
# data visualization
from matplotlib import pyplot as plt
# load the data with pandas
dataset = pd.read_csv('dataset.csv', header=None)
dataset = np.array(dataset)
plt.scatter(dataset[:,0], dataset[:,1], s=10)
p... |
13,557 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Algorithms Exercise 1
Imports
Step3: Word counting
Write a function tokenize that takes a string of English text returns a list of words. It should also remove stop words, which are common ... | Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
Explanation: Algorithms Exercise 1
Imports
End of explanation
def tokenize(s, stop_words=None, punctuation='`~!@#$%^&*()_-+={[}]|\:;"<,>.?/}\t'):
Split a string into a list of words, removing punctuation and stop words.
all_... |
13,558 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
# Defensive programming (1)
How much time do you spend writing software? How much time do you spend
debugging that software? It turns out that it is very easy to spend lots
of time fi... | Python Code:
def cell_volume(X, Y, Z):
# Return the volume of a unit cell
# described by lattice vectors X, Y and Z
# The volume is given by the determinant of
# the matrix formed by sticking the three
# vectors together. i.e.
#
# | X[0] Y[0] Z[0] |
# V = | X[1] Y[1] Z[1] |
# ... |
13,559 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network written with ... | Python Code:
import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a n... |
13,560 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regression Week 4
Step1: Polynomial regression, revisited
We build on the material from Week 3, where we wrote the function to produce an SFrame with columns containing the powers of a give... | Python Code:
import graphlab
Explanation: Regression Week 4: Ridge Regression (interpretation)
In this notebook, we will run ridge regression multiple times with different L2 penalties to see which one produces the best fit. We will revisit the example of polynomial regression as a means to see the effect of L2 regular... |
13,561 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Try not to peek at the solutions when you go through the exercises. ;-)
First let's make sure this notebook works well in both Python 2 and Python 3
Step1: Techniques for Training Deep Nets... | Python Code:
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
tf.__version__
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("tmp/data/")
Explanation: Try not to peek at the solutions when you go through the exercises... |
13,562 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Elemento Beam
Fundamento teórico
El elemento Beam (viga) es un elemento finito bidimensional donde las coordenadas locales y globales coinciden. Está caracterizado por una función de forma l... | Python Code:
%matplotlib inline
import numpy as np
from nusa import *
import itertools
import matplotlib.pyplot as plt
def pairwise(iterable):
#~ "s -> (s0,s1), (s1,s2), (s2, s3), ..."
a, b = itertools.tee(iterable)
next(b, None)
return zip(a, b)
# Input data
E = 210e9 # Pa
I = 1e-5
L = 1
P = 10e3
nelm... |
13,563 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hints to know
Step1: Example code block
print("Hello man"
Example maths
$$
y = \frac{a}{b+c}
$$
$$
\int f(x) dx
$$
$$
\int f(x)\,dx
$$
Detailed info for LaTex in jupyter
Timing code
Step2: ... | Python Code:
print("Hello man")
Explanation: Hints to know
End of explanation
def fibo(n):
if n == 0:
return 0
elif n == 1:
return 1
return fibo(n-1) + fibo(n-2)
%timeit fibo(20)
Explanation: Example code block
print("Hello man"
Example maths
$$
y = \frac{a}{b+c}
$$
$$
\int f(x) dx
$$
$$
\in... |
13,564 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Demonstrate using the simulator for a surface simulation, deterministic
integration.
Run time
Step1: Perform the simulation
Step2: Plot pretty pictures of what we just did | Python Code:
from tvb.datatypes.cortex import Cortex
from tvb.datatypes.local_connectivity import LocalConnectivity
from tvb.simulator.lab import *
Explanation: Demonstrate using the simulator for a surface simulation, deterministic
integration.
