Unnamed: 0 int64 0 16k | text_prompt stringlengths 110 62.1k | code_prompt stringlengths 37 152k |
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15,900 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Physiology
1) Using the ion concentrations of interstitial and intracellular compartments and the Nernst equation, calculate the equilibrium potentials for Na+, K+, and Cl-
Step1: 2) Assumi... | Python Code:
from math import log
# RT/F = 26.73 at room temperature
rt_div_f = 26.73
nernst = lambda xO, xI, z: rt_div_f/z * log(1.0 * xO / xI)
Na_Eq = nernst(145, 15, 1)
K_Eq = nernst(4.5, 120, 1)
Cl_Eq = nernst(116, 20, -1)
print "Na+ equilibrium potential is %.2f mV" % (Na_Eq)
print "K+ equilibrium potential is %.2... |
15,901 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Decoding (MVPA)
Step1: Transformation classes
Scaler
The
Step2: PSDEstimator
The
Step3: Source power comodulation (SPoC)
Source Power Comodulation (
Step4: Decoding over time
This stra... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
import mne
from mne.datasets import sample
from mne.decoding import (SlidingEstimator, GeneralizingEstimator, Sc... |
15,902 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Semi-Monocoque Theory
Step1: Import Section class, which contains all calculations
Step2: Initialization of sympy symbolic tool and pint for dimension analysis (not really implemented rn a... | Python Code:
from pint import UnitRegistry
import sympy
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import sys
%matplotlib inline
from IPython.display import display
Explanation: Semi-Monocoque Theory: corrective solutions
End of explanation
from Section import Section
Explanation: Import S... |
15,903 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
From FITS to HDF5
Purpose of this notebook is to get the data to suitable
data structure for preprocessing.
FITS file format
https
Step1: HDUs
A FITS file is comprised of segmets called Hea... | Python Code:
%matplotlib inline
import os
import glob
import random
import h5py
import astropy.io.fits
import numpy as np
import matplotlib.pyplot as plt
# find the normalized spectra in data_path directory
# add all filenames to the list fits_paths
FITS_DIR = 'data/ondrejov/'
fits_paths = glob.glob(FITS_DIR + '*.fits'... |
15,904 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook analyzes the predictions of the trading model. <br/>At different thresholds, how effective is the model at predicting<br/> larger-than-average range days?
Step1: This file con... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
pwd
cd output
ls
Explanation: This notebook analyzes the predictions of the trading model. <br/>At different thresholds, how effective is the model at predicting<br/> larger-than-average range days?
End of explanation
ranking_frame = pd.read_csv('ra... |
15,905 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Section 5.5 superposition in time.
Simulating varying head at x=0 using a sequence of sudden changes of head at $x=0$ and $t=0$
IHE, Delft, 20120-01-06
@T.N.Olsthoorn
Context
The aquifer is ... | Python Code:
import numpy as np
from scipy.special import erfc
from matplotlib import pyplot as plt
from matplotlib import animation, rc
from matplotlib.animation import FuncAnimation
from matplotlib.patches import PathPatch, Path
from IPython.display import HTML
from scipy.special import erfc
import pdb
Explanation: S... |
15,906 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sampling from a Population
The law of averages also holds when the random sample is drawn from individuals in a large population.
As an example, we will study a population of flight delay ti... | Python Code:
united = Table.read_table('http://inferentialthinking.com/notebooks/united_summer2015.csv')
united
Explanation: Sampling from a Population
The law of averages also holds when the random sample is drawn from individuals in a large population.
As an example, we will study a population of flight delay times. ... |
15,907 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regression Week 1
Step1: Useful pandas summary functions
In order to make use of the closed form soltion as well as take advantage of graphlab's built in functions we will review some impor... | Python Code:
import pandas as pd
import numpy as np
dtype_dict = {'bathrooms':float, 'waterfront':int, 'sqft_above':int, 'sqft_living15':float,
'grade':int, 'yr_renovated':int, 'price':float, 'bedrooms':float, 'zipcode':str,
'long':float, 'sqft_lot15':float, 'sqft_living':float, 'floors':s... |
15,908 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
pyplearnr demo
Here I demonstrate pyplearnr, a wrapper for building/training/validating scikit learn pipelines using GridSearchCV or RandomizedSearchCV.
Quick keyword arguments give access t... | Python Code:
import pandas as pd
df = pd.read_pickle('trimmed_titanic_data.pkl')
df.info()
Explanation: pyplearnr demo
Here I demonstrate pyplearnr, a wrapper for building/training/validating scikit learn pipelines using GridSearchCV or RandomizedSearchCV.
