Unnamed: 0 int64 0 16k | text_prompt stringlengths 110 62.1k | code_prompt stringlengths 37 152k |
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11,600 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step5: Factor Network Functions
Messenger Class
Performs transformations on data.
eg. f(x) -> y
Decoupled from the other factor network code, and can be swapped with other implementations... | Python Code:
class Messenger:
def __init__(self, config='cdr', size=2000):
:param url: str
Fully qualified url to an elasticsearch instance
:param size: int|
Size limit to set on elasticsearch query
self.conn = connections.get_connection(config)
... |
11,601 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
练习 1：写函数，求n个随机整数均值的平方根，整数范围在m与k之间。
Step1: 练习 2：写函数，共n个随机整数，整数范围在m与k之间，求西格玛log(随机整数)及西格玛1/log(随机整数)
Step2: 练习 3：写函数，求s=a+aa+aaa+aaaa+aa...a的值，其中a是[1,9]之间的随机整数。例如2+22+222+2222+22222(此时共有5个数相... | Python Code:
import random,math
def Square():
m=int(input('plz input the min '))
k=int(input('plz input the max '))
n=int(input('plz input n : '))
i=0
total=0
while i<n:
i+=1
temp=random.randint(m,k)
total+=temp
print (math.sqrt(total/n))
Square()
Explanation: 练习... |
11,602 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Decision Trees in Practice
In this assignment we will explore various techniques for preventing overfitting in decision trees. We will extend the implementation of the binary decision trees ... | Python Code:
import numpy as np
import pandas as pd
import json
Explanation: Decision Trees in Practice
In this assignment we will explore various techniques for preventing overfitting in decision trees. We will extend the implementation of the binary decision trees that we implemented in the previous assignment. You w... |
11,603 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Logistic Regression with Grid Search (scikit-learn)
<a href="https
Step1: This example features
Step2: Imports
Step3: Log Workflow
This section demonstrates logging model metadata and tra... | Python Code:
# restart your notebook if prompted on Colab
try:
import verta
except ImportError:
!pip install verta
Explanation: Logistic Regression with Grid Search (scikit-learn)
<a href="https://colab.research.google.com/github/VertaAI/modeldb/blob/master/client/workflows/demos/census-end-to-end.ipynb" target... |
11,604 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Test suite for Jupyter-notebook
Sample example of use of PyCOMPSs from Jupyter
First step
Import ipycompss library
Step1: Second step
Initialize COMPSs runtime
Parameters indicates if the e... | Python Code:
import pycompss.interactive as ipycompss
Explanation: Test suite for Jupyter-notebook
Sample example of use of PyCOMPSs from Jupyter
First step
Import ipycompss library
End of explanation
ipycompss.start(graph=True, trace=True, debug=True, project_xml='../project.xml', resources_xml='../resources.xml', com... |
11,605 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Predicting house prices using k-nearest neighbors regression
In this notebook, we will implement k-nearest neighbors regression. You will
Step1: Unzipping files with house sales data
For th... | Python Code:
import os
import zipfile
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
%matplotlib inline
Explanation: Predicting house prices using k-nearest neighbors regression
In this notebook, we will implement k-nearest... |
11,606 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Comparaison $T_{ext}$ mesurée et celle de la météo
Step1: Comparaison avec une autre position GPS
Step2: Data from ROMMA
http
Step3: laquelle est correcte ??
à priori Romma | Python Code:
coords_grenoble = (45.1973288, 5.7139923) #(45.1973288, 5.7103223)
startday, lastday = pd.to_datetime('22/06/2017', format='%d/%m/%Y'), pd.to_datetime('now')
# download the data:
data = wf.buildmultidayDF(startday, lastday, coords_grenoble )
import emoncmsfeed as getfeeds
dataframefreq = '10min'
feeds = {... |
11,607 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PyGSLIB
PPplot
Step1: Getting the data ready for work
If the data is in GSLIB format you can use the function pygslib.gslib.read_gslib_file(filename) to import the data into a Pandas DataFr... | Python Code:
#general imports
import pygslib
Explanation: PyGSLIB
PPplot
End of explanation
#get the data in gslib format into a pandas Dataframe
mydata= pygslib.gslib.read_gslib_file('../datasets/cluster.dat')
true= pygslib.gslib.read_gslib_file('../datasets/true.dat')
true['Declustering Weight'] = 1
Explanation: G... |
11,608 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ARC2 download example
In this demo we show how to download ARC2 data
Step1: <font color='red'>Please put your datahub API key into a file called APIKEY and place it to the notebook folder o... | Python Code:
%matplotlib notebook
import dh_py_access.lib.datahub as datahub
import dh_py_access.package_api as package_api
Explanation: ARC2 download example
In this demo we show how to download ARC2 data
End of explanation
server = 'api.planetos.com'
API_key = open('APIKEY').readlines()[0].strip() #'<YOUR API KEY HER... |
11,609 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Classify handwritten digits with Keras
Data from
Step1: <a id="01">1. Download the MNIST dataset from Internet </a>
I've made the dataset into a zipped tar file. You'll have to download it ... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set()
import pandas as pd
import sklearn
import os
import requests
from tqdm._tqdm_notebook import tqdm_notebook
import tarfile
Explanation: Classify handwritten digits with Keras
Data from: the MNIST dataset
Do... |
11,610 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introducing CivisML 2.0
Note
Step1: Downloading data
Before we build any models, we need a dataset to play with. We're going to use the most recent College Scorecard data from the Departmen... | Python Code:
# first, let's import the packages we need
import requests
from io import StringIO
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import model_selection
# import the Civis Python API client
import civis
# ModelPipeline is the class used to build Ci... |
11,611 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Class 03 - Supplemental
Using Categorical data in machine learning
Now that we've created some categorical data or other created features, we would like to use them as inputs for our machine... | Python Code:
import pandas as pd
import numpy as np
sampledata = pd.read_csv('Class03_supplemental_data.csv')
print(sampledata.dtypes)
sampledata.head()
Explanation: Class 03 - Supplemental
Using Categorical data in machine learning
Now that we've created some categorical data or other created features, we would like t... |
11,612 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
El "problema"
Es posible que al usar funciones con parámetros por defecto se encuentren con cierto comportamiento inesperado o poco intuitivo de Python. Por estas cosas siempre hay que revis... | Python Code:
def funcion(lista=[]):
lista.append(1)
print("La lista vale: {}".format(lista))
Explanation: El "problema"
Es posible que al usar funciones con parámetros por defecto se encuentren con cierto comportamiento inesperado o poco intuitivo de Python. Por estas cosas siempre hay que revisar el código, co... |
11,613 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
数据应用学院 Data Scientist Program Hw2
<h1 id="tocheading">Table of Contents</h1>
<div id="toc"></div>
Step1: 1. Gnerate x = a sequence of points, y = sin(x)+a where a is a small random error.