Run time: approximately 35 s (geodist step of local Connect) + ~5 min (wor... |
13,565 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Remote texting demo
Start a Mosquitto container first. For example
Step1: Start client
Step2: Prepare messages
Step3: Send out messages and get asynchonous results
Step4: Stop the demo | Python Code:
import os
import sys
import time
sys.path.append(os.path.abspath(os.path.join(os.path.pardir, '..\\codes', 'client')))
sys.path.append(os.path.abspath(os.path.join(os.path.pardir, '..\\codes', 'node')))
sys.path.append(os.path.abspath(os.path.join(os.path.pardir, '..\\codes', 'shared')))
sys.path.append(... |
13,566 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Units and Quantities
Objectives
Use units
Create functions that accept quantities as arguments
Create new units
Basics
How do we define a Quantity and which parts does it have?
Step1: Quant... | Python Code:
from astropy import units as u
# Define a quantity length
# print it
# Type of quantity
# Type of unit
# Quantity
# value
# unit
# information
Explanation: Units and Quantities
Objectives
Use units
Create functions that accept quantities as arguments
Create new units
Basics
How do we define a Quantity and ... |
13,567 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Turn magnitudes into colors
Step1: Filter out bad data
Step2: Create classification labels
Step3: Load the IDs of the narrowband population
Step4: Setup locations of images
Step5: Copy ... | Python Code:
combined["g_minus_r"] = combined.gcmodel_mag - combined.rcmodel_mag
combined["r_minus_i"] = combined.rcmodel_mag - combined.icmodel_mag
combined["i_minus_z"] = combined.icmodel_mag - combined.zcmodel_mag
combined["z_minus_y"] = combined.zcmodel_mag - combined.ycmodel_mag
Explanation: Turn magnitudes into c... |
13,568 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
STA 208
Step1: Load the following medical dataset with 750 patients. The response variable is survival dates (Y), the predictors are 104 measurements measured at a specific time (numerical ... | Python Code:
import numpy as np
import pandas as pd
# dataset path
data_dir = "."
Explanation: STA 208: Homework 2
This is based on the material in Chapters 3, 4.4 of 'Elements of Statistical Learning' (ESL), in addition to lectures 4-6. Chunzhe Zhang came up with the dataset and the analysis in the second section.
In... |
13,569 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="http
Step1: The first step in creating your own class is to use the class keyword, then the name of the class as shown in Figure 4. In this course the class parent will always be o... | Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: <a href="http://cocl.us/topNotebooksPython101Coursera"><img src = "https://ibm.box.com/shared/static/yfe6h4az47ktg2mm9h05wby2n7e8kei3.png" width = 750, align = "center"></a>
<a href="https://www.bigdatauniversity.com"><img src = "https://ibm... |
13,570 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Parsing nemeth.json in python to integrate in pybrl
nemeth.json is a file which includes the Nemeth code in order to translate LaTeX files. I found it in the latex2nemeth project by Antonis ... | Python Code:
# Import the dependencies
import six # Python 2 and 3 compatibility
import json # Load/Save JSON
import pybrl as brl # pybrl
# Load the JSON file
jdata = {}
with open("nemeth.json", 'r') as f:
jdata = json.load(f)
jdata.keys()
Explanation: Parsing nemeth.json in python to inte... |
13,571 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Finding Lane Lines on the Road
In this project, you will use the tools you learned about in the lesson to identify lane lines on the road. You can develop your pipeline on a series of indiv... | Python Code:
#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
#reading in an image
image = mpimg.imread('test_images/solidWhiteRight.jpg')
#printing out some stats and plotting
print('This image is:', type(image), 'with dim... |
13,572 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Authors.
Step3: Naive forecasting
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step4: Trend and Seasonality
Ste... | 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... |
13,573 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using Python as a Calculator
Let's try some simple python commands
Numbers
The interpreter acts as a simple calculator
Step1: With Python, use ** operator to calculate powers.
