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600 | <SYSTEM_TASK:>
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Description:
Deep LSTM RNNs
Step1: Dataset
Step2: Check the data real quick
Step3: Preparing the data for training
Step4: Long short-term memory (LSTM) RNNs
An ... | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import mxnet as mx
from mxnet import nd, autograd
import numpy as np
from collections import defaultdict
mx.random.seed(1)
# ctx = mx.gpu(0)
ctx = mx.cpu(0)
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
imp... |
601 | <SYSTEM_TASK:>
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Description:
Soil Moisture Active Passive (SMAP) Level 4 Data demo
In this demo we are downloading data using Planet OS Package-API which let's us use bigger amount... | <ASSISTANT_TASK:>
Python Code:
import time
import os
from package_api import download_data
import xarray as xr
from netCDF4 import Dataset, num2date
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
import matplotlib
import datetime
import warnings
warnings.filterwarnings("igno... |
602 | <SYSTEM_TASK:>
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Description:
This is the <a href="https
Step1: Magnetic Dipole Applet
Purpose
The objective is to learn about the magnetic field observed at the ground's surface, ... | <ASSISTANT_TASK:>
Python Code:
from geoscilabs.mag.MagDipoleApp import MagneticDipoleApp
Explanation: This is the <a href="https://jupyter.org/">Jupyter Notebook</a>, an interactive coding and computation environment. For this lab, you do not have to write any code, you will only be running it.
To use the notebook:
- ... |
603 | <SYSTEM_TASK:>
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Description:
Integración numérica Montecarlo
Referencia
Step1: Integración Montecarlo tipo 1
Se basa en la definición de valor promedio de una función y en el valo... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import YouTubeVideo
YouTubeVideo('Ti5zUD08w5s')
YouTubeVideo('jmsFC0mNayM')
Explanation: Integración numérica Montecarlo
Referencia:
- https://ocw.mit.edu/courses/mechanical-engineering/2-086-numerical-computation-for-mechanical-engineers-fall-2014/nutshells-guis/MIT2... |
604 | <SYSTEM_TASK:>
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Description:
Dependencies
Step1: Loading Data
First, we want to create our word vectors. For simplicity, we're going to be using a pretrained model.
As one of the... | <ASSISTANT_TASK:>
Python Code:
# Tensorflow
import tensorflow as tf
print('Tested with TensorFLow 1.2.0')
print('Your TensorFlow version:', tf.__version__)
# Feeding function for enqueue data
from tensorflow.python.estimator.inputs.queues import feeding_functions as ff
# Rnn common functions
from tensorflow.contrib.le... |
605 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Two time correlation example notebook
Step4: Brute force correlation
set num_levs to 1 and num_bufs to the number of images you want to correlate
Step... | <ASSISTANT_TASK:>
Python Code:
import skbeam.core.correlation as corr
from skbeam.core.correlation import two_time_corr, two_time_state_to_results
import skbeam.core.roi as roi
import skbeam.core.utils as utils
from xray_vision.mpl_plotting.roi import show_label_array_on_image
import numpy as np
import time as ttime
im... |
606 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
See go/flax-air
Flax
You probably want to keep the Flax documentation ready in another tab
Step1: Functional core
Step2: Stateless Linen module
Step3... | <ASSISTANT_TASK:>
Python Code:
# from typing import Callable, Sequence # used ?
import flax
from flax import linen as nn
Explanation: See go/flax-air
Flax
You probably want to keep the Flax documentation ready in another tab:
https://flax.readthedocs.io/
End of explanation
# Simple module with matmul layer. Note that ... |
607 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
DeepLearning
Introduction
In this notebook, we introduce H2O Deep Learning via fully-connected artificial neural networks. We also show many useful fea... | <ASSISTANT_TASK:>
Python Code:
import h2o
h2o.init(nthreads=-1)
import os.path
PATH = os.path.expanduser("~/h2o-3/")
test_df = h2o.import_file(PATH + "bigdata/laptop/mnist/test.csv.gz")
train_df = h2o.import_file(PATH + "bigdata/laptop/mnist/train.csv.gz")
Explanation: DeepLearning
Introduction
In this notebook, we int... |
608 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Visualize the MetaLearning pipeline built on top NitroML.
We are using NitroML on Kubeflow
Step1: Connect to the ML Metadata (MLMD) database
First we ... | <ASSISTANT_TASK:>
Python Code:
# Step 1: Configure your cluster with gcloud
# `gcloud container clusters get-credentials <cluster_name> --zone <cluster-zone> --project <project-id>
# Step 2: Get the port where the gRPC service is running on the cluster
# `kubectl get configmap metadata-grpc-configmap -o jsonpath={.data... |
609 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
TensorFlow Dataset API
Learning Objectives
1. Learn how use tf.data to read data from memory
1. Learn how to use tf.data in a training loop
1. Learn ho... | <ASSISTANT_TASK:>
Python Code:
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.0 || pip install tensorflow==2.0
import json
import math
import os
from pprint import pprint
import numpy as np
import tensorflow as tf
print(tf.version.VERSION)
Explanation: TensorFlow Dataset API
Lea... |
610 | <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: 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 ... |
611 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
DATASCI W261
Step1: Part 1
Step2: (1b) Sparse vectors
Data points can typically be represented with a small number of non-zero OHE features relative... | <ASSISTANT_TASK:>
Python Code:
labVersion = 'MIDS_MLS_week12_v_0_9'
%cd ~/Documents/W261/hw12/
import os
import sys
spark_home = os.environ['SPARK_HOME'] = \
'/Users/davidadams/packages/spark-1.5.1-bin-hadoop2.6/'
if not spark_home:
raise ValueError('SPARK_HOME enviroment variable is not set')
sys.path.insert(0,... |
612 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Finite-Length Performance on the BEC Channel
This code is provided as supplementary material of the lecture Channel Coding 2 - Advanced Methods.