S... | Python Code:
%%javascript
$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')
# import the necessary package at the very beginning
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import sklearn
Explanation: 数据应用学院 Data Scientist Program H... |
11,614 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
< 04 - Time and Chronology | Home | 06 - Stable Roommates, Marriages, and Gender >
Cliques and Communities
Step1: Communities are just as important in the social structure of novels as th... | Python Code:
from bookworm import *
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (12,9)
import pandas as pd
import numpy as np
import networkx as nx
Explanation: < 04 - Time and Chronology | Home | 06 - Stable Roommates, Marriages, and Gender >
Cliques and Communities
End of exp... |
11,615 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I use linear SVM from scikit learn (LinearSVC) for binary classification problem. I understand that LinearSVC can give me the predicted labels, and the decision scores but I wanted ... | Problem:
import numpy as np
import pandas as pd
import sklearn.svm as suppmach
X, y, x_test = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
assert type(x_test) == np.ndarray
# Fit model:
svmmodel=suppmach.LinearSVC()
from sklearn.calibration import CalibratedClassifierCV
calibrated_svc = Calibra... |
11,616 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: Recurrent Neural Networks (RNN) with Keras
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: ... | 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... |
11,617 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
KNN
Motivation
The principle behind nearest neighbor methods is to find a predefined number of training samples closest in distance to the new point, and predict the label from these. The nu... | Python Code:
import pandas
import numpy
import csv
#from scipy.stats import mode
from sklearn import neighbors
from sklearn.neighbors import DistanceMetric
from pprint import pprint
MY_TITANIC_TRAIN = 'train.csv'
MY_TITANIC_TEST = 'test.csv'
titanic_dataframe = pandas.read_csv(MY_TITANIC_TRAIN, header=0)
print('length... |
11,618 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Here is an example of simulated sea surface height from the NEMO model run at 1/4°, that is represented by an xarray.DataArray object. Note that dask array can be used by precising chunks.
S... | Python Code:
signal_xyt = xr.open_dataset(sigdir + test_file, decode_times=False)['sossheig'].chunk(chunks={'time_counter': 50})
print signal_xyt
signal_xyt.isel(time_counter=0).plot(vmin=-0.07, vmax=0.07, cmap='seismic')
Explanation: Here is an example of simulated sea surface height from the NEMO model run at 1/4°, t... |
11,619 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Denavit Hartenberg Notation
Kevin Walchko
Created
Step1: Rise of the Robots
Robot arms and legs are hard to control (lots of math), but are required for most robotic applications.
<img src... | Python Code:
%matplotlib inline
from __future__ import print_function
from __future__ import division
import numpy as np
from math import cos, sin, pi
from IPython.display import HTML # need this for embedding a movie in an iframe
Explanation: Denavit Hartenberg Notation
Kevin Walchko
Created: 10 July 2017
Denavit Hart... |
11,620 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Preprocessing for Machine Learning
Learning Objectives
* Understand the different approaches for data preprocessing in developing ML models
* Use Dataflow to perform data preprocessing ... | Python Code:
#Ensure that we have the correct version of Apache Beam installed
!pip freeze | grep apache-beam || sudo pip install apache-beam[gcp]==2.12.0
import tensorflow as tf
import apache_beam as beam
import shutil
import os
print(tf.__version__)
Explanation: Data Preprocessing for Machine Learning
Learning Object... |
11,621 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Collaborative filtering on the MovieLense Dataset
Learning objectives
1. Explore the data using BigQuery.
2. Use the model to make recommendations for a user.
3. Use the model to recommend a... | Python Code:
import os
import tensorflow as tf
PROJECT = "your-project-here" # REPLACE WITH YOUR PROJECT ID
# Do not change these
os.environ["PROJECT"] = PROJECT
os.environ["TFVERSION"] = '2.6'
%%bash
mkdir bqml_data
cd bqml_data
curl -O 'http://files.grouplens.org/datasets/movielens/ml-20m.zip'
unzip ml-20m.zip
yes | ... |
11,622 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualize the model
https
Step1: Show convolutional filters
Step2: Show activations with quiver
Install quiver | Python Code:
from keras.models import load_model,Model
import dogs_vs_cats as dvc
import numpy as np
modelname = "cnn_model_trained.h5"
cnn_model = load_model(modelname)
# Load some data
from keras.applications.imagenet_utils import preprocess_input
all_files = dvc.image_files()
all_files = np.array(all_files)
files_te... |
11,623 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lesson 17
Step1: Dictionaries are equivalent.
Step2: A Dictionary cannot lookup a value that is not available.
Step3: You can check if a variable is in a dictionary.
Step4: You can use m... | Python Code:
eggs = {
'name': 'Zophie',
'species': 'cat',
'age': 8
}
ham = {
'species': 'cat',
'name': 'Zophie',
'age': 8
}
print(eggs)
print(ham)
Explanation: Lesson 17:
The Dictionary Data Type
Switched to the Jupyter Notebook for REPL convenience.