Step2: Use e... | Python Code:
4
2 + 2
50 - 5*6
(50-5)*6
8/5
8//5 # Floor division discards the fractional part
8%5 # The % operator return the remainder of the division
Explanation: Using Python as a Calculator
Let's try some simple python commands
Numbers
The interpreter acts as a simple calculator: you can type an expression at it ... |
13,574 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Correlação Cruzada - biblioteca Scipy signal.correlate2d
Passos para simulação de template matching que tem como objetivo encontrar a localização do olho direito da modelo (Lena) da imagem ... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from scipy import signal
img = mpimg.imread('../figures/lena_greyscale.png')
arr = np.asarray(img)
template = np.copy(arr[240:290, 240:290])
#plt.figure(figsize=(15,10))
plt.subplot(1,2,1)
plt.title('Template - olhos de Len... |
13,575 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
I would want to get rid of all rows missing any of the data before running the sampler.
Step1: Do Geweke Test to see if trace has converged. Burn-in looks to be around 2000.
Step2: Make pr... | Python Code:
train = train[train['longitude'] > 1]
train = train[train['latitude'] < 0]
train = train[train['construction_year'] != 0]
train = train[train['gps_height'] != 0]
features = ['longitude','latitude']
trainLoc = train[features]
#hasLocIdx = train['longitude']>1
#trainLoc = trainLoc[hasLocIdx] #remove rows wit... |
13,576 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
XGBoost Training on AI Platform
This notebook uses the Census Income Data Set to demonstrate how to train a model on Ai Platform.
How to bring your model to AI Platform
Getting your model re... | Python Code:
%env PROJECT_ID <YOUR_PROJECT_ID>
%env BUCKET_ID <YOUR_BUCKET_ID>
%env REGION <REGION>
%env TRAINER_PACKAGE_PATH ./census_training
%env MAIN_TRAINER_MODULE census_training.train
%env JOB_DIR <gs://YOUR_BUCKET_ID/xgb_job_dir>
%env RUNTIME_VERSION 1.9
%env PYTHON_VERSION 3.5
! mkdir census_training
Explanati... |
13,577 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ch 5. Interactive Data Analysis
This notebook introduces carrying out interactive data analysis of data in BigQuery using a Jupyter Notebook managed by Vertex AI Workbench.
This cell, for ex... | Python Code:
a = 3
b = a + 5
print("a={} b={}".format(a,b))
Explanation: Ch 5. Interactive Data Analysis
This notebook introduces carrying out interactive data analysis of data in BigQuery using a Jupyter Notebook managed by Vertex AI Workbench.
This cell, for example, is a mark-down cell. Which is why you are seeing ... |
13,578 | Given the following text description, write Python code to implement the functionality described.
Description:
Cumulative product of digits of all numbers in the given range
Function to get product of digits ; Function to find the product of digits of all natural numbers in range L to R ; Iterate between L to R ; Drive... | Python Code:
def getProduct(n ) :
product = 1
while(n != 0 ) :
product = product *(n % 10 )
n = int(n / 10 )
return product
def productinRange(l , r ) :
if(r - l > 9 ) :
return 0
else :
p = 1
for i in range(l , r + 1 ) :
p = p * getProduct(i )
return p
l = 11
r = 15
pri... |
13,579 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: tf.data
Step2: Basic mechanics
<a id="basic-mechanics"/>
To create an input pi... | 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 law or agreed to in... |
13,580 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Probability Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: Approximate inference for STS models with non-Gaussian observations... | 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 law or agreed to in... |
13,581 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Painting OM10 Lens Systems with Synthetic Colors
Jenny Kim, Michael Baumer, Phil Marshall
The OM10 mock lensed quasar catalog qso_mock.fits contains estimates of the lens galaxy $i$-band mag... | Python Code:
import matplotlib.pyplot as plt
import seaborn as sns
import os, matplotlib, numpy as np
import om10, corner
from om10 import plotting
from __future__ import division, print_function
from astropy.table import Table
from astropy.io import ascii
import pandas as pd
sns.set()
%load_ext autoreload
%autoreload ... |
13,582 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Read a forward operator and display sensitivity maps
Forward solutions can be read using read_forward_solution in Python.