This c... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=matplotlib.checkdep_usetex(True))
matplotlib.rc('figure', figsize=(18, 6) )
Explanation: Finite-Length Performance on the BEC Channe... |
613 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Detecção de Outliers nas Cotas Parlamentares
Primeiro, vamos investigar manualmente alguns gastos dos deputados em 2015. Em seguida, usaremos uma técni... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
ceap = pd.read_csv('dados/ceap2015.csv.zip')
linhas, colunas = ceap.shape
print('Temos {} entradas com {} colunas cada.'.format(linhas, colunas))
print('Primeira entrada:')
ceap.iloc[0]
Explanation: Detecção de Outliers nas Cotas Parlamentares
Primeiro, vamos investiga... |
614 | <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 align="center">Testing SimpleITK Setup</h1>
Check that SimpleITK and auxilliary program(s) are correctly installed in your environment, and that yo... | <ASSISTANT_TASK:>
Python Code:
import SimpleITK as sitk
from downloaddata import fetch_data, fetch_data_all
print(sitk.Version())
Explanation: <h1 align="center">Testing SimpleITK Setup</h1>
Check that SimpleITK and auxilliary program(s) are correctly installed in your environment, and that you have the SimpleITK versi... |
615 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
SVM, Undersampling and Data Cleaning for Imbalanced Data
Date created
Step1: <h3>II. Preprocessing </h3>
We process the missing values first, dropping... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split as tts
from sklearn.ensemble impo... |
616 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Data Creation
Accounts
Create several gzipped files
Each line in each file is a JSON encoded dictionary with the following keys
id
Step1: Denormalize ... | <ASSISTANT_TASK:>
Python Code:
from accounts import create_accounts_json
num_files = 25
n = 100000 # number of accounts per file
k = 500 # number of transactions
create_accounts_json(num_files, n, k)
Explanation: Data Creation
Accounts
Create several gzipped files
Each line in each file is a JSON encoded dictionary w... |
617 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
About
This notebook demonstrates several additional tools to optimize classification model provided by Reproducible experiment platform (REP) package
S... | <ASSISTANT_TASK:>
Python Code:
%pylab inline
Explanation: About
This notebook demonstrates several additional tools to optimize classification model provided by Reproducible experiment platform (REP) package:
grid search for the best classifier hyperparameters
different optimization algorithms
different scoring models... |
618 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Power spectrum example
This tutorial shows how to make and manipulate a power spectrum of two light curves using Stingray.
Step1: 1. Create a light cu... | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
import numpy as np
from stingray import Lightcurve, Powerspectrum, AveragedPowerspectrum
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
%matplotlib inline
font_prop = font_manager.FontProperties(size=16)
Explanation: Power... |
619 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Find Natural Neighbors Verification
Finding natural neighbors in a triangulation
A triangle is a natural neighbor of a point if that point is within a ... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import Delaunay
from metpy.gridding.triangles import find_natural_neighbors
# Create test observations, test points, and plot the triangulation and points.
gx, gy = np.meshgrid(np.arange(0, 20, 4), np.arange(0, 20, 4))
... |
620 | <SYSTEM_TASK:>
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
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Problem:
I would like to predict the probability from Logistic Regression model with cross-validation. I know you can get the cross-validation scores, ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
X, y = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
cv = StratifiedKFold(5).split(X, y)
logreg = LogisticRegression()
fro... |
621 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Polynomial Regression
What if your data doesn't look linear at all? Let's look at some more realistic-looking page speed / purchase data
Step1: numpy ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from pylab import *
np.random.seed(2)
pageSpeeds = np.random.normal(3.0, 1.0, 1000)
purchaseAmount = np.random.normal(50.0, 10.0, 1000) / pageSpeeds
scatter(pageSpeeds, purchaseAmount)
Explanation: Polynomial Regression
What if your data doesn't look linear at all? Let'... |
622 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Data Wrangling with OpenStreetMap <br/> Final Project
Author
Step1: Number of nodes
Step2: Number of ways
Step3: Top 10 types of amenities
Step4: T... | <ASSISTANT_TASK:>
Python Code:
bayarea.find().count()
Explanation: Data Wrangling with OpenStreetMap <br/> Final Project
Author: William Truong
Map Area: San Francisco Bay Area, CA, United States
Sources:
Mapzen Metro Extract Link
Audit and Shaping into JSON IPython Notebook
Audit and Shaping into JSON IPython Note... |
623 | <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: \title{Logic Gate Primitives in myHDL}
\author{Steven K Armour}
\maketitle
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="t... | <ASSISTANT_TASK:>
Python Code:
#This notebook also uses the `(some) LaTeX environments for Jupyter`
#https://github.com/ProfFan/latex_envs wich is part of the
#jupyter_contrib_nbextensions package
from myhdl import *
from myhdlpeek import Peeker
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%ma... |
624 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
10 Mining Social-Network Graphs
how to identify "communities"?
communities
Step1: Is Fig 10.1 typical of a social network, in the sense that it ... | <ASSISTANT_TASK:>
Python Code:
plt.imshow(plt.imread('./res/fig10_1.png'))
Explanation: 10 Mining Social-Network Graphs
how to identify "communities"?
communities: strong connections, usually overlap.
explore efficient algorithms for discovering other properities of graphs.
