Unnamed: 0 int64 0 16k | text_prompt stringlengths 110 62.1k | code_prompt stringlengths 37 152k |
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12,700 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<table>
<tr align=left><td><img align=left src="./images/CC-BY.png">
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approve... | Python Code:
%matplotlib inline
import numpy as np
import scipy.linalg as la
import matplotlib.pyplot as plt
Explanation: <table>
<tr align=left><td><img align=left src="./images/CC-BY.png">
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT li... |
12,701 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Rossmann
Data preparation / Feature engineering
In addition to the provided data, we will be using external datasets put together by participants in the Kaggle competition. You can download ... | Python Code:
PATH=Config().data_path()/Path('rossmann/')
table_names = ['train', 'store', 'store_states', 'state_names', 'googletrend', 'weather', 'test']
tables = [pd.read_csv(PATH/f'{fname}.csv', low_memory=False) for fname in table_names]
train, store, store_states, state_names, googletrend, weather, test = tables
l... |
12,702 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Performing Maximum Likelihood Estimates (MLEs) in IPython
By Delaney Granizo-Mackenzie and Andrei Kirilenko.
This notebook developed in collaboration with Prof. Andrei Kirilenko as part of t... | Python Code:
import math
import matplotlib.pyplot as plt
import numpy as np
import scipy
import scipy.stats
Explanation: Performing Maximum Likelihood Estimates (MLEs) in IPython
By Delaney Granizo-Mackenzie and Andrei Kirilenko.
This notebook developed in collaboration with Prof. Andrei Kirilenko as part of the Master... |
12,703 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href='http
Step1: Concatenation
Concatenation basically glues together DataFrames. Keep in mind that dimensions should match along the axis you are concatenating on. You can use pd.conca... | Python Code:
import pandas as pd
df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']},
index = [0, 1, 2, 3])
df2 = pd.DataFrame({'A': ['A4', 'A5', 'A... |
12,704 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Working with BigQuery tables and the Genomics API
Case Study
Step1: Next we're going to need to authenticate using the service account on the Datalab host.
Step2: Now we can create a clien... | Python Code:
!pip install --upgrade google-api-python-client==1.4.2
Explanation: Working with BigQuery tables and the Genomics API
Case Study: BRAF V600 mutations in CCLE cell-lines
In this notebook we'll show you how you might combine information available in BigQuery tables with sequence-reads that have been imported... |
12,705 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Load Data
In order to expediate the testing process, I added a debug flag to the pipeline method in our pipeline file which outputs the fixed and moving images prior to registration
Step1: ... | Python Code:
import sys
sys.path.append('../code/functions')
sys.path.append('/home/simpleElastix/build/SimpleITK-build/Wrapping/Python')
import pickle
import cv2
import time
import SimpleITK as sitk
import numpy as np
import matplotlib.pyplot as plt
import nibabel as nib
from cluster import Cluster
from tiffIO import ... |
12,706 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Excercises Electric Machinery Fundamentals
Chapter 1
Problem 1-16
Step1: Description
The core shown in Figure P1-2
Step2: Sketch the voltage present at the terminals of the coil.
SOLUTION
... | Python Code:
%pylab notebook
Explanation: Excercises Electric Machinery Fundamentals
Chapter 1
Problem 1-16
End of explanation
N = 500
dphi = array([0.010, -0.020, 0.010, 0.010]) # [Wb]
dt = array([2e-3 , 3e-3, 2e-3, 1e-3]) # [s]
Explanation: Description
The core shown in Figure P1-2:
<img src="figs/FigC_P1-2.jp... |
12,707 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using grlc from python
Being written in python itself, it is easy to use grlc from python. Here we show how to use grlc to run a SPARQL query which is stored on github.
First we start by imp... | Python Code:
import json
import pandas as pd
from io import StringIO
import grlc
import grlc.utils as utils
import grlc.swagger as swagger
Explanation: Using grlc from python
Being written in python itself, it is easy to use grlc from python. Here we show how to use grlc to run a SPARQL query which is stored on github.... |
12,708 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sections
Introduction to Sequential Backward Selection
Further Reading
Iris Example
Wine Data Example
Gridsearch Example 1
Gridsearch Example 2
Introduction to Sequential Backward Selection
... | Python Code:
from mlxtend.sklearn import SBS
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
knn = KNeighborsClassifier(n_neighbors=4)
sbs = SBS(knn, k_features=2, scoring='accuracy', cv=5)
sbs.fit(X, y)
print('Indices of selecte... |
12,709 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Load IPython support for working with MPI tasks
Step1: Let's also load the plotting and numerical libraries so we have them ready for visualization later on.