Dictionaries use key pairs to store ... |
11,624 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementat... | Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of t... |
11,625 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I have two csr_matrix, c1, c2. | Problem:
from scipy import sparse
c1 = sparse.csr_matrix([[0, 0, 1, 0], [2, 0, 0, 0], [0, 0, 0, 0]])
c2 = sparse.csr_matrix([[0, 3, 4, 0], [0, 0, 0, 5], [6, 7, 0, 8]])
Feature = sparse.hstack((c1, c2)).tocsr() |
11,626 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Estimating Current Cases by Category
This notebook explores a methodology to estimate current mild, severe and critical patients. Both mild and critical categories appear to be correlated to... | Python Code:
# Since reported numbers are approximate, they are rounded for the sake of simplicity
severe_ratio = .15
critical_ratio = .05
mild_ratio = 1 - severe_ratio - critical_ratio
Explanation: Estimating Current Cases by Category
This notebook explores a methodology to estimate current mild, severe and critical p... |
11,627 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Gradient Checking
Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking.
You are part of a team working to make mobile paym... | Python Code:
# Packages
import numpy as np
from testCases import *
from gc_utils import sigmoid, relu, dictionary_to_vector, vector_to_dictionary, gradients_to_vector
Explanation: Gradient Checking
Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking.
... |
11,628 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Analyzing Thanksgiving Dinner
This notebook analyzes Thanksgiving dinner in the US. The dataset contains 1058 responses to an online survey about what Americans eat for Thanksgiving dinner, ... | Python Code:
import pandas as pd
data = pd.read_csv("thanksgiving.csv", encoding = 'Latin-1')
data.head()
data.columns
data['Do you celebrate Thanksgiving?'].value_counts()
Explanation: Analyzing Thanksgiving Dinner
This notebook analyzes Thanksgiving dinner in the US. The dataset contains 1058 responses to an online s... |
11,629 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook will perform analysis of functional connectivity on simulated data.
Step1: Now let's add on an activation signal to both voxels
Step2: How can we address this problem? A gene... | Python Code:
import os,sys
import numpy
%matplotlib inline
import matplotlib.pyplot as plt
sys.path.insert(0,'../utils')
from mkdesign import create_design_singlecondition
from nipy.modalities.fmri.hemodynamic_models import spm_hrf,compute_regressor
from make_data import make_continuous_data
data=make_continuous_data(N... |
11,630 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python Crash Course Exercises
This is an optional exercise to test your understanding of Python Basics. If you find this extremely challenging, then you probably are not ready for the rest o... | Python Code:
7**4
Explanation: Python Crash Course Exercises
This is an optional exercise to test your understanding of Python Basics. If you find this extremely challenging, then you probably are not ready for the rest of this course yet and don't have enough programming experience to continue. I would suggest you tak... |
11,631 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sending Secret Messages with Python
This notebook will teach you how to send secret messages to your friends using a computer language called "Python." Python is used by thousands of program... | Python Code:
print ("Hello my name is Levi.")
Explanation: Sending Secret Messages with Python
This notebook will teach you how to send secret messages to your friends using a computer language called "Python." Python is used by thousands of programmers around the world to create websites and video games, to do science... |
11,632 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
EuroPython program grid
Step1: Load the data
Step2: Clean up the data
Here I pick from talk_sessions only the talks with the type that I need for scheduling.
I also remove from all these t... | Python Code:
%%javascript
IPython.OutputArea.auto_scroll_threshold = 99999;
//increase max size of output area
import json
import datetime as dt
from random import choice, randrange, shuffle
from copy import deepcopy
from collections import OrderedDict, defaultdict
from itertools import product
from functools import pa... |
11,633 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Gate Zoo
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step1: Cirq comes with many gates that are standard across quantum computing. This noteb... | Python Code:
try:
import cirq
except ImportError:
print("installing cirq...")
!pip install --quiet --pre cirq
print("installed cirq.")
import IPython.display as ipd
import cirq
import inspect
def display_gates(*gates):
for gate_name in gates:
ipd.display(ipd.Markdown("---"))
gat... |
11,634 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
quant-econ Solutions
Step1: Setup
To recall, we consider the following problem
Step2: Here we want to solve a finite state version of the continuous state model above.
We discretize the st... | Python Code:
%matplotlib inline
from __future__ import division, print_function
import numpy as np
import scipy.sparse as sparse
import matplotlib.pyplot as plt
from quantecon import compute_fixed_point
from quantecon.markov import DiscreteDP
Explanation: quant-econ Solutions: Discrete Dynamic Programming
Solutions for... |
11,635 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lab 4
Step1: Part 1a. Simple Line Fitting
Step2: Try a range of slopes and intercepts, and calculate $\chi^2$ values for each set.
Step3: What is chi2? What happens if you print it? Wh... | Python Code:
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
Explanation: Lab 4: Curve Fitting
Jacob Skinner
End of explanation
x=np.array([1.1,2.2,3.1,4.0,5.0,5.8,6.9])
y=np.array([2.0,3.0,4.0,5.0,6.0,7.0,8.0])
dely=np.array([0.1,0.1,0.1,0.1,0.1,0.1,0.1])
plt.plot(x,y,'ro')
plt.show()
Expla... |
11,636 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using isomorphism doesn't help
Step1: Since the Property map doesn't map exactly to the node id in the main graph, I have to use the induced subgraphs. | Python Code:
re = isomorphism(re.get_graph(), m3_5.gt_motif, isomap=True)
re[1][2]
re = m3_5_r[2][0][0]
graph_draw(re.get_graph(), output_size=(100,100))
re.get_graph().get_edges()
re[0]
re[1]
re[2]
g.get_out_edges(216)
re.get_graph().get_edges()
re[0], re[1], re[2]
for i in _:
print(g.get_out_edges(i))
Explanation... |
11,637 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Goal
Retrieve the discretization attributes available in PredicSis.ai GUI using the Python SDK
Prerequisites
PredicSis.ai Python SDK (pip install predicsis; documentation)
A predictive model... | Python Code:
# Load PredicSis.ai SDK
from predicsis import PredicSis
import predicsis.config as config, os, sys
os.environ['PREDICSIS_URL'] = 'your_instance'
if sys.version_info[0] >= 3:
from importlib import reload
reload(config)
Explanation: Goal
Retrieve the discretization attributes available in PredicSis.ai GU... |
11,638 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Classification with a Multi-layer Perceptron (MLP)
Author
Step1: A few notes on Pytorch syntax
(Many thanks to Vanessa Bohm!!)