10.1 Social Networks as Graphs
10.1.1 W... |
625 | <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: Image Classification
In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and othe... | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if ... |
626 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Copyright 2018 The TensorFlow Authors.
Step1: tf.dataを使って画像をロードする
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href... | <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... |
627 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Fast Proportional Selection
[RETWEET]
Proportional selection -- or, roulette wheel selection -- comes up frequently when developing agent-based models.... | <ASSISTANT_TASK:>
Python Code:
import random
from bisect import bisect_left
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
Explanation: Fast Proportional Selection
[RETWEET]
Proportional selection -- or, roulette wheel selection -- comes up frequently whe... |
628 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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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... |
629 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
.. _tut_stats_cluster_methods
Step1: Set parameters
Step2: Construct simulated data
Make the connectivity matrix just next-neighbor spatially
Step... | <ASSISTANT_TASK:>
Python Code:
# Authors: Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import numpy as np
from scipy import stats
from functools import partial
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa; this changes hidden mpl vars
from mne.stats import (spatio_t... |
630 | <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 - Seaice
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributor... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3-lr', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: EC-EARTH... |
631 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Region of interest
This notebook detects signals, or regions of interest, in a spectrogram generated from a recording of the natural acoustic environme... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy.ndimage import label, find_objects
from scipy.ndimage.morphology import generate_binary_structure
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
from nacoustik import Wave
from nacoustik.spectrum import psd
from nacoustik.noise impor... |
632 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the d... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression
Explanation: Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the data, plot, and ... |
633 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Example on How to Use Net Yields
Prepared by Christian Ritter
Step1: Default setup - total yields
Step2: Setup with total yields as input but net yie... | <ASSISTANT_TASK:>
Python Code:
%matplotlib nbagg
import matplotlib.pyplot as plt
import sys
import matplotlib
import numpy as np
from NuPyCEE import sygma as s
from NuPyCEE import omega as o
from NuPyCEE import stellab
from NuPyCEE import read_yields as ry
table='yield_tables/agb_and_massive_stars_nugrid_MESAonly_fryer... |
634 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Tutorial - Distributed training in a notebook!
Using Accelerate to launch a training script from your notebook
Step1: Overview
In this tutorial we wil... | <ASSISTANT_TASK:>
Python Code:
#|all_multicuda
Explanation: Tutorial - Distributed training in a notebook!
Using Accelerate to launch a training script from your notebook
End of explanation
#hide
from fastai.vision.all import *
from fastai.distributed import *
from fastai.vision.models.xresnet import *
from accelerate ... |
635 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<h3>Current School Panda</h3>
Working with directory school data
Creative Commons in all schools
This script uses a csv file from Creative Commons New ... | <ASSISTANT_TASK:>
Python Code:
crcom = pd.read_csv('/home/wcmckee/Downloads/List of CC schools - Sheet1.csv', skiprows=5, index_col=0, usecols=[0,1,2])
Explanation: <h3>Current School Panda</h3>
Working with directory school data
Creative Commons in all schools
This script uses a csv file from Creative Commons New Zeal... |
636 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
LAB 3c
Step1: Lab Task #1
Step2: Get training information and evaluate
Let's first look at our training statistics.
Step3: Now let's evaluate our tr... | <ASSISTANT_TASK:>
Python Code:
%%bigquery
-- LIMIT 0 is a free query; this allows us to check that the table exists.
SELECT * FROM babyweight.babyweight_data_train
LIMIT 0
%%bigquery
-- LIMIT 0 is a free query; this allows us to check that the table exists.
SELECT * FROM babyweight.babyweight_data_eval
LIMIT 0
Explanat... |
637 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Copyright 2021 The TensorFlow Authors.
Step1: Human Pose Classification with MoveNet and TensorFlow Lite
This notebook teaches you how to train a pose... | <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... |
638 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Exploratory work on the ABPI Disclosure data
This notebook sets out our initial exploratory analysis of the new ABPI Disclosure data on payments from ... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
pd.set_option('display.float_format', lambda x: '%.2f' % x)
dtype = {
'Title': str,
'First Name': str,
'Last Name': str,
'Speciality': str,
'Institution Name': str
}
df = pd.read_csv('./data/payments.csv', dtype=dtype)
Explanation... |
639 | <SYSTEM_TASK:>
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Description:
Q001
Multiples of 3 and 5
If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3, 5, 6 and 9. The sum of these multiples is... | <ASSISTANT_TASK:>
Python Code:
solution = 0
N = 0
while N < 1000:
if (N % 3 == 0) or (N % 5 == 0):
solution = solution + N
N+=1
print solution
Explanation: Q001
Multiples of 3 and 5
If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3, 5, 6 and 9. The sum of these multiples... |
640 | <SYSTEM_TASK:>
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Description:
阅读笔记
作者：方跃文
Email
Step1: 基础知识
语言语义
python语言的设计特点是重视可读性、简洁性和明确性。
缩进，而不是大括号
python是通过空白符（制表符或者空格）来阻止代码的，不像R、C++等用的是大括号。该书原作者建议使用4空格作为缩进量。
万物皆对象
pytho... | <ASSISTANT_TASK:>
Python Code:
%run appendix-A/simple01.py
Explanation: 阅读笔记
作者：方跃文
Email: fyuewen@gmail.com
时间：始于2017年9月12日， 结束写作于
附录 A
附录A在原书最后，不过我自己为了复习python的一些命令，所以特意将这一部分提前到此。
python 解释器
python解释器通过“一次执行一条语句”的方式运行程序。多加利用Ipython。
通过使用 %run 命令，IPython 会在同个进程中执行指定文件中的代码。例如我在当年目录的下级目录appendix-A中创建了一个simple01.py... |
641 | <SYSTEM_TASK:>
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Description:
A Network Tour of Data Science
Michaël Defferrard, PhD student, Pierre Vandergheynst, Full Professor, EPFL LTS2.