Step2: Now, we load the MPI li... | Python Code:
from ipyparallel import Client, error
cluster = Client()
view = cluster[:]
Explanation: Load IPython support for working with MPI tasks
End of explanation
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
Explanation: Let's also load the plotting and numerical libraries so we have them ... |
12,710 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
This notebook demonstrates how BioThings Explorer can be used to answer the following query
Step1: Step 2
Step2: The df object contains the full output from BioThings Explorer... | Python Code:
from biothings_explorer.hint import Hint
ht = Hint()
prdx1 = ht.query("PRDX1")['Gene'][0]
prdx1
Explanation: Introduction
This notebook demonstrates how BioThings Explorer can be used to answer the following query:
"Finding Marketed Drugs that Might Tre... |
12,711 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1A.2 - Deviner la langue d'un texte (correction)
Calcul d'un score pour détecter la langue d'un texte. Ce notebook aborde les dictionnaires, les fichiers et les graphiques (correction).
Step... | Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
Explanation: 1A.2 - Deviner la langue d'un texte (correction)
Calcul d'un score pour détecter la langue d'un texte. Ce notebook aborde les dictionnaires, les fichiers et les graphiques (correction).
End of explanation
def re... |
12,712 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tables to Networks, Networks to Tables
Networks can be represented in a tabular form in two ways
Step1: At this point, we have our stations and trips data loaded into memory.
How we constr... | Python Code:
# This block of code checks to make sure that a particular directory is present.
if "divvy_2013" not in os.listdir('datasets/'):
print('Unzip the divvy_2013.zip file in the datasets folder.')
stations = pd.read_csv('datasets/divvy_2013/Divvy_Stations_2013.csv', parse_dates=['online date'], index_col='i... |
12,713 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Transform EEG data using current source density (CSD)
This script shows an example of how to use CSD [1] [2] [3]_.
CSD takes the spatial Laplacian of the sensor signal (derivative in both
x ... | Python Code:
# Authors: Alex Rockhill <aprockhill206@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
Explanation: Transform EEG data using current source density (CSD)
This script shows an ... |
12,714 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Images
Step1: Get the Header-Data-Units (hdu's) from a fits file. This particular one only has 1.
Step2: This 4x3x2 matrix can actually also be generated from scratch using basic numpy
Ste... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
# import pyfits as fits # deprecated
from astropy.io import fits
Explanation: Images: rows, columns and all that jazzy mess....
Two dimensional data arrays are normally stored in column-major or row-major order. In... |
12,715 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Strategies
The strategy object describes the behaviour of an agent, given its vocabulary. The main algorithms that vary among strategies are
Step1: Let's create a strategy. We will also nee... | Python Code:
import naminggamesal.ngstrat as ngstrat
import naminggamesal.ngvoc as ngvoc
Explanation: Strategies
The strategy object describes the behaviour of an agent, given its vocabulary. The main algorithms that vary among strategies are:
* how to choose a link (meaning-word) to enact,
* how to guess a meaning fr... |
12,716 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter02
1 Discrete random variable
1.1 (0-1) distribution
$P(X=k)=p^k(1-p)^{1-k}, k=0,1 \space (0<p<1)$
1.2 binomial distribution
$P(X=k)=C_n^kp^k(1-p)^{n-k}, \space k=0,1,\ldots,n \space ... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
plt.plot([1,2], [1,1], linewidth=2,c='k')
plt.plot([1,1], [0,1],'k--', linewidth=2)
plt.plot([2,2], [0,1],'k--', linewidth=2)
plt.plot([0,1], [1,1],'k--')
plt.xticks([1,2],[r'$a$',r'$b$'])
plt.yticks([1],[r'$\frac{1}{b-a}$'])
plt.xlabel('x')
plt.ylabel(r'$... |
12,717 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Inverse Kinematics (2D)
Step1: Coordinate Transformation
Step2: Parameters of robot arm
Step3: Forward Kinematics
Step4: Inverse Kinematics
Numerical Solution with Jacobian
NOTE | Python Code:
%matplotlib notebook
from matplotlib import pylab as plt
from numpy import sin, cos, pi, matrix, random, linalg, asarray
from scipy.linalg import pinv
from __future__ import division
from math import atan2
from IPython import display
from ipywidgets import interact, fixed
Explanation: Inverse Kinematics (2... |
12,718 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pivoted document length normalization
It is seen that in many cases normalizing the tfidf weights for each terms tends to favor weight of terms of the documents with shorter length. Pivoted ... | Python Code:
%matplotlib inline
from sklearn.linear_model import LogisticRegression
from gensim.corpora import Dictionary
from gensim.sklearn_api.tfidf import TfIdfTransformer
from gensim.matutils import corpus2csc
import numpy as np
import matplotlib.pyplot as py
import gensim.downloader as api
# This function returns... |
12,719 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Powering a Machine Learning Data Store with Redis Labs Cloud
This notebook demonstrates how to use the Machine Learning API for automatically analyzing each column of the IRIS dataset. In th... | Python Code:
# Setup the Sci-pype environment
import sys, os
# Only Redis Labs is needed for this notebook:
os.environ["ENV_DEPLOYMENT_TYPE"] = "RedisLabs"
# Load the Sci-pype PyCore as a named-object called "core" and environment variables
from src.common.load_ipython_env import *
Explanation: Powering a Machine Learn... |
12,720 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Analysing Smartwatch Data
This notebook gives an overview of how to use HeartPy in the analysis of raw PPG data taken from a commercial (Samsung) smartwatch device.