Pytorch datatype summary
Step2: Problem 2b Make a histogram s... | Python Code:
# this module contains our dataset
!pip install astronn
#this is pytorch, which we will use to build our nn
import torch
#Standards for plotting, math
import matplotlib.pyplot as plt
import numpy as np
#for our objective function
from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrix... |
11,639 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
主题模型
王成军
wangchengjun@nju.edu.cn
计算传播网 http
Step1: Download data
http
Step2: Build the topic model
Step3: We can see the list of topics a document refers to
by using the model[doc] syntax... | Python Code:
%matplotlib inline
from __future__ import print_function
from wordcloud import WordCloud
from gensim import corpora, models, similarities, matutils
import matplotlib.pyplot as plt
import numpy as np
Explanation: 主题模型
王成军
wangchengjun@nju.edu.cn
计算传播网 http://computational-communication.com
2014年高考前夕，百度“基于海... |
11,640 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Límite de Shockley–Queisser
Bandas de conduccion y Bandgap
Primero librerias
Step1: Graficas chidas!
Step2: 1 A graficar el Hermoso Espectro Solar
Primero constantes numericas
Utilizaremo... | Python Code:
import numpy as np # modulo de computo numerico
import matplotlib.pyplot as plt # modulo de graficas
import pandas as pd # modulo de datos
import seaborn as sns
import scipy as sp
import scipy.interpolate, scipy.integrate # para interpolar e integrar
import wget, tarfile # para bajar datos y descompirmir
... |
11,641 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Clase 7
Step1: 2. Modelo normal para los rendimientos
Step2: 3. Simulación usando el histograma de los rendimientos | Python Code:
#importar los paquetes que se van a usar
import pandas as pd
import pandas_datareader.data as web
import numpy as np
from sklearn.neighbors import KernelDensity
import datetime
from datetime import datetime, timedelta
import scipy.stats as stats
import scipy as sp
import scipy.optimize as optimize
import s... |
11,642 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Custom Generator objects
This example should guide you to build your own simple generator.
Step1: Basic knowledge
We assume that you have completed at least some of the previous examples an... | Python Code:
from adaptivemd import (
Project, Task, File, PythonTask
)
project = Project('tutorial')
engine = project.generators['openmm']
modeller = project.generators['pyemma']
pdb_file = project.files['initial_pdb']
Explanation: Custom Generator objects
This example should guide you to build your own simple gen... |
11,643 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example Assignment
<a href="#Problem-1">Problem 1</a>
<a href="#Problem-2">Problem 2</a>
<a href="#Part-A">Part A</a>
<a href="#Part-B">Part B</a>
<a href="#Part-C">Part C</a>
Before you tur... | Python Code:
NAME = ""
COLLABORATORS = ""
Explanation: Example Assignment
<a href="#Problem-1">Problem 1</a>
<a href="#Problem-2">Problem 2</a>
<a href="#Part-A">Part A</a>
<a href="#Part-B">Part B</a>
<a href="#Part-C">Part C</a>
Before you turn this problem in, make sure everything runs as expected. First, restart th... |
11,644 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Create Text Data
Step2: Tokenize Words
Step3: Tokenize Sentences | Python Code:
# Load library
from nltk.tokenize import word_tokenize, sent_tokenize
Explanation: Title: Tokenize Text
Slug: tokenize_text
Summary: How to tokenize text from unstructured text data for machine learning in Python.
Date: 2016-09-08 12:00
Category: Machine Learning
Tags: Preprocessing Text
Authors: Chris Al... |
11,645 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Kalman filter for altitude estimation from accelerometer and sonar
I) TRAJECTORY
We assume sinusoidal trajectory
Step1: II) MEASUREMENTS
Sonar
Step2: Baro
Step3: GPS
Step4: GPS velocity... | Python Code:
m = 10000 # timesteps
dt = 1/ 250.0 # update loop at 250Hz
t = np.arange(m) * dt
freq = 0.1 # Hz
amplitude = 0.5 # meter
alt_true = 405 + amplitude * np.cos(2 * np.pi * freq * t)
height_true = 5 + amplitude * np.cos(2 * np.pi * freq * t)
vel_true = - amplitude * (2 * np.pi * freq) * np.sin(2 *... |
11,646 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Reading CTD data with PySeabird
Author
Step1: Let's first download an example file with some CTD data
Step2: The profile dPIRX003.cnv.OK was loaded with the default rule cnv.yaml
Step3: W... | Python Code:
%matplotlib inline
from seabird.cnv import fCNV
from gsw import z_from_p
Explanation: Reading CTD data with PySeabird
Author: Guilherme Castelão
pySeabird is a package to parse/load CTD data files. It should be an easy task but the problem is that the format have been changing along the time. Work with mul... |
11,647 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
In general your solutions are more elegant.
Great use of available libraries
Like your solutions for day3 (spiral memory), day11 (hex grid)
Day 1
Smart, clean and elegant.
Step1: Day 3
Well... | Python Code:
digits = '91212129'
L = len(digits)
sum([int(digits[i]) for i in range(L) if digits[i] == digits[(i+1) % L]])
def solve(captcha):
captcha = list(map(int, captcha))
prev_val = captcha[-1]
repeated = 0
for v in captcha:
if v == prev_val:
repeated += v
prev_val = v
... |
11,648 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Titanic kaggle competition with SVM - Advanced
Step1: Let's load the processed data and feature scale Age and Fare
Step2: Select the features from data, and convert to numpy arrays
Step3: ... | Python Code:
#import all the needed package
import numpy as np
import scipy as sp
import re
import pandas as pd
import sklearn
from sklearn.cross_validation import train_test_split,cross_val_score
from sklearn import metrics
import matplotlib
from matplotlib import pyplot as plt
%matplotlib inline
from sklearn.svm imp... |
11,649 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Training Logistic Regression via Stochastic Gradient Ascent
The goal of this notebook is to implement a logistic regression classifier using stochastic gradient ascent. You will
Step1: Load... | Python Code:
from __future__ import division
import graphlab
Explanation: Training Logistic Regression via Stochastic Gradient Ascent
The goal of this notebook is to implement a logistic regression classifier using stochastic gradient ascent. You will:
Extract features from Amazon product reviews.