Exercise 5
Step1: 1 Graph
Goal
Step2:... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy.spatial
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: A Network Tour of Data Science
Michaël Defferrard, PhD student, Pierre Vandergheynst, Full Professor, EPFL LTS2.
Exercise 5: Graph Signals and Fourier Transform
The goal of this exercis... |
642 | <SYSTEM_TASK:>
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Description:
Ants in Space! An introduction to the code in beam_paco__gtoc5
Luís F. Simões
2017-04
<h1 id="tocheading">Table of Contents</h1>
<div id="toc"></div>
S... | <ASSISTANT_TASK:>
Python Code:
# https://esa.github.io/pykep/
# https://github.com/esa/pykep
# https://pypi.python.org/pypi/pykep/
import PyKEP as pk
import numpy as np
from tqdm import tqdm, trange
import matplotlib.pylab as plt
%matplotlib inline
import seaborn as sns
plt.rcParams['figure.figsize'] = 10, 8
from gtoc5... |
643 | <SYSTEM_TASK:>
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Description:
ES-DOC CMIP6 Model Properties - Landice
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributo... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'sandbox-1', 'landice')
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: DWD
Source ID: SANDBOX-1
Topic: Landice
Sub-Topics: Glac... |
644 | <SYSTEM_TASK:>
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Description:
Módulo 3
Step1: Operações com Arquivos
Criando os Arquivos
Criando Estrutura de Pastas
Step5: Dataset orders.csv
Step9: Dataset stores.csv
Step13: ... | <ASSISTANT_TASK:>
Python Code:
import os
import pandas as pd
Explanation: Módulo 3: Leitura e Escrita em Arquivos + Combinando Tabelas
Tutorial
Imports para a Aula
End of explanation
try:
os.makedirs(os.path.join("data", "tutorial"))
print("Pasta criada.")
except OSError:
print("Pasta já existe!")
Explanati... |
645 | <SYSTEM_TASK:>
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Description:
Prevalence of Personal Attacks
In this notebook, we do some basic investigation into the frequency of personal attacks on Wikipedia. We will attempt to... | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
from load_utils import *
from analysis_utils import compare_groups
d = load_diffs()
df_event... |
646 | <SYSTEM_TASK:>
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Description:
Import Libraries
Step1: Read image and Check inspect values of image at different locations
Step2: RGB pixel intensity 0-255
Step3: RGB line intensi... | <ASSISTANT_TASK:>
Python Code:
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Import Libraries
End of explanation
img_RGB = cv2.imread('demo1.jpg')
plt.imshow(cv2.cvtColor(img_RGB, cv2.COLOR_BGR2RGB))
print('Shape_RGB:', img_RGB.shape)
print('Type_RGB:', img_RGB.dtype)
Explanation: Read imag... |
647 | <SYSTEM_TASK:>
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Description:
WARNING
It is a non-public API. It may change with no previous notice
We are going to show how to work with the CARTO custom visualizations (aka Kuviz)... | <ASSISTANT_TASK:>
Python Code:
USERNAME = ""
BASE_URL = "https://{u}.carto.com".format(u=USERNAME)
API_KEY = ""
from carto.auth import APIKeyAuthClient
auth_client = APIKeyAuthClient(api_key=API_KEY, base_url=BASE_URL)
Explanation: WARNING
It is a non-public API. It may change with no previous notice
We are going to sh... |
648 | <SYSTEM_TASK:>
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Description:
There are 50 observations and 5 columns. 4 columns - R&D Spend, Administration and Marketing Spend, and Profile are numeric and one is categorical - St... | <ASSISTANT_TASK:>
Python Code:
df_null_idx = df[df.isnull().sum(axis = 1) > 0].index
df.iloc[df_null_idx]
median_values = df.groupby("State")[["R&D Spend", "Marketing Spend"]].median()
median_values
df["R&D Spend"] = df.apply(lambda row: median_values.loc[row["State"], "R&D Spend"] if np.isnan(row["R&D Spend"]) else r... |
649 | <SYSTEM_TASK:>
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Description:
RichStr consist of pieces of strings and RichStrs
Step1: __repr__esentation of a rich string shows a "flat" representation of a RichStr - a sequence o... | <ASSISTANT_TASK:>
Python Code:
n=RichStr("I am ", "normal")
Explanation: RichStr consist of pieces of strings and RichStrs
End of explanation
n
Explanation: __repr__esentation of a rich string shows a "flat" representation of a RichStr - a sequence of styles and strings where style applies to everything after it. This ... |
650 | <SYSTEM_TASK:>
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Description:
You can download water chemistry of an entire HUC. It downloads wells and springs and major ions by default, unless specified otherwise.
Step1: Stand... | <ASSISTANT_TASK:>
Python Code:
chem = wa.WQP(16020301,'huc')
Explanation: You can download water chemistry of an entire HUC. It downloads wells and springs and major ions by default, unless specified otherwise.