Quick keyword arguments give access to optional feature selecti... |
15,909 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regressão Linear
Este notebook mostra uma implementação básica de Regressão Linear e o uso da biblioteca MLlib do PySpark para a tarefa de regressão na base de dados Million Song Dataset do ... | Python Code:
# carregar base de dados
import os.path
fileName = os.path.join('Data', 'millionsong.txt')
numPartitions = 2
rawData = sc.textFile(fileName, numPartitions)
# EXERCICIO
numPoints = rawData.count()
print (numPoints)
samplePoints = rawData.take(5)
print (samplePoints)
# TEST Load and check the data (1a)
asser... |
15,910 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Map
<script type="text/javascript">
localStorage.setItem('language', 'language-py')
</script>
<table align="left" style="margin-right
Step2: Examples
In the following... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License")
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this fi... |
15,911 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Formulas
Step1: import convention
Step2: OLS regression using formulas
Step3: Categorical variables
Looking at the summary printed above, notice that patsy determined that elements of Reg... | Python Code:
import numpy as np
import statsmodels.api as sm
Explanation: Formulas: Fitting models using R-style formulas
loading modules and fucntions
End of explanation
from statsmodels.formula.api import ols
import statsmodels.formula.api as smf
dir(smf)
Explanation: import convention
End of explanation
dta = sm.dat... |
15,912 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Curve fitting in python
A.M.C. Dawes - 2015
An introduction to various curve fitting routines useful for physics work.
The first cell is used to import additional features so they are availa... | Python Code:
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
Explanation: Curve fitting in python
A.M.C. Dawes - 2015
An introduction to various curve fitting routines useful for physics work.
The first cell is used to import additional features so they are available in our notebook. matplotlib pr... |
15,913 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Multithreading, Multiprocessing, and Subprocessing
- Chris Sterling
This is a primer to help you get started, not an all-encompassing guide
I'll be posting this, so no need to frantically wr... | Python Code:
def count_down(n):
while n > 0:
n-=1
COUNT = 500000000
Explanation: Multithreading, Multiprocessing, and Subprocessing
- Chris Sterling
This is a primer to help you get started, not an all-encompassing guide
I'll be posting this, so no need to frantically write code
Here and there, I had to do ... |
15,914 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<CENTER>
<p><font size="5"> Queuing theory
Step1: 2) The discrete valued random variable $X$ follows a Poisson distribution if its probabilities depend on a parameter $\lambda$ and are suc... | Python Code:
%matplotlib inline
from pylab import *
N = 10**5
lambda_ = 2.0
########################################
# Supply the missing coefficient herein below
V1 = -1.0/lambda_
data = V1*log(rand(N))
########################################
m = mean(data)
v = var(data)
print("\u0... |
15,915 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Setup
$ mkvirtualenv aws_name_similarity
$ pip install --upgrade pip
$ pip install jellyfish jupyter scipy matplotlib
$ jupyter notebook
Step1: # Testing it out
Step2: With real AWS servic... | Python Code:
from itertools import combinations
import jellyfish
from scipy.cluster import hierarchy
import numpy as np
import matplotlib.pyplot as plt
Explanation: Setup
$ mkvirtualenv aws_name_similarity
$ pip install --upgrade pip
$ pip install jellyfish jupyter scipy matplotlib
$ jupyter notebook
End of explanation... |
15,916 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Self-Driving Car Engineer Nanodegree
Deep Learning
Project
Step1: Step 1
Step2: Include an exploratory visualization of the dataset
Visualize the German Traffic Signs Dataset using the pic... | Python Code:
# Load pickled data
import pickle
import cv2 # for grayscale and normalize
# TODO: Fill this in based on where you saved the training and testing data
training_file ='traffic-signs-data/train.p'
validation_file='traffic-signs-data/valid.p'
testing_file = 'traffic-signs-data/test.p'
with open(training_file,... |
15,917 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python 浮点数运算
浮点数用来存储计算机中的小数，与现实世界中的十进制小数不同的是，浮点数通过二进制的形式来表示一个小数。在深入了解浮点数的实现之前，先来看几个 Python 浮点数计算有意思的例子：
Step1: IEEE 浮点数表示法
这些看起来违反常识的“错误”并非 Python 的错，而是由浮点数的规则所决定的，即使放到其它语言中结果也是这样的。要理解计算机中浮... | Python Code:
0.1 == 0.10000000000000000000001
0.1+0.1+0.1 == 0.3
Explanation: Python 浮点数运算
浮点数用来存储计算机中的小数，与现实世界中的十进制小数不同的是，浮点数通过二进制的形式来表示一个小数。在深入了解浮点数的实现之前，先来看几个 Python 浮点数计算有意思的例子：
End of explanation
(0.1).as_integer_ratio()
Explanation: IEEE 浮点数表示法
这些看起来违反常识的“错误”并非 Python 的错，而是由浮点数的规则所决定的，即使放到其它语言中结果也是这样的。要理解计算机中浮点数的... |