Convert an SFrame int... |
11,650 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exercises
Step1: Exercise 1
Step2: b. Graphing
Using the techniques laid out in lecture, plot a histogram of the returns
Step3: c. Cumulative distribution
Plot the cumulative distribution... | Python Code:
# Useful Functions
import numpy as np
import matplotlib.pyplot as plt
Explanation: Exercises: Plotting
By Christopher van Hoecke, Max Margenot, and Delaney Mackenzie
Lecture Link:
https://www.quantopian.com/lectures/plotting-data
IMPORTANT NOTE:
This lecture corresponds to the Plotting Data lecture, which ... |
11,651 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Classification
We have seen how you can evaluate a supervised learner with a loss function. Classification is the learning task where one tried to predict a binary response variable, this c... | Python Code:
import pandas as pd
import numpy as np
import matplotlib as mpl
import plotnine as p9
import matplotlib.pyplot as plt
import itertools
import warnings
warnings.simplefilter("ignore")
from matplotlib.pyplot import rcParams
rcParams['figure.figsize'] = 6,6
Explanation: Classification
We have seen how you can... |
11,652 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Import Python Packages
To install the kernel used by NERSC-metatlas users, copy the following text to $HOME/.ipython/kernels/mass_spec_cori/kernel.json
{
"argv"
Step1: 2. Set atlas, pro... | Python Code:
from IPython.core.display import Markdown, display, clear_output, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
%matplotlib notebook
%matplotlib inline
%env HDF5_USE_FILE_LOCKING=FALSE
import sys, os
#### add a path to your private code if not using production code ####
#print ... |
11,653 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Generation of tables and figures of MRIQC paper
This notebook is associated to the paper
Step1: Read some data (from mriqc package)
Step2: Figure 1
Step3: Figure 2
Step4: Figure 3
Step5:... | Python Code:
%matplotlib inline
%load_ext autoreload
%autoreload 2
import os.path as op
import numpy as np
import pandas as pd
from pkg_resources import resource_filename as pkgrf
from mriqc.viz import misc as mviz
from mriqc.classifier.data import read_dataset, combine_datasets
# Where the outputs should be saved
outp... |
11,654 | 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
import math
%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... |
11,655 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Characteristic times in real networks
Step1: Model Chassagnole2002
Create a new network object and load the informations from the model Chassagnole2002.
In the original model, the concentra... | Python Code:
from imp import reload
import re
import numpy as np
from scipy.integrate import ode
import NetworkComponents
Explanation: Characteristic times in real networks
End of explanation
chassagnole = NetworkComponents.Network("chassagnole2002")
chassagnole.readSBML("./published_models/Chassagnole2002.xml")
chassa... |
11,656 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Problem Set 8 Review & Transfer Learning with word2vec
Import various modules that we need for this notebook (now using Keras 1.0.0)
Step1: I. Problem Set 8, Part 1
Let's work through a sol... | Python Code:
%pylab inline
import copy
import numpy as np
import pandas as pd
import sys
import os
import re
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD, RMSprop
from keras.layers.normalization import BatchNormalization
from keras.layers.... |
11,657 | Given the following text description, write Python code to implement the functionality described below step by step
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 scripts from 27 seasons. The Neural Ne... | 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 Simpsons TV script... |
11,658 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook describes setting up a model inspired by the Maxout Network (Goodfellow et al.) which they ran out the CIFAR-10 dataset.
The yaml file was modified as little as possible, subst... | Python Code:
!obj:pylearn2.train.Train {
dataset: &train !obj:neukrill_net.dense_dataset.DensePNGDataset {
settings_path: %(settings_path)s,
run_settings: %(run_settings_path)s,
training_set_mode: "train"
},
model: !obj:pylearn2.models.mlp.MLP {
batch_size: &batch... |
11,659 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
DV360 Report Emailed To BigQuery
Pulls a DV360 Report from a gMail email into BigQuery.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you... | Python Code:
!pip install git+https://github.com/google/starthinker
Explanation: DV360 Report Emailed To BigQuery
Pulls a DV360 Report from a gMail email into BigQuery.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with ... |
11,660 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
NATURAL LANGUAGE PROCESSING
This notebook covers chapters 22 and 23 from the book Artificial Intelligence
Step1: CONTENTS
Overview
Languages
HITS
Question Answering
CYK Parse
Chart Parsing
... | Python Code:
import nlp
from nlp import Page, HITS
from nlp import Lexicon, Rules, Grammar, ProbLexicon, ProbRules, ProbGrammar
from nlp import CYK_parse, Chart
from notebook import psource
Explanation: NATURAL LANGUAGE PROCESSING
This notebook covers chapters 22 and 23 from the book Artificial Intelligence: A Modern A... |
11,661 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Pandas</h1>
Step1: <h2>Imports</h2>
Step2: <h2>The structure of a dataframe</h2>
Step3: <h3>Accessing columns and rows</h3>
Step4: <h3>Getting column data</h3>
Step5: <h3>Getting ro... | Python Code:
#installing pandas libraries
!pip install pandas-datareader
!pip install --upgrade html5lib==1.0b8
#There is a bug in the latest version of html5lib so install an earlier version
#Restart kernel after installing html5lib
Explanation: <h1>Pandas</h1>
End of explanation
import pandas as pd #pandas library
fr... |
11,662 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
NNabla Python API Demonstration Tutorial
Let us import nnabla first, and some additional useful tools.
Step1: NdArray
NdArray is a data container of a multi-dimensional array. NdArray is de... | Python Code:
!pip install nnabla-ext-cuda100
!git clone https://github.com/sony/nnabla.git
%cd nnabla/tutorial
import nnabla as nn # Abbreviate as nn for convenience.
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
Explanation: NNabla Python API Demonstration Tutorial
Let us import nnabla first, ... |
11,663 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Note For this to work, you will need the lsst.sims stack to be installed.