End of explanation
Results = chem.massage_results()
Stations = chem.massage_stations()
Piv = chem.piv_chem()... |
651 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Analyse hsa-miR-124a-3p transfection time-course
In order to do this analysis you have to be in the tests directory of GEOparse.
In the paper Systemati... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import GEOparse
import pandas as pd
import pylab as pl
import seaborn as sns
pl.rcParams['figure.figsize'] = (14, 10)
pl.rcParams['ytick.labelsize'] = 12
pl.rcParams['xtick.labelsize'] = 11
pl.rcParams['axes.labelsize'] = 23
pl.rcParams['legend.fontsize'] = 20
sns.set_s... |
652 | <SYSTEM_TASK:>
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Description:
The EDEX modelsounding plugin creates 64-level vertical profiles from GFS and ETA (NAM) BUFR products distirubted over NOAAport. Paramters which are re... | <ASSISTANT_TASK:>
Python Code:
from awips.dataaccess import DataAccessLayer
import matplotlib.tri as mtri
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from math import exp, log
import numpy as np
from metpy.calc import get_wind_components, lcl, dry_lapse, parcel_profile, ... |
653 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
DataFrame object
Create SparkContext and SparkSession
Step1: Create a DataFrame object
Creat DataFrame by reading a file
Step2: Create DataFrame with... | <ASSISTANT_TASK:>
Python Code:
from pyspark import SparkContext
sc = SparkContext(master = 'local')
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("Python Spark SQL basic example") \
.config("spark.some.config.option", "some-value") \
.getOrCreate()
Explanatio... |
654 | <SYSTEM_TASK:>
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Description:
Plotting whitened data
This tutorial demonstrates how to plot
Step1: Raw data with whitening
<div class="alert alert-info"><h4>Note</h4><p>In the
St... | <ASSISTANT_TASK:>
Python Code:
import mne
from mne.datasets import sample
Explanation: Plotting whitened data
This tutorial demonstrates how to plot :term:whitened <whitening>
evoked data.
Data are whitened for many processes, including dipole fitting, source
localization and some decoding algorithms. Viewing whi... |
655 | <SYSTEM_TASK:>
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Description:
Table of Contents
<p><div class="lev1 toc-item"><a href="#Rotations" data-toc-modified-id="Rotations-1"><span class="toc-item-num">1 </span>... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import xgboost as xgb
from sklearn.metrics import roc_curve, auc
from sklearn.metrics import precision_recall_curve
df = pd.read_csv("iris.csv")
Explanation: Table of Contents
<p><div class="lev1 ... |
656 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
This notebook presents the techniques of displaying the precision of the Radio Telemetry Tracker system.
Step1: Model Estimation
To determine the loca... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt; plt.ion()
from scipy.optimize import least_squares
from scipy import stats as st
Explanation: This notebook presents the techniques of displaying the precision of the Radio Telemetry Tracker system.
End of explanation
def receivePowerMod... |
657 | <SYSTEM_TASK:>
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Description:
Locating TFBS
I will be using Biopython's Bio.motifs package. I cannot get the python 3 kernel working with the biopython package. This needs to be f... | <ASSISTANT_TASK:>
Python Code:
from Bio import motifs
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.Alphabet import IUPAC, generic_dna, generic_protein
import pandas as pd
import os, sys
Explanation: Locating TFBS
I will be using Biopython's Bio.motifs package. I cannot get the python 3 kernel working with t... |
658 | <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 - Atmos
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', 'ncc', 'noresm2-lmec', 'atmos')
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: NCC
Source ID: NORESM2-LMEC
Topic: Atmos
Sub-Topics: Dyna... |
659 | <SYSTEM_TASK:>
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Description:
ES-DOC CMIP6 Model Properties - Atmos
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', 'uhh', 'sandbox-1', 'atmos')
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: UHH
Source ID: SANDBOX-1
Topic: Atmos
Sub-Topics: Dynamical ... |
660 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Visualizing Evoked data
This tutorial shows the different visualization methods for
~mne.Evoked objects.
As usual we'll start by importing the modules ... | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import mne
Explanation: Visualizing Evoked data
This tutorial shows the different visualization methods for
~mne.Evoked objects.
As usual we'll start by importing the modules we need:
End of explanation
sample_data_folder = mne.datasets.sample.data_path()
samp... |
661 | <SYSTEM_TASK:>
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Problem:
I have a time-series A holding several values. I need to obtain a series B that is defined algebraically as follows: | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
A = pd.Series(np.random.randn(10,))
a = 2
b = 3
B = np.empty(len(A))
for k in range(0, len(B)):
if k == 0:
B[k] = a*A[k]
else:
B[k] = a*A[k] + b*B[k-1]
<END_TASK> |
662 | <SYSTEM_TASK:>
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Description:
Methane
Step1: Any Compound can contain other Compounds which can be added using its add() method. Compounds at the bottom of such a hierarchy are ref... | <ASSISTANT_TASK:>
Python Code:
import mbuild as mb
class Methane(mb.Compound):
def __init__(self):
super(Methane, self).__init__()
Explanation: Methane: Compounds and bonds
Note: mBuild expects all distance units to be in nanometers.