15,918 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lab 3 - Multi Layer Perceptron with MNIST
This lab corresponds to Module 3 of the "Deep Learning Explained" course. We assume that you have successfully completed Lab 1 (Downloading the MNI... | Python Code:
# Figure 1
Image(url= "http://3.bp.blogspot.com/_UpN7DfJA0j4/TJtUBWPk0SI/AAAAAAAAABY/oWPMtmqJn3k/s1600/mnist_originals.png", width=200, height=200)
Explanation: Lab 3 - Multi Layer Perceptron with MNIST
This lab corresponds to Module 3 of the "Deep Learning Explained" course. We assume that you have succe... |
15,919 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
H2O Tutorial
Author
Step1: Enable inline plotting in the Jupyter Notebook
Step2: Intro to H2O Data Munging
Read csv data into H2O. This loads the data into the H2O column compressed, in-me... | Python Code:
import pandas as pd
import numpy
from numpy.random import choice
from sklearn.datasets import load_boston
import h2o
h2o.init()
# transfer the boston data from pandas to H2O
boston_data = load_boston()
X = pd.DataFrame(data=boston_data.data, columns=boston_data.feature_names)
X["Median_value"] = boston_dat... |
15,920 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I realize my question is fairly similar to Vectorized moving window on 2D array in numpy , but the answers there don't quite satisfy my needs. | Problem:
import numpy as np
a = np.array([[1,2,3,4],
[2,3,4,5],
[3,4,5,6],
[4,5,6,7]])
size = (3, 3)
def window(arr, shape=(3, 3)):
ans = []
# Find row and column window sizes
r_win = np.floor(shape[0] / 2).astype(int)
c_win = np.floor(shape[1] / 2).astype(int)
x, y = arr.shape
... |
15,921 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<center>
<img src="http
Step1: Hints
Step2: Example 2
Considers the following IVP
Step3: Example 3
Considers the following IVP
Step4: Example 4
See classnotes!
Step5: Example 7, for... | Python Code:
import numpy as np
import scipy.sparse.linalg as sp
import sympy as sym
from scipy.linalg import toeplitz
import ipywidgets as widgets
from ipywidgets import IntSlider
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatte... |
15,922 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Machine Learning Engineer Nanodegree
Model Evaluation & Validation
Project 1
Step1: Data Exploration
In this first section of this project, you will make a cursory investigation about the B... | Python Code:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
import visuals as vs # Supplementary code
from sklearn.cross_validation import ShuffleSplit
# Pretty display for notebooks
%matplotlib inline
# Load the Boston housing dataset
data = pd.read_csv('housing.csv')
prices = dat... |
15,923 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TFX pipeline example - Chicago Taxi tips prediction
Overview
Tensorflow Extended (TFX) is a Google-production-scale machine
learning platform based on TensorFlow. It provides a configuration... | Python Code:
!python3 -m pip install pip --upgrade --quiet --user
!python3 -m pip install kfp --upgrade --quiet --user
pip install tfx==1.4.0 tensorflow==2.5.1 --quiet --user
Explanation: TFX pipeline example - Chicago Taxi tips prediction
Overview
Tensorflow Extended (TFX) is a Google-production-scale machine
learning... |
15,924 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<div align="right">Python 3.6 Jupyter Notebook</div>
Visual communication
Geocoding and markdown examples
Your completion of the notebook exercises will be graded based on your ability to do... | Python Code:
# Load relevant libraries.
import pandas as pd
import numpy as np
import matplotlib
import folium
import geocoder
from tqdm import tqdm
%pylab inline
pylab.rcParams['figure.figsize'] = (10, 8)
Explanation: <div align="right">Python 3.6 Jupyter Notebook</div>
Visual communication
Geocoding and markdown exam... |
15,925 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compare fit of mixture model where the nulldistribution is either with or without prethreshold
In this notebook, I did a first effort to see if we can apply the thresholdfree peakdistributio... | Python Code:
import matplotlib
% matplotlib inline
import numpy as np
import scipy
import scipy.stats as stats
import scipy.optimize as optimize
import scipy.integrate as integrate
from __future__ import print_function, division
import os
import math
from nipy.labs.utils.simul_multisubject_fmri_dataset import surrogate... |
15,926 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using ChemicalEnvironments
Chemical Environments were created as a way to parse SMIRKS strings and make changes in chemical perception space.
In this workbook, we will show you have chemica... | Python Code:
# import necessary functions
from openff.toolkit.typing.chemistry import environment as env
from openeye import oechem
Explanation: Using ChemicalEnvironments
Chemical Environments were created as a way to parse SMIRKS strings and make changes in chemical perception space.