- opsimsummary uses healpy which is installed with the sims stack, but also available from pip/conda
- snsims use... | Python Code:
import opsimsummary as oss
from opsimsummary import Tiling, HealpixTiles
# import snsims
import healpy as hp
%matplotlib inline
import matplotlib.pyplot as plt
Explanation: Note For this to work, you will need the lsst.sims stack to be installed.
- opsimsummary uses healpy which is installed with the sim... |
11,664 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
More fun with pandas
Let's use pandas to dive into some more complicated data.
The data
We're going to be working with FDA import refusal data from 2014 to September 2017. From the source
St... | Python Code:
# to avoid errors with the FDA files, we're going to specify the encoding
# as latin_1, which is common with gov't data
# so it's a decent educated guess to start with
# main dataframe
# country code lookup dataframe
# refusal code lookup dataframe
# specify that the 'ASC_ID' column comes in as a string
# ... |
11,665 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
In this exercise, you'll apply target encoding to features in the Ames dataset.
Run this cell to set everything up!
Step1: First you'll need to choose which features you want t... | Python Code:
# Setup feedback system
from learntools.core import binder
binder.bind(globals())
from learntools.feature_engineering_new.ex6 import *
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
from category_encoders import MEstimateEncoder
from sklearn.mod... |
11,666 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lab Session
Step1: 1. Introduction
In this notebook we explore an application of clustering algorithms to shape segmentation from binary images. We will carry out some exploratory work with... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.misc import imread
Explanation: Lab Session: Clustering algorithms for Image Segmentation
Author: Jesús Cid Sueiro
Jan. 2017
End of explanation
name = "birds.jpg"
name = "Seeds.jpg"
birds = imread("Images/" + name)
birdsG = np... |
11,667 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
로지스틱 회귀 분석
로지스틱 회귀(Logistic Regression) 분석은 회귀 분석이라는 명칭을 가지고 있지만 분류(classsification) 방법의 일종이다.
로지스틱 회귀 모형에서는 베르누이 확률 변수(Bernoilli random variable)의 모수(parameter) $\theta$가 독립 변수 $x$에 의존한다고 가... | Python Code:
xx = np.linspace(-10, 10, 1000)
plt.plot(xx, (1/(1+np.exp(-xx)))*2-1, label="logistic (scaled)")
plt.plot(xx, sp.special.erf(0.5*np.sqrt(np.pi)*xx), label="erf (scaled)")
plt.plot(xx, np.tanh(xx), label="tanh")
plt.ylim([-1.1, 1.1])
plt.legend(loc=2)
plt.show()
Explanation: 로지스틱 회귀 분석
로지스틱 회귀(Logistic Regr... |
11,668 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
Bipartite graphs are graphs that have two (bi-) partitions (-partite) of nodes. Nodes within each partition are not allowed to be connected to one another; rather, they can only... | Python Code:
G = cf.load_crime_network()
G.edges(data=True)[0:5]
G.nodes(data=True)[0:10]
Explanation: Introduction
Bipartite graphs are graphs that have two (bi-) partitions (-partite) of nodes. Nodes within each partition are not allowed to be connected to one another; rather, they can only be connected to nodes in t... |
11,669 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bubble Sort
Bubble sort, sometimes referred to as sinking sort, is a simple sorting algorithm that repeatedly steps through the list to be sorted, compares each pair of adjacent items and sw... | Python Code:
def bubble_sort(unsorted_list):
x = ipytracer.List1DTracer(unsorted_list)
display(x)
length = len(x)-1
for i in range(length):
for j in range(length-i):
if x[j] > x[j+1]:
x[j], x[j+1] = x[j+1], x[j]
return x.data
Explanation: Bubble Sort
Bubble sort, ... |
11,670 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Linear Algebra in NumPy
Unit 9, Lecture 2
Numerical Methods and Statistics
Prof. Andrew White, March 30, 2020
Step1: Working with Matrices in Numpy
We saw earlier in the class how to create... | Python Code:
import random
import numpy as np
import matplotlib.pyplot as plt
from math import sqrt, pi, erf
import scipy.stats
import numpy.linalg
Explanation: Linear Algebra in NumPy
Unit 9, Lecture 2
Numerical Methods and Statistics
Prof. Andrew White, March 30, 2020
End of explanation
matrix = [ [4,3], [6, 2] ]
pri... |
11,671 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
New to Plotly?
Plotly's Python library is free and open source! Get started by downloading the client and reading the primer.
<br>You can set up Plotly to work in online or offline mode, or ... | Python Code:
import plotly
plotly.__version__
Explanation: New to Plotly?
Plotly's Python library is free and open source! Get started by downloading the client and reading the primer.
<br>You can set up Plotly to work in online or offline mode, or in jupyter notebooks.
<br>We also have a quick-reference cheatsheet (ne... |
11,672 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. This is a Jupyter notebook!
<p>A <em>Jupyter notebook</em> is a document that contains text cells (what you're reading right now) and code cells. What is special with a notebook is that i... | Python Code:
# I'm a code cell, click me, then run me!
256 * 60 * 24 # Children × minutes × hours
Explanation: 1. This is a Jupyter notebook!
<p>A <em>Jupyter notebook</em> is a document that contains text cells (what you're reading right now) and code cells. What is special with a notebook is that it's <em>interactive... |
11,673 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Dealing with spectrum data
This tutorial demonstrates how to use Spectrum class to do various arithmetic operations of Spectrum. This demo uses the Jsc calculation as an example, namely
\beg... | Python Code:
%matplotlib inline
import numpy as np
import scipy.constants as sc
import matplotlib.pyplot as plt
from pypvcell.spectrum import Spectrum
from pypvcell.illumination import Illumination
from pypvcell.photocurrent import gen_step_qe_array
Explanation: Dealing with spectrum data
This tutorial demonstrates how... |
11,674 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Learning
Assignment 1
The objective of this assignment is to learn about simple data curation practices, and familiarize you with some of the data we'll be reusing later.