The primary building block in mBuild is a Compound. Anything you construct... |
663 | <SYSTEM_TASK:>
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Description:
Visualizando datos de entrada
Step1: Algoritmo de Regresion Lineal en TensorFlow
Step2: Regresion Lineal en Polinomios de grado N
Step3: Regularizac... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
# Regresa 101 numeros igualmmente espaciados en el intervalo[-1,1]
x_train = np.linspace(-1, 1, 101)
# Genera numeros pseudo-aleatorios multiplicando la matriz x_train * 2 y
# sumando a cada elemento un ruido (una matriz del mismo tamani... |
664 | <SYSTEM_TASK:>
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Description:
Midterm Review
CSCI 1360E
Step1: Answering this is not simply taking what's in the autograder and copy-pasting it into your solution
Step2: The whole... | <ASSISTANT_TASK:>
Python Code:
number = 3.14159265359
Explanation: Midterm Review
CSCI 1360E: Foundations for Informatics and Analytics
Material
Anything in Lectures 1 through 10 are fair game!
Anything in assignments 1 through 4 are fair game!
Topics
Data Science
- Definition
- Intrinsic interdisciplinarity
- "Grea... |
665 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
So my the code for my solution can be found in
Step1: The above bit of boiler-plate code is useful in a number of situations. Indeed, this is a patter... | <ASSISTANT_TASK:>
Python Code:
## Assume that this code exists in a file named example.py
def main():
print(1 + 1)
if __name__ == "__main__":
main()
Explanation: So my the code for my solution can be found in:
../misc/minesweeper.py
In this lecture I shall be going through some bits of code and explaining parts... |
666 | <SYSTEM_TASK:>
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Description:
Analyzing Data using Python and SQLite3
SQLite basics
Create a connection
conn = sqlite3.connect('database_file')
cur = conn.curser()
Execute SQL comma... | <ASSISTANT_TASK:>
Python Code:
import sqlite3
conn = sqlite3.connect('election_tweets.sqlite')
cur = conn.cursor()
Explanation: Analyzing Data using Python and SQLite3
SQLite basics
Create a connection
conn = sqlite3.connect('database_file')
cur = conn.curser()
Execute SQL commands
execute: cur.execute('SQL COMMANDS')
... |
667 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Model16
Step1: Right, now, you can use those module.
GMM
Classifying questions
features
Step3: B. Modeling
Select model
Step4: n_iter=10 | <ASSISTANT_TASK:>
Python Code:
from utils import load_buzz, select, write_result
from features import featurize, get_pos
from containers import Questions, Users, Categories
Explanation: Model16: Extract common functions
Now, we know what kind of common functions we need. So, I have make some functions which we used as ... |
668 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
A-scan from a metal cylinder (2D)
This example is the GPR modelling equivalent of 'Hello World'! It demonstrates how to simulate a single trace (A-scan... | <ASSISTANT_TASK:>
Python Code:
%%writefile ../../user_models/cylinder_Ascan_2D.in
#title: A-scan from a metal cylinder buried in a dielectric half-space
#domain: 0.240 0.210 0.002
#dx_dy_dz: 0.002 0.002 0.002
#time_window: 3e-9
#material: 6 0 1 0 half_space
#waveform: ricker 1 1.5e9 my_ricker
#hertzian_dipole: z 0.100 ... |
669 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Regression Week 1
Step1: Load house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
Step2:... | <ASSISTANT_TASK:>
Python Code:
import graphlab
Explanation: Regression Week 1: Simple Linear Regression
In this notebook we will use data on house sales in King County to predict house prices using simple (one input) linear regression. You will:
* Use graphlab SArray and SFrame functions to compute important summary st... |
670 | <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:
Healthy Communities Data and Indicators Project (HCI)
Healthy Communities Data and Indicators Project (HCI)
Step2: First, create a set of views to lim... | <ASSISTANT_TASK:>
Python Code:
from ambry import get_library
l = get_library()
b = l.bundle('cdph.ca.gov-hci-0.0.2')
Explanation: Healthy Communities Data and Indicators Project (HCI)
Healthy Communities Data and Indicators Project (HCI)
End of explanation
w = b.warehouse('hci_counties')
w.clean()
print w.dsn
w.query(
... |
671 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
MIDS - w261 Machine Learning At Scale
Course Lead
Step1: <a name="HW10.1.1"><h2 style="color
Step2: <a name="HW10.2"> <h2 style="color
Step3: <a nam... | <ASSISTANT_TASK:>
Python Code:
## Code goes here
## Drivers & Runners
## Run Scripts, S3 Sync
Explanation: MIDS - w261 Machine Learning At Scale
Course Lead: Dr James G. Shanahan (email Jimi via James.Shanahan AT gmail.com)
Assignment - HW10
Name: Your Name Goes Here
Class: MIDS w261 (Section Your Section Goes Here,... |
672 | <SYSTEM_TASK:>
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Description:
benchmarking for the basic document and efficient document
Step1: Plot the Basic and Efficient data first
Basic
Step2: do a linear regression for the... | <ASSISTANT_TASK:>
Python Code:
data = pd.read_csv('../benchMarkingResult.txt',
header=None,
sep='\t',
names=('iteration',
'basic_result',
'efficient_result'))
Explanation: benchmarking for the basic document and... |
673 | <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:
SCat Algorithm
I have pushed multiple iterations of a simple sequence labeling algorithm called SCat. This iteration is the first version that will be ... | <ASSISTANT_TASK:>
Python Code:
# Find the city in a weather related query
train_x = [
"What is the weather like in Paris ?",
"What kind of weather will it do in London ?",