In this workbook, we will show y... |
15,927 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Linear Regression
Step5: Partitioning the data
Before we can begin training our model and testing it, it is important to first properly partition the data into three subsets
Step9: ... | Python Code:
# import libraries
import matplotlib
import IPython
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import pylab
import seaborn as sns
import sklearn as sk
%matplotlib inline
housing = Read the .csv file containing the housing data
Explanation: Linear Regress... |
15,928 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Leakage Coefficient Summary
This notebook summarize the leakage coefficient fitted from 4 dsDNA samples.
Import software
Step1: Data files
Step2: Plot style
Step3: Average leakage
Mean pe... | Python Code:
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from cycler import cycler
import seaborn as sns
%matplotlib inline
%config InlineBackend.figure_format='retina' # for hi-dpi displays
figure_size = (5, 4)
default_figure = lambda: plt.subplots(figsize... |
15,929 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Thin Plate Splines (TPS) Transforms
Step1: Let's create the landmarks used in Principal Warps paper (http
Step2: Let's visualize the TPS
Step3: This proves that the result is correct
Step... | Python Code:
import numpy as np
from menpo.transform import ThinPlateSplines
from menpo.shape import PointCloud
Explanation: Thin Plate Splines (TPS) Transforms
End of explanation
# landmarks used in Principal Warps paper
# http://user.engineering.uiowa.edu/~aip/papers/bookstein-89.pdf
src_landmarks = np.array([[3.6929... |
15,930 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 5 Statistics
Describing a Single Set of Data
Step4: Central Tendencies
Step7: Dispersion
Step8: Correlation | Python Code:
num_friends = [100,49,41,40,25,21,21,19,19,18,18,16,15,15,15,15,14,14,13,13,13,13,12,12,11,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,8,8,8,8,8,8,8,8,8,8,8,8,8,7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,4... |
15,931 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Download, Parse and Interrogate Apple Health Export Data
The first part of this program is all about getting the Apple Health export and putting it into an analyzable format. At that point ... | Python Code:
import xml.etree.ElementTree as et
import pandas as pd
import numpy as np
from datetime import *
import matplotlib.pyplot as plt
import re
import os.path
import zipfile
import pytz
%matplotlib inline
plt.rcParams['figure.figsize'] = 16, 8
Explanation: Download, Parse and Interrogate Apple Health Export D... |
15,932 | Given the following text description, write Python code to implement the functionality described.
Description:
Count of arrays in which all adjacent elements are such that one of them divide the another
Python3 program to count the number of arrays of size n such that every element is in range [ 1 , m ] and adjacent ar... | Python Code:
MAX = 1000
def numofArray(n , m ) :
dp =[[ 0 for i in range(MAX ) ] for j in range(MAX ) ]
di =[[ ] for i in range(MAX ) ]
mu =[[ ] for i in range(MAX ) ]
for i in range(1 , m + 1 ) :
for j in range(2 * i , m + 1 , i ) :
di[j ] . append(i )
mu[i ] . append(j )
di[i ] . append(i )
... |
15,933 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Comparison of the accuracy of a cutting plane active learning procedure using the (i) analytic center; (ii) Chebyshev center; and (iii) random center on the Iris flower data set
The set up
S... | Python Code:
import numpy as np
import active
import experiment
import logistic_regression as logr
from sklearn import datasets # The Iris dataset is imported from here.
from IPython.display import display
import matplotlib.pyplot as plt
%matplotlib inline
%load_ext autoreload
%autoreload 1
%aimport active
%aimport exp... |
15,934 | 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... |
15,935 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Setup
Setup that is specific only to Jupyter notebooks
Step1: Setup to use Python libraries/modules
cf. www.coolprop.org/coolprop/examples.html
Step2: Examples from CoolProp's Examples
cf.... | Python Code:
from pathlib import Path
import sys
notebook_directory_parent = Path.cwd().resolve().parent
if str(notebook_directory_parent) not in sys.path:
sys.path.append(str(notebook_directory_parent))
Explanation: Setup
Setup that is specific only to Jupyter notebooks
End of explanation
# Import the things you n... |
15,936 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
add paths of ac-calibration tools and plotting functions
Step1: create PSDMeasurement object - holding the power spectra of one calibration
Step2: create PSDfit object and fit the psds | Python Code:
import sys
sys.path.append('../..')