This notebook ... | Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy import ndimage
from... |
11,675 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introducing the Keras Sequential API
Learning Objectives
1. Build a DNN model using the Keras Sequential API
1. Learn how to use feature columns in a Keras model
1. Learn how to train ... | Python Code:
import datetime
import os
import shutil
import numpy as np
import pandas as pd
import tensorflow as tf
from google.cloud import aiplatform
from matplotlib import pyplot as plt
from tensorflow import keras
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.layers import Dense, DenseFea... |
11,676 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Working with relational data using Pandas
Testing the waters with sample relational data
Based on well defined theory and availability of highly mature, scalable and accessible relational da... | Python Code:
import pandas as pd
# Some basic data
users = [
{ 'name': 'John', 'age': 29, 'id': 1 },
{ 'name': 'Doe', 'age': 19, 'id': 2 },
{ 'name': 'Alex', 'age': 32, 'id': 3 },
{ 'name': 'Rahul', 'age': 27, 'id': 4 },
{ 'name': 'Ellen', 'age': 23, 'id': 5},
{ 'name': 'Shristy', 'age': 30, 'id... |
11,677 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1> 2. Creating a sampled dataset </h1>
In this notebook, you will implement
Step1: <h2> Create ML dataset by sampling using BigQuery </h2>
<p>
Sample the BigQuery table publicdata.samples... | Python Code:
# TODO: change these to reflect your environment
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
%%bash
if ! gsutil ls | grep -q gs://${BUCKET}/; then
gsutil mb... |
11,678 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Empircally observed SaaS churn
A subscribtion-as-a-service company has a typical customer churn pattern. During periods of no billing, the churn is relatively low compared to periods of bill... | Python Code:
kmf = KaplanMeierFitter().fit(df['T'], df['E'])
kmf.plot(figsize=(11,6));
Explanation: Empircally observed SaaS churn
A subscribtion-as-a-service company has a typical customer churn pattern. During periods of no billing, the churn is relatively low compared to periods of billing (typically every 30 or 365... |
11,679 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Machine Learning Test Cases
Case 1.1 - Biomedical Device for Parkinson's Disease Progression Monitoring
The dataset used in this test case is the Oxford Parkinson's Disease Telemonitoring Da... | Python Code:
import numpy as np
import pandas as pd
import os
from pandas import DataFrame
from pandas import read_csv
from numpy import mean
from numpy import std
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
matplotlib.style.use('ggplot')
import seaborn as sns
results = read_csv('parkinsons_upd... |
11,680 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The csv module can be used to work with data exported from spreadsheets and databases into text files formatted with fields and records, commonly referred to as comma-separated value (CSV) f... | Python Code:
import csv
import sys
unicode_chars = 'å∫ç'
with open('data.csv', 'wt') as f:
writer = csv.writer(f)
writer.writerow(('Title 1', 'Title 2', 'Title 3', 'Title 4'))
for i in range(3):
row = (
i + 1,
chr(ord('a') + i),
'08/{:02d}/07'.format(i + 1),
... |
11,681 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Previous
1.18 映射名称到序列元素
问题
你有一段通过下标访问列表或者元组中元素的代码，但是这样有时候会使得你的代码难以阅读， 于是你想通过名称来访问元素。
解决方案
collections.namedtuple() 函数通过使用一个普通的元组对象来帮你解决这个问题。 这个函数实际上是一个返回 Python 中标准元组类型子类的一个工厂方法。 你需要传递一个类型名和... | Python Code:
from collections import namedtuple
Subscriber = namedtuple("Subscriber", ["addr", "joined"])
sub = Subscriber("jonesy@example.com", "2012-10-19")
sub
sub.addr
sub.joined
Explanation: Previous
1.18 映射名称到序列元素
问题
你有一段通过下标访问列表或者元组中元素的代码，但是这样有时候会使得你的代码难以阅读， 于是你想通过名称来访问元素。
解决方案
collections.namedtuple() 函数通过使用一个普... |
11,682 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compile and deploy the TFX pipeline to Kubeflow Pipelines
This notebook is the second of two notebooks that guide you through automating the Real-time Item-to-item Recommendation with BigQue... | Python Code:
%load_ext autoreload
%autoreload 2
!pip install -q -U kfp
Explanation: Compile and deploy the TFX pipeline to Kubeflow Pipelines
This notebook is the second of two notebooks that guide you through automating the Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN solution ... |
11,683 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This quickstart guide explains how to join two tables A and B using edit distance measure. First, you need to import the required packages as follows (if you have installed py_stringsimjoin ... | Python Code:
# Import libraries
import py_stringsimjoin as ssj
import py_stringmatching as sm
import pandas as pd
import os, sys
print('python version: ' + sys.version)
print('py_stringsimjoin version: ' + ssj.__version__)
print('py_stringmatching version: ' + sm.__version__)
print('pandas version: ' + pd.__version__)
... |
11,684 | Given the following text description, write Python code to implement the functionality described.
Description:
Count of numbers whose difference with Fibonacci count upto them is atleast K
Python 3 program to find the count of numbers whose difference with Fibonacci count upto them is atleast K ; fibUpto [ i ] denotes ... | Python Code:
MAX = 1000005
fibUpto =[0 ] *(MAX + 1 )
def compute(sz ) :
isFib =[False ] *(sz + 1 )
prev = 0
curr = 1
isFib[prev ] = True
isFib[curr ] = True
while(curr <= sz ) :
temp = curr + prev
if(temp <= sz ) :
isFib[temp ] = True
prev = curr
curr = temp
fibUpto[0 ] = 1
for i in... |
11,685 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Differential Equations
An ordinary differential equation or ODE is a mathematical equation containing a function or functions of one independent variable and its derivatives. The term ordina... | Python Code:
def rungekutta(fn, y0, ti=0, tf=10, h=0.01):
h = np.float(h)
x = np.arange(ti, tf, h)
Y = np.zeros((len(x), len(y0)))
Y[0] = y0
for i in range(0, len(x)-1):
yi = Y[i]
xi = x[i]
k1 = h * fn(xi, yi)
k2 = h * fn(xi + 0.5 * h, yi +... |
11,686 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Import the necessary packages to read in the data, plot, and create a linear regression model
Step1: 2. Read in the hanford.csv file
Step2: 3. Calculate the basic descriptive statistics... | Python Code:
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
%matplotlib inline
import statsmodels.formula.api as smf
Explanation: 1. Import the necessary packages to read in the data, plot, and create a linear regression model
End of explanation
df = pd.read_csv('hanford.csv')
Expl... |
11,687 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fitting Models
Learning Objectives
Step1: Introduction
In Data Science it is common to start with data and develop a model of that data. Such models can help to explain the data and make pr... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy import optimize as opt
from IPython.html.widgets import interact
Explanation: Fitting Models
Learning Objectives: learn to fit models to data using linear and non-linear regression.