"Give me the weather forecast in Berlin please .",
"Tell me the forecast in New York !",
"Give me the weather in San Francisco .... |
674 | <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:
Writing Low-Level TensorFlow Code
Learning Objectives
Practice defining and performing basic operations on constant Tensors
Use Tensorflow's automatic ... | <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==2.1 || pip install tensorflow==2.1
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
print(tf.__version__)... |
675 | <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:
Loads the mechanical Turk data
Run this script to load the data. Your job after loading the data is to make a 20 questions style game (see www.20q.net ... | <ASSISTANT_TASK:>
Python Code:
# Read in the list of 250 movies, making sure to remove commas from their names
# (actually, if it has commas, it will be read in as different fields)
import csv
movies = []
with open('movies.csv','r') as csvfile:
myreader = csv.reader(csvfile)
for index, row in enumerate(myreader... |
676 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Basic TensorFlow with GPU
Step1: Multiply 2 matrices
Step2: Sessions must be closed to release resources. We may use the 'with' syntax to close sess... | <ASSISTANT_TASK:>
Python Code:
!nvidia-smi
import tensorflow as tf
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
logdir = '/root/pipeline/logs/tensorflow'
import numpy as np
import matplotlib.pyplot as plt
import datetime
from tensorflow.python.framework import ops
from tensorflow.python.platform ... |
677 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Example
Step2: The first thing we shall need is holdings data. For this example, we assume that holdings data is provided in a CSV format, and insert ... | <ASSISTANT_TASK:>
Python Code:
import loman
comp = loman.Computation()
Explanation: Example: Using Loman to Value a Portfolio
In this example, we'll look at valuing a simple portfolio composed of equities and futures. In additional, we'll calculate an intraday P&L, and some simple exposure measures.
The main challenge ... |
678 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Exact solution used in MES runs
We would like to MES the operation
$$
\nabla \cdot \mathbf{f}_\perp
$$
Using cylindrical geometry.
Step1: Initialize
S... | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
from sympy import init_printing
from sympy import S
from sympy import sin, cos, tanh, exp, pi, sqrt
from boutdata.mms import x, y, z, t
from boutdata.mms import Delp2, DDX, DDY, DDZ
import os, sys
# If we add to sys.path, then it must be an absolute path
common_dir = ... |
679 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Resolviendo un Laberinto
El problema es muy sencillo
Step1: Pintemos el laberinto!
Step2: Queda chulo, ¿verdad?
Creando un camino a partir de un geno... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import laberinto.algen as ag
import laberinto.laberinto as lab
import numpy as np
import matplotlib.pyplot as plt
mapa1 = lab.Map()
Explanation: Resolviendo un Laberinto
El problema es muy sencillo: tenemos un laberinto, y deseamos que nuestro algoritmo genético encuent... |
680 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Facies classification using Machine Learning
aaML Submission
By
Step1: Loading the data training data without Shankle well
Step2: Loading the SHANKLE... | <ASSISTANT_TASK:>
Python Code:
from libtools import *
Explanation: Facies classification using Machine Learning
aaML Submission
By:
Alexsandro G. Cerqueira,
Alã de C. Damasceno
There are tow main notebooks:
Data Analysis and edition
Submission
End of explanation
training = pd.read_csv('data-test.csv')
training.head()... |
681 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
First Taste of Plotly
Step1: Try Plotting a Dirichlet Distribution
Step4: Make an Interactive 3D Plot with Parameter Selection | <ASSISTANT_TASK:>
Python Code:
trace0 = go.Scatter(
x=[1, 2, 3, 4],
y=[10, 15, 13, 17]
)
trace1 = go.Scatter(
x=[1, 2, 3, 4],
y=[16, 5, 11, 9]
)
data = go.Data([trace0, trace1])
py.iplot(data, filename = 'basic-line')
Explanation: First Taste of Plotly
End of explanation
alpha = np.array([5, 5, 5])
rv =... |
682 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Distribution Test Tables
This example demonstrates how to create some conditional probability tables and a bayesian network.
Step1: First let's define... | <ASSISTANT_TASK:>
Python Code:
from pomegranate import *
import math
Explanation: Distribution Test Tables
This example demonstrates how to create some conditional probability tables and a bayesian network.
End of explanation
c_table = [[0, 0, 0, 0.6],
[0, 0, 1, 0.4],
[0, 1, 0, 0.7],
[0... |
683 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Objective
Build a Sentiment Classifier
Step1: Load Data
Load Training Data
Split into Train and Validation
Step2: Vectorize
Vectorize using Scikit-Le... | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function # Python 2/3 compatibility
import numpy as np
import pandas as pd
from IPython.display import Image
Explanation: Objective
Build a Sentiment Classifier
End of explanation
## Your Turn
Explanation: Load Data
Load Training Data
Split into Train and Val... |
684 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Simulating with FBA
Simulations using flux balance analysis can be solved using Model.optimize(). This will maximize or minimize (maximizing is the def... | <ASSISTANT_TASK:>
Python Code:
from cobra.io import load_model
model = load_model("textbook")
Explanation: Simulating with FBA
Simulations using flux balance analysis can be solved using Model.optimize(). This will maximize or minimize (maximizing is the default) flux through the objective reactions.