import pyotc
Explanation: add paths of ac-calibration tools and plotting functions
End of explanation
directory = '../exampleData/height_calibration_single_psds/'
fname = 'B01_1000.dat'
pm = pyotc.PSDMeasurement(warn=False)
pm.load(directory, fname)
Explanation: create P... |
15,937 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Before we get started, a couple of reminders to keep in mind when using iPython notebooks
Step1: Fixing Data Types
Step2: Note when running the above cells that we are actively changing th... | Python Code:
import unicodecsv
## Longer version of code (replaced with shorter, equivalent version below)
# enrollments = []
# f = open('enrollments.csv', 'rb')
# reader = unicodecsv.DictReader(f)
# for row in reader:
# enrollments.append(row)
# f.close()
with open('enrollments.csv', 'rb') as f:
reader = unico... |
15,938 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Building a LAS file from scratch
This example shows
Step1: Step 1
Create some synthetic data, and make some of the values in the middle null values (numpy.nan specifically). Note that of co... | Python Code:
import lasio
print(lasio.__version__)
import datetime
import numpy
import os
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Building a LAS file from scratch
This example shows:
Creating a pretend/synthetic data curve that we'll call "SYNTH", including some null values
Creating an empty LAS... |
15,939 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
국민대, 파이썬, 데이터
W10 NumPy 101
Table of Contents
NumPy Basic
NumPy Exercises
NumPy Example
Step1: 1. NumPy Basic
1. Data Container
데이터를 담는 그릇, Data Container라고 합니다.
Python 에서는 List/Tuple/Dicti... | Python Code:
from IPython.display import Image
Explanation: 국민대, 파이썬, 데이터
W10 NumPy 101
Table of Contents
NumPy Basic
NumPy Exercises
NumPy Example: Random Walks
Coding Convention
import numpy as np
End of explanation
import numpy as np
Explanation: 1. NumPy Basic
1. Data Container
데이터를 담는 그릇, Data Container라고 합니다.
Pyt... |
15,940 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: モデルの保存と復元
<table class="tfo-notebook-buttons" align="left">
<td> <a target="_blank" href="https
Step2: サンプルデータセットの取得
ここでは、重みの保存と読み込みをデモす... | 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... |
15,941 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Image Classification
In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. You'll preprocess the images... | 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'
class DLProgress(tqdm):
last_block = 0
def h... |
15,942 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Self-Adaptive Evolution Strategy (SAES)
TODO
Step2: (1+1)-$\sigma$-Self-Adaptation-ES
Step3: Some explanations about $\sigma$ and $\tau$
Step4: Other inplementations
PyAI
Import required ... | Python Code:
# Init matplotlib
%matplotlib inline
import matplotlib
matplotlib.rcParams['figure.figsize'] = (8, 8)
# Setup PyAI
import sys
sys.path.insert(0, '/Users/jdecock/git/pub/jdhp/pyai')
# Set the objective function
#from pyai.optimize.functions import sphere as func
from pyai.optimize.functions import sphere2d ... |
15,943 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Intro to Cython
Why Cython
Outline
Step1: Now, let's time this
Step2: Not too bad, but this can add up. Let's see if Cython can do better
Step3: That's a little bit faster, which is nice ... | Python Code:
def f(x):
y = x**4 - 3*x
return y
def integrate_f(a, b, n):
dx = (b - a) / n
dx2 = dx / 2
s = f(a) * dx2
for i in range(1, n):
s += f(a + i * dx) * dx
s += f(b) * dx2
return s
Explanation: Intro to Cython
Why Cython
Outline:
Speed up Python code
Interact with Nu... |
15,944 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Trading Framework
This framework is developed based on Peter Henry https
Step1: Create a new OpenAI Gym environment with the customised Trading environment
.initialise_simulator() must be i... | Python Code:
csv = "data/EURUSD60.csv"
Explanation: Trading Framework
This framework is developed based on Peter Henry https://github.com/Henry-bee/gym_trading/ which in turn on developed of Tito Ingargiola's https://github.com/hackthemarket/gym-trading.