This material is licensed under the MIT lice... |
11,688 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CI/CD for a Kubeflow pipeline on Vertex AI
Learning Objectives
Step1: Let us make sure that the artifact store exists
Step2: Creating the KFP CLI builder for Vertex AI
Review the Dockerfil... | Python Code:
PROJECT_ID = !(gcloud config get-value project)
PROJECT_ID = PROJECT_ID[0]
REGION = "us-central1"
ARTIFACT_STORE = f"gs://{PROJECT_ID}-kfp-artifact-store"
Explanation: CI/CD for a Kubeflow pipeline on Vertex AI
Learning Objectives:
1. Learn how to create a custom Cloud Build builder to pilote Vertex AI Pip... |
11,689 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fitting the H.E.S.S. Crab spectrum with iminuit and emcee
As an example of a chi^2 fit, we use the flux points from the Crab nebula
as measured by H.E.S.S. in 2006
Step1: The data
We start ... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
Explanation: Fitting the H.E.S.S. Crab spectrum with iminuit and emcee
As an example of a chi^2 fit, we use the flux points from the Crab nebula
as measured by H.E.S.S. in 2006: http://adsabs.... |
11,690 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Tabular-Output" data-toc-modified-id="Tabular-Output-1"><span class="... | Python Code:
from myhdl import *
from myhdlpeek import Peeker # Import the myhdlpeeker module.
def mux(z, a, b, sel):
A simple multiplexer.
@always_comb
def mux_logic():
if sel == 1:
z.next = a # Signal a sent to mux output when sel is high.
else:
z.next = b # Sign... |
11,691 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
LSTM text generation from Nietzsche's writings
The original script is here. It has the following message regarding speed
Step1: Get the data
Step2: Build the neural network
Step3: Train t... | Python Code:
# Imports
from __future__ import print_function
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.layers import LSTM
from keras.utils.data_utils import get_file
import numpy as np
import random
import sys
Explanation: LSTM text generation from Nietzsche's wr... |
11,692 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Formulas
Step1: Import convention
You can import explicitly from statsmodels.formula.api
Step2: Alternatively, you can just use the formula namespace of the main statsmodels.api.
Step3: O... | Python Code:
import numpy as np # noqa:F401 needed in namespace for patsy
import statsmodels.api as sm
Explanation: Formulas: Fitting models using R-style formulas
Since version 0.5.0, statsmodels allows users to fit statistical models using R-style formulas. Internally, statsmodels uses the patsy package to convert ... |
11,693 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Read Loans csv and Create test/train csv files
Step1: Read and process train and test dataframes
Step2: Model Tuning with skopt
Step3: GBM best results - sorted
Step4: XGB best results -... | Python Code:
%%time
print('Reading: loan_stat542.csv into loans dataframe...')
loans = pd.read_csv('loan_stat542.csv')
print('Loans dataframe:', loans.shape)
test_ids = pd.read_csv('Project3_test_id.csv', dtype={'test1':int,'test2':int, 'test3':int,})
print('ids dataframe:', test_ids.shape)
trains = []
tests = []
label... |
11,694 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook explores the speed of searching for values in sets and lists.
After reading this notebook, watch Brandon Rhodes' videos All Your Ducks In A Row
Step1: Notice that the differen... | Python Code:
def make_list(n):
if True:
return list(range(n))
else:
return list(str(i) for i in range(n))
n = int(25e6)
# n = 5
m = (0, n // 2, n-1, n)
a_list = make_list(n)
a_set = set(a_list)
n, m
# Finding something that is in a set is fast.
# The key one is looking for has little effect on t... |
11,695 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Spark Cluster Overview
Spark applications run as independent sets of processes on a cluster, coordinated by the SparkContext object in your main program (called the driver program).
Step1: ... | Python Code:
import socket
Explanation: Spark Cluster Overview
Spark applications run as independent sets of processes on a cluster, coordinated by the SparkContext object in your main program (called the driver program).
End of explanation
print( "Hello World from " + socket.gethostname() )
Explanation: This code runs... |
11,696 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fruitful Functions
Return Values
Some of the built-in functions we have used, such as the math functions, produce results. Calling the function generates a value, which we usually assign to ... | Python Code:
import math
e = math.exp(1.0)
e
Explanation: Fruitful Functions
Return Values
Some of the built-in functions we have used, such as the math functions, produce results. Calling the function generates a value, which we usually assign to a variable or use as part of an expression.
End of explanation
# Functio... |
11,697 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Process U-Wind
Step1: 2. Read u-wind data and pick variables
2.1 Use print to check variable information
Actually, you can also use numdump infile.nc -h to check the same inforamtion
Step2:... | Python Code:
% matplotlib inline
from pylab import *
import numpy as np
from scipy.interpolate import interp2d
from netCDF4 import Dataset as netcdf # netcdf4-python module
import matplotlib.pyplot as plt
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 12, 6
Explanation: Process U-Wind: Zonal Mean an... |
11,698 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Working with data files
Reading and writing data files is a common task, and Python offers native support for working with many kinds of data files. Today, we're going to be working mainly w... | Python Code:
# open the MLB data file `as` mlb
# create a reader object
# loop over the rows in the file
# assign variables to each element in the row (shortcut!)
# print the row, which is a list
Explanation: Working with data files
Reading and writing data files is a... |
11,699 | 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
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's wor... |
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