Step1: Show gain matrix a.k.a. leadfield matrix with sensitivity ma... | Python Code:
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
import matplotlib.pyplot as plt
print(__doc__)
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sa... |
13,583 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Word2Vec Tutorial
In case you missed the buzz, word2vec is a widely featured as a member of the “new wave” of machine learning algorithms based on neural networks, commonly referred to as "d... | Python Code:
# import modules & set up logging
import gensim, logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
sentences = [['first', 'sentence'], ['second', 'sentence']]
# train word2vec on the two sentences
model = gensim.models.Word2Vec(sentences, min_count=1)
Expla... |
13,584 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Authors.
Step1: Custom layers
<table class="tfo-notebook-buttons" align="left"><td>
<a target="_blank" href="https
Step2: Layers
Step3: The full list of pre... | 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... |
13,585 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Laminar Pipe Flow</h1>
In this first tutorial, you will simulate a laminar pipe flow using the cad file <FONT FACE="courier" style="color
Step1: The wall shear stress is defined | Python Code:
D = 2.5e-2 # m
nu = 15.e-6 #m^2/s
rho = 1.2 #kg/m^3
mu = nu/rho
R = D/2
Re = 500.
Ub = Re * nu / D
print("Bulk velocity= %2.2f m/s" %Ub)
import numpy as np
n = 30
r = np.linspace(0,R,n)
U = 2 * Ub * (1 - np.power(r,2)/R**2)
import matplotlib.pyplot as plt
plt.plot(r,U,linewidth = 2)
plt.xlabel(r"$r$ (m)",... |
13,586 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial 01
Step1: This scenario, as well as all other scenarios in Flow, is parametrized by the following arguments
Step2: 2.2 VehicleParams
The VehicleParams class stores state informati... | Python Code:
from flow.scenarios.loop import LoopScenario
Explanation: Tutorial 01: Running Sumo Simulations
This tutorial walks through the process of running non-RL traffic simulations in Flow. Simulations of this form act as non-autonomous baselines and depict the behavior of human dynamics on a network. Similar sim... |
13,587 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Predicting the SPY's Future Closing Price with a Multi-Model Forecast
Creating many machine learning models to predict future price movements from Redis.
How?
Uses pricing metrics (hlocv)
St... | Python Code:
from __future__ import print_function
import sys, os, requests, json, datetime
# Load the environment and login the user
from src.common.load_redten_ipython_env import user_token, user_login, csv_file, run_job, core, api_urls, ppj, rt_url, rt_user, rt_pass, rt_email, lg, good, boom, anmt, mark, ppj, uni_ke... |
13,588 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: 迁移学习和微调
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https
Step2: 数据预处理
数据下载
在本教程中，您将使用包含数千个猫和狗图像的数据集。下载并解... | 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... |
13,589 | 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', 'noaa-gfdl', 'gfdl-esm2m', 'landice')
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: GFDL-ESM2M
Topic: Landice
Sub-Topics: Glaciers... |
13,590 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A simple Deep Neural Network using Keras
In this notebook, we are going to explore a deep neural network to classify MNIST dataset.
We have picked Keras, which is high level wrapper over The... | Python Code:
from __future__ import print_function
import keras
# For MNIST dataset
from keras.datasets import mnist
# Keras model module
from keras.models import Sequential
# Keras core layers
from keras.layers import Dense, Dropout, Flatten
# Keras CNN Layers
from keras.layers import Conv2D, MaxPooling2D
from keras i... |
13,591 | Given the following text description, write Python code to implement the functionality described.