End of explanation... |
685 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Mixed Precision Training
Introduction
Traditionally, for training a neural network, we used to use FP32 for weights and activations; however computatio... | <ASSISTANT_TASK:>
Python Code:
ctx = get_extension_context("cudnn", type_config="half")
Explanation: Mixed Precision Training
Introduction
Traditionally, for training a neural network, we used to use FP32 for weights and activations; however computation costs for training a neural network rapidly increase over years as... |
686 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Rich Output
In Python, objects can declare their textual representation using the __repr__ method. IPython expands on this idea and allows objects to ... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import display
Explanation: Rich Output
In Python, objects can declare their textual representation using the __repr__ method. IPython expands on this idea and allows objects to declare other, rich representations including:
HTML
JSON
PNG
JPEG
SVG
LaTeX
A single obje... |
687 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Map election results to regions.
Assumes you have huffpostdata/election-2012-results cloned at ../../election-2012-results. Does 2000 regions by defaul... | <ASSISTANT_TASK:>
Python Code:
from __future__ import division, print_function
%matplotlib inline
import numpy as np
import pandas as pd
import re
import six
from IPython.display import display
import sys
sys.path.append('..')
from pummeler.data import geocode_data
county_to_region = geocode_data('county_region_10').re... |
688 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
ES-DOC CMIP6 Model Properties - Ocean
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', 'nasa-giss', 'sandbox-1', 'ocean')
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: SANDBOX-1
Topic: Ocean
Sub-Topics... |
689 | <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:
Assign NERC labels to plants using 860 data and RandomForest
Instructions
Make sure the file_date parameter below is set to whatever value you would li... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import os
from os.path import join
import pandas as pd
from sklearn import neighbors, metrics
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split, GridSearchCV
from collections import Counte... |
690 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
1. Кластеризация
Выбор оптимального количества кластеров
Кластерный анализ (Data clustering) — это задача разбиения заданной выборки объектов (ситуаци... | <ASSISTANT_TASK:>
Python Code:
#импортируем библиотеки
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from sklearn.cluster import DBSCAN
plt.figure(figsize=(12, 12))
n_samples = 2300
random_state = 220
X, y = make_blobs(n_samples=n_samples, ... |
691 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Chapter 12 – Distributed TensorFlow
This notebook contains all the sample code and solutions to the exercises in chapter 12.
Setup
First, let's make su... | <ASSISTANT_TASK:>
Python Code:
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
def reset_graph(seed=42):
tf.reset_default_graph()
tf.set_random_seed(seed)
... |
692 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Update domain in Research
Sometimes one needs to change the domain of parameters during Research execution. update_domain mathod helps to do that.
We s... | <ASSISTANT_TASK:>
Python Code:
import sys
import os
import shutil
import numpy as np
import matplotlib
%matplotlib inline
os.environ["CUDA_VISIBLE_DEVICES"] = "6"
sys.path.append('../../..')
from batchflow import Pipeline, B, C, V, D, L
from batchflow.opensets import CIFAR10
from batchflow.models.torch import VGG7, VGG... |
693 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
The FrameAgentType is an alternative way to specify a model.
The library contains a demonstration of this form of model, ConsPortfolioFrameModel, which... | <ASSISTANT_TASK:>
Python Code:
pct = cpm.PortfolioConsumerType(T_sim=5000, AgentCount=200)
pct.cycles = 0
# Solve the model under the given parameters
pct.solve()
pct.track_vars += [
"mNrm",
"cNrm",
"Share",
"aNrm",
"Risky",
"Adjust",
"PermShk",
"TranShk",
"bNrm",
"who_dies"
]
pc... |
694 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Projecting terrestrial biodiversity using PREDICTS and LUH2
This notebook shows how to use rasterset to project a PREDICTS model using the LUH2 land-us... | <ASSISTANT_TASK:>
Python Code:
import click
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import numpy.ma as ma
import rasterio
from rasterio.plot import show, show_hist
Explanation: Projecting terrestrial biodiversity using PREDICTS and LUH2
This notebook shows how to use rasterset to project a... |
695 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
SVM Learning
Load Dataset
Step1: Train-Test Split
Before we construct out model pipeline, we divide the dataset into separate a separate training data... | <ASSISTANT_TASK:>
Python Code:
from coherence import load_coherence_dataset
coherence_ds = load_coherence_dataset()
X = coherence_ds.data
y = coherence_ds.target
Explanation: SVM Learning
Load Dataset
End of explanation
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_... |
696 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
ES-DOC CMIP6 Model Properties - Toplevel
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contribut... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'sandbox-3', 'toplevel')
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: MIROC
Source ID: SANDBOX-3
Sub-Topics: Radiative For... |
697 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
<small><i>This notebook was put together by Jake Vanderplas. Source and license info is on GitHub.</i></small>
Basic Principles of Machine Learning
Her... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn')
Explanation: <small><i>This notebook was put together by Jake Vanderplas. Source and license info is on GitHub.</i></small>
Basic Principles of Machine Learning
Here we'll dive into the basic pr... |
698 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
<< nazaj
Step1: 3. naloga
Step2: Polje za diskusijo
Naslednje kode ni treba poganjati, saj je namenjena le temu, da se na koncu zvezka prikaže polje ... | <ASSISTANT_TASK:>
Python Code:
1+1
Explanation: << nazaj: Predgovor
Uvod
Preden se lotimo trenja matematičnih orehov s kladivom imenovanim Python, si moramo pripraviti primerno okolje.
Dokumenti so napisani v obliki Jupyter notebook, ki je interaktivno okolje za Python, v katerem lahko združujemo programsko kodo in be... |
699 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
<a href="https
Step1: Environment Preparation
Install bigdl-chronos
You can install the latest pre-release version with chronos support using pip inst... | <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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... |
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