First, define the address for the CSV data
End of explanation
en... |
15,945 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent neural network that performs sentiment analysis. Using an RNN rather than a feedfoward network is more accurate ... | Python Code:
import numpy as np
import tensorflow as tf
with open('../sentiment_network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment_network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
Explanation: Sentiment Analysis with an RNN
In this notebook, you'll implement a recurrent ... |
15,946 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
UTSC Machine Learning WorkShop
Cross-validation for feature selection with Linear Regression
From the video series
Step1: MSE is more popular than MAE because MSE "punishes" larger errors. ... | Python Code:
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.feature_selection import SelectKBest, f_regression
from sklearn.cross_validation import cross_val_score
# read in the advertising dataset
data = pd.read_csv('data/Advertising.csv', index_col=0)
# create a ... |
15,947 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Now that we understand variables, we can start to develop more complex code structures which can build more interesting functionality into our scripts. Up to this point, our scripts have bee... | Python Code:
b = True
if b:
print 'b is True'
Explanation: Now that we understand variables, we can start to develop more complex code structures which can build more interesting functionality into our scripts. Up to this point, our scripts have been pretty basic, and limited to only executing in a top-down order, ... |
15,948 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Implementation of a Devito self adjoint variable density visco- acoustic isotropic modeling operator <br>-- Linearized Ops --
This operator is contributed by Chevron Energy Technology Compan... | Python Code:
import numpy as np
from examples.seismic import RickerSource, Receiver, TimeAxis
from devito import (Grid, Function, TimeFunction, SpaceDimension, Constant,
Eq, Operator, solve, configuration, norm)
from devito.finite_differences import Derivative
from devito.builtins import gaussian_s... |
15,949 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
let the function to read the directory recursively into a dataset
| Python Code::
import tensorflow as tf
from tensorflow.keras.utils import image_dataset_from_directory
PATH = ".../Citrus/Leaves"
ds = image_dataset_from_directory(PATH,
validation_split=0.2, subset="training",
image_size=(256,256), interpolation="bilin... |
15,950 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This example assumes the notebook server has been called with ipython notebook --pylab inline and the trunk version of numba at Github.
Step1: Numba provides two major decorators
Step2: Th... | Python Code:
import numpy as np
from numba import autojit, jit, double
%pylab inline
Explanation: This example assumes the notebook server has been called with ipython notebook --pylab inline and the trunk version of numba at Github.
End of explanation
def sum(arr):
M, N = arr.shape
sum = 0.0
for i in range... |
15,951 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The issues associated with validation and
cross-validation are some of the most important
aspects of the practice of machine learning. Selecting the optimal model
for your data is vital, a... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import Pipeline
from sklearn.svm import SVR
from sklearn import cross_validation
np.random.seed(0)
n_samples = 200
kernels = ['linear', 'poly', 'rbf']
true_fun = lambda X: X ** 3
X = np.sort(5 * (np.random.rand(n_samples) - .5))
y = t... |
15,952 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Segmentation
Segmentation is the division of an image into "meaningful" regions. If you've seen The Terminator, you've seen image segmentation
Step1: We can try to create a nicer visualizat... | Python Code:
import numpy as np
from matplotlib import pyplot as plt
import skdemo
plt.rcParams['image.cmap'] = 'spectral'
from skimage import io, segmentation as seg, color
url = '../images/spice_1.jpg'
image = io.imread(url)
labels = seg.slic(image, n_segments=18, compactness=10)
skdemo.imshow_all(image, labels.astyp... |
15,953 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SF Purchases Example
In this example, interact is used to build a UI for exploring San Francisco department purchases by city agency data.
Step1: You can take a quick look at the first 5 ro... | Python Code:
# Import Pandas and then load the data.
from pandas import read_csv
df = read_csv('SFDeptPurchases.csv')
Explanation: SF Purchases Example
In this example, interact is used to build a UI for exploring San Francisco department purchases by city agency data.
End of explanation
df[:5]
Explanation: You can tak... |
15,954 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Limb Darkening
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to upda... | Python Code:
!pip install -I "phoebe>=2.1,<2.2"
Explanation: Limb Darkening
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
%matplotlib inline
impo... |
15,955 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regression Week 4
Step1: Load in house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
Step2: If we want to do any "feature engi... | Python Code:
import graphlab
Explanation: Regression Week 4: Ridge Regression (gradient descent)
In this notebook, you will implement ridge regression via gradient descent. You will:
* Convert an SFrame into a Numpy array
* Write a Numpy function to compute the derivative of the regression weights with respect to a sin... |
15,956 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Template for test
Step1: Controlling for Random Negatve vs Sans Random in Imbalanced Techniques using S, T, and Y Phosphorylation.
Included is N Phosphorylation however no benchmarks are av... | Python Code:
from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
Explanation: Template for test
End of explanation
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
for i in par:
print("y", i)
y = Predictor()
y.load_data(file="Data/Train... |
15,957 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Elements and the periodic table
This data came from Penn State CS professor Doug Hogan.
Thanks to UCF undergraduates Sam Borges, for finding the data set, and Lissa Galguera, for formatting ... | Python Code:
# Import modules that contain functions we need
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
Explanation: Elements and the periodic table
This data came from Penn State CS professor Doug Hogan.
Thanks to UCF undergraduates Sam Borges, for finding the data set, a... |
15,958 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Phone Digits
Given a phone number create a list of all the possible words that you can make given a dictionary from numbers to letters.