Description:
Find 2 ^ ( 2 ^ A ) % B
Function to return 2 ^ ( 2 ^ A ) % B ; Base case , 2 ^ ( 2 ^ 1 ) % B = 4 % B ; Driver code ; Print 2 ^ ( 2 ^ A ) % B
| Python Code:
def F(A , B ) :
if(A == 1 ) :
return(4 % B ) ;
else :
temp = F(A - 1 , B ) ;
return(temp * temp ) % B ;
A = 25 ;
B = 50 ;
print(F(A , B ) ) ;
|
13,592 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Load a neuroimaging dataset
Step1: Advanced visualization with nilearn
Step2: Processing data with nilearn
Step3: Check nilearn website
http | Python Code:
import nibabel as nib
fp = '/home/grg/upf/Data/RM/rch2.nii'
im = nib.load(fp)
print im.header.keys()
print im.header['pixdim']
%matplotlib inline
im.orthoview()
from matplotlib import pyplot as plt
import numpy as np
d_t1 = np.array(im.dataobj)
d2 = d_t1[35,:,:]
plt.imshow(d2)
fp2 = '/home/grg/upf/Data/RM/... |
13,593 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 The TF-Agents Authors.
Step1: SAC minitaur with the Actor-Learner API
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Setu... | 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... |
13,594 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Constrained Local Models - Basics
The aim of this notebook is to showcase how one can build and fit CLMs to images using menpofit.
Note that this notebook assumes that the user has previousl... | Python Code:
%matplotlib inline
from pathlib import Path
path_to_lfpw = Path('/vol/atlas/databases/lfpw')
import menpo.io as mio
training_images = []
# load landmarked images
for i in mio.import_images(path_to_lfpw / 'trainset', verbose=True):
# crop image
i = i.crop_to_landmarks_proportion(0.1)
# convert i... |
13,595 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Homework #4
These problem sets focus on list comprehensions, string operations and regular expressions.
Problem set #1
Step1: In the following cell, complete the code with an expression tha... | Python Code:
numbers_str = '496,258,332,550,506,699,7,985,171,581,436,804,736,528,65,855,68,279,721,120'
Explanation: Homework #4
These problem sets focus on list comprehensions, string operations and regular expressions.
Problem set #1: List slices and list comprehensions
Let's start with some data. The following cell... |
13,596 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyri... | Python Code:
import random
import string
import mxnet as mx
from mxnet import gluon, np
import numpy as onp
Explanation: Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright owner... |
13,597 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The Shunting Yard Algorithm (Operator Precedence Parsing)
The function $\texttt{toInt}(s)$ tries to convert the string $s$ to an integer. If this works out, the integer is returned. Otherw... | Python Code:
def toInt(s):
try:
return int(s)
except ValueError:
return s
toInt('123')
toInt('**')
Explanation: The Shunting Yard Algorithm (Operator Precedence Parsing)
The function $\texttt{toInt}(s)$ tries to convert the string $s$ to an integer. If this works out, the integer is returned... |
13,598 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Evaluation of Poincare Embeddings
This notebook demonstrates how well Poincare embeddings perform on the tasks detailed in the original paper about the embeddings.
The following two external... | Python Code:
%cd ../..
# Some libraries need to be installed that are not part of Gensim
! pip install click>=6.7 nltk>=3.2.5 prettytable>=0.7.2 pygtrie>=2.2
import csv
from collections import OrderedDict
from IPython.display import display, HTML
import logging
import os
import pickle
import random
import re
import cli... |
13,599 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Source of the materials
Step1: Graphics including GenomeDiagram
The Bio.Graphics module depends on the third party Python library ReportLab. Although focused on producing PDF files, ReportL... | Python Code:
#Lets load notebook's Image
from IPython.core.display import Image
from reportlab.lib import colors
from reportlab.lib.units import cm
from Bio.Graphics import GenomeDiagram
from Bio import SeqIO
Explanation: Source of the materials: Biopython cookbook (adapted)
End of explanation
record = SeqIO.read("dat... |
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