In python there is a itertools.permutations('abc')
th... | Python Code:
letters_map = {'2':'ABC', '3':'DEF', '4':'GHI', '5':'JKL',
'6':'MNO', '7':'PQRS', '8':'TUV', '9':'WXYZ'}
def printWords(number, ):
#number is phone number
def printWordsUtil(numb, curr_digit, output, n):
if curr_digit == n:
print('%s ' % output)
return
... |
15,959 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
$$
\def\CC{\bf C}
\def\QQ{\bf Q}
\def\RR{\bf R}
\def\ZZ{\bf Z}
\def\NN{\bf N}
$$
Fonctions def
Step1: Une fonction rassemble un ensemble d'instructions qui permettent d'atteindre un certain... | Python Code:
from __future__ import division, print_function # Python 3
Explanation: $$
\def\CC{\bf C}
\def\QQ{\bf Q}
\def\RR{\bf R}
\def\ZZ{\bf Z}
\def\NN{\bf N}
$$
Fonctions def
End of explanation
def FONCTION( PARAMETRES ):
INSTRUCTIONS
Explanation: Une fonction rassemble un ensemble d'instructions qui permett... |
15,960 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Filter
<script type="text/javascript">
localStorage.setItem('language', 'language-py')
</script>
<table align="left" style="margin-right
Step2: Examples
In the follow... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License")
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this fi... |
15,961 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Project 1
Used Vehicle Price Prediction
Introduction
1.2 Million listings scraped from TrueCar.com - Price, Mileage, Make, Model dataset from Kaggle
Step1: Exercise P1.1 (50%)
Develop a mac... | Python Code:
%matplotlib inline
import pandas as pd
data = pd.read_csv('https://github.com/albahnsen/PracticalMachineLearningClass/raw/master/datasets/dataTrain_carListings.zip')
data.head()
data.shape
data.Price.describe()
data.plot(kind='scatter', y='Price', x='Year')
data.plot(kind='scatter', y='Price', x='Mileage')... |
15,962 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Variance Reduction in Hull-White Monte Carlo Simulation Using Moment Matching
Goutham Balaraman
In an earlier blog post on how the Hull-White Monte Carlo simulations are notorious for not co... | Python Code:
import QuantLib as ql
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from scipy.integrate import cumtrapz
ql.__version__
Explanation: Variance Reduction in Hull-White Monte Carlo Simulation Using Moment Matching
Goutham Balaraman
In an earlier blog post on how the Hull-White Monte Ca... |
15,963 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
仿照求$ \sum_{i=1}^mi + \sum_{i=1}^ni + \sum_{i=1}^ki$的完整代码，写程序，可求m!+n!+k!
Step1: 写函数可返回1 - 1/3 + 1/5 - 1/7...的前n项的和。在主程序中，分别令n=1000及100000，打印4倍该函数的和。
Step2: 将task3中的练习1及练习4改写为函数，并进行调用。
练习 1... | Python Code:
def compute_sum(n):
i=0
sum=0
while i<n:
i=i+1
sum+=i
return sum
m=int(input('plz input m: '))
n=int(input('plz input n: '))
k=int(input('plz input k: '))
print(compute_sum(m) + compute_sum(n) + compute_sum(k))
Explanation: 仿照求$ \sum_{i=1}^mi + \sum_{i=1}^ni + \sum_{i=1}^ki... |
15,964 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Finding stories in data with Python and Jupyter notebooks
Journocoders London, April 13, 2017
David Blood/@davidcblood/[first] dot [last] at ft.com
Introduction
The Jupyter notebook provid... | Python Code:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from bokeh.plotting import figure, show
from bokeh.io import output_notebook
%matplotlib inline
Explanation: Finding stories in data with Python and Jupyter notebooks
Journocoders London, April 13, 2017
David Blo... |
15,965 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
今回のレポートでは、①オートエンコーダの作成、②再帰型ニューラルネットワークの作成を試みた。
①コブダクラス型生産関数を再現できるオートエンコーダの作成が目標である。
Step1: 定義域は0≤x≤1である。
<P>コブ・ダグラス型生産関数は以下の通りである。</P>
<P>z = x_1**0.5*x_2*0.5</P>
Step2: NNのクラスはすでにNN.pyからi... | Python Code:
%matplotlib inline
import numpy as np
import pylab as pl
import math
from sympy import *
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from mpl_toolkits.mplot3d import Axes3D
from NN import NN
Explanation: 今回のレポートでは、①オートエンコーダの作成、②再帰型ニューラルネットワークの作成を試みた。
①コブダクラス型生産関数を再現できるオートエンコーダ... |
15,966 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
pandas 패키지의 소개
pandas 패키지
Index를 가진 자료형인 R의 data.frame 자료형을 Python에서 구현
참고 자료
http
Step1: Vectorized Operation
Step2: 명시적인 Index를 가지는 Series
생성시 index 인수로 Index 지정
Index 원소는 각 데이터에 대한 key ... | Python Code:
s = pd.Series([4, 7, -5, 3])
s
s.values
type(s.values)
s.index
type(s.index)
Explanation: pandas 패키지의 소개
pandas 패키지
Index를 가진 자료형인 R의 data.frame 자료형을 Python에서 구현
참고 자료
http://pandas.pydata.org/
http://pandas.pydata.org/pandas-docs/stable/10min.html
http://pandas.pydata.org/pandas-docs/stable/tutorials.html... |
15,967 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This document is intended for intermediate to advanced users. It deals with the internals of the MoveStrategy and MoveScheme objects, as well how to create custom versions of them. For most ... | Python Code:
%matplotlib inline
import openpathsampling as paths
from openpathsampling.visualize import PathTreeBuilder, PathTreeBuilder
from IPython.display import SVG, HTML
import openpathsampling.high_level.move_strategy as strategies # TODO: handle this better
# real fast setup of a small network
from openpathsampl... |
15,968 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Before you turn this problem in, make sure everything runs as expected. First, restart the kernel (in the menubar, select Kernel$\rightarrow$Restart) and then run all cells (in the menubar, ... | Python Code:
NAME = ""
COLLABORATORS = ""
Explanation: Before you turn this problem in, make sure everything runs as expected. First, restart the kernel (in the menubar, select Kernel$\rightarrow$Restart) and then run all cells (in the menubar, select Cell$\rightarrow$Run All).
Make sure you fill in any place that says... |
15,969 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Analyzer
Analyzer is a python program that tries to gauge the evolvability and maintainability of java software. To achieve this, it tries to measure the complexity of the software under eva... | Python Code:
# Imports and directives
%matplotlib inline
import numpy as np
from math import log
import matplotlib.pyplot as plt
from matplotlib.mlab import PCA as mlabPCA
import javalang
import os, re, requests, zipfile, json, operator
from collections import Counter
import colorsys
import random
from StringIO import ... |
15,970 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
File (Revision) upload example
To run this example, you'll need the Ovation Python API. Install with pip
Step1: Connection
You use a connection.Session to interact with the Ovaiton REST API... | Python Code:
import ovation.core as core
from ovation.session import connect
from ovation.upload import upload_revision, upload_file, upload_folder
from ovation.download import download_revision
from pprint import pprint
from getpass import getpass
from tqdm import tqdm_notebook as tqdm
Explanation: File (Revision) upl... |
15,971 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Background
Example notebook for the visualiztion of metagenomic data using MinHash signatures calculated with sourmash compute, classified with sourmash gather, and compared with sourmash co... | Python Code:
#Import matplotlib
%matplotlib inline
#Import pandas, seaborn, and ipython display
import pandas as pd
import seaborn as sns
from IPython.display import display, HTML
Explanation: Background
Example notebook for the visualiztion of metagenomic data using MinHash signatures calculated with sourmash comput... |
15,972 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ML Workbench Sample --- Image Classification
<br><br>
Introduction of ML Workbench
ML Workbench provides an easy command line interface for machine learning life cycle, which involves four s... | Python Code:
# ML Workbench magics (%%ml) are under google.datalab.contrib namespace. It is not enabled by default and you need to import it before use.
import google.datalab.contrib.mlworkbench.commands
Explanation: ML Workbench Sample --- Image Classification
<br><br>
Introduction of ML Workbench
ML Workbench provide... |
15,973 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tech - calcul matriciel avec numpy
numpy est la librairie incontournable pour faire des calculs en Python. Ces fonctionnalités sont disponibles dans tous les langages et utilisent les optimi... | Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
Explanation: Tech - calcul matriciel avec numpy
numpy est la librairie incontournable pour faire des calculs en Python. Ces fonctionnalités sont disponibles dans tous les langages et utilisent les optimisations processeurs. ... |
15,974 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Inspect Graph
Edges
Your graph edges are represented by a list of tuples of length 3. The first two elements are the node names linked by the edge. The third is the dictionary of edge attrib... | Python Code:
# Preview first 5 edges
list(g.edges(data=True))[0:5]
Explanation: Inspect Graph
Edges
Your graph edges are represented by a list of tuples of length 3. The first two elements are the node names linked by the edge. The third is the dictionary of edge attributes.
End of explanation
# Preview first 10 nodes
... |
15,975 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Mediation analysis with duration data
This notebook demonstrates mediation analysis when the
mediator and outcome are duration variables, modeled
using proportional hazards regression. Thes... | Python Code:
import pandas as pd
import numpy as np
import statsmodels.api as sm
from statsmodels.stats.mediation import Mediation
Explanation: Mediation analysis with duration data
This notebook demonstrates mediation analysis when the
mediator and outcome are duration variables, modeled
using proportional hazards reg... |
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