A signal measured this wa... | Python Code:
import numpy as np
import heartpy as hp
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('raw_ppg.csv')
df.keys()
Explanation: Analysing Smartwatch Data
This notebook gives an overview of how to use HeartPy in the analysis of raw PPG data taken from a commercial (Samsung) smartwatch dev... |
12,721 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Don't forget to delete the hdmi_out and hdmi_in when finished
Image Overlay 256 Color Filter Example
In this notebook, we will overlay an image on the output videofeed. By default, an image ... | Python Code:
from pynq.drivers.video import HDMI
from pynq import Bitstream_Part
from pynq.board import Register
from pynq import Overlay
Overlay("demo.bit").download()
Explanation: Don't forget to delete the hdmi_out and hdmi_in when finished
Image Overlay 256 Color Filter Example
In this notebook, we will overlay an ... |
12,722 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
This IPython notebook illustrates how to select the best learning based matcher. First, we need to import py_entitymatching package and other libraries as follows
Step1: Then, ... | Python Code:
# Import py_entitymatching package
import py_entitymatching as em
import os
import pandas as pd
# Set the seed value
seed = 0
!ls $datasets_dir
# Get the datasets directory
datasets_dir = em.get_install_path() + os.sep + 'datasets'
path_A = datasets_dir + os.sep + 'dblp_demo.csv'
path_B = datasets_dir + o... |
12,723 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pydiffexp
The pydiffexp package is meant to provide an interface between R and Python to do differential expression analysis.
Imports
Step1: Load Data
Each DEAnalysis object (DEA) operates ... | Python Code:
import pandas as pd
from pydiffexp import DEAnalysis
Explanation: Pydiffexp
The pydiffexp package is meant to provide an interface between R and Python to do differential expression analysis.
Imports
End of explanation
test_path = "/Users/jfinkle/Documents/Northwestern/MoDyLS/Python/sprouty/data/raw_data/a... |
12,724 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
pyIAST example (N$_2$, CO$_2$, H$_2$O)
data from Mason et al. here
construct models for pure-component adsorption isotherms
Step1: binary (CO$_2$/N$_2$ adsorption)
CO$_2$ partial pressure
S... | Python Code:
df_N2 = pd.read_csv("N2.csv", skiprows=1)
N2_isotherm = pyiast.ModelIsotherm(df_N2, loading_key="Loading(mmol/g)",
pressure_key="P(bar)", model='Henry')
pyiast.plot_isotherm(N2_isotherm)
N2_isotherm.print_params()
df_CO2 = pd.read_csv("CO2.csv", skiprows=1)
CO2_iso... |
12,725 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Profiling BatchFlow code
A profile is a set of statistics that describes how often and for how long various parts of the program executed.
This notebooks shows how to profile various parts o... | Python Code:
import sys
sys.path.append("../../..")
from batchflow import B, V, W
from batchflow.opensets import MNIST
from batchflow.models.torch import ResNet18
dataset = MNIST()
Explanation: Profiling BatchFlow code
A profile is a set of statistics that describes how often and for how long various parts of the progr... |
12,726 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bayesian MLP for MNIST using preconditioned SGLD
We use the Jax Bayes library
by James Vuckovic
to fit an MLP to MNIST using SGD, and SGLD (with RMS preconditioning).
Code is based on
Ste... | Python Code:
%%capture
!pip install git+https://github.com/deepmind/dm-haiku
!pip install git+https://github.com/jamesvuc/jax-bayes
import haiku as hk
import jax.numpy as jnp
from jax.experimental import optimizers
import jax
import jax_bayes
import sys, os, math, time
import numpy as onp
import numpy as np
from functo... |
12,727 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Imports and setup
Imports
Step4: Common functions
Step5: Get charges
Calculate RESP charges using Gaussian through submit_gaussian for use with GAFF.
Step6: Parameterize molecule in GAFF ... | Python Code:
import re, os, sys, shutil
import shlex, subprocess
import glob
import pandas as pd
import panedr
import numpy as np
import MDAnalysis as mda
import nglview
import matplotlib.pyplot as plt
import parmed as pmd
import py
import scipy
from scipy import stats
from importlib import reload
from thtools import c... |
12,728 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pandas
CLEPY - August Module of the month
Anurag Saxena
@_asaxena
Pandas - Python Data Analysis Library
pandas.pydata.org
Open Source
High Performance
Easy to use Data Structures and Data An... | Python Code:
import pandas as pd
import numpy as np
Explanation: Pandas
CLEPY - August Module of the month
Anurag Saxena
@_asaxena
Pandas - Python Data Analysis Library
pandas.pydata.org
Open Source
High Performance
Easy to use Data Structures and Data Analysis Tools
End of explanation
obj = pd.Series([1,3,4,5,6,7,8,9]... |
12,729 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Simple implementation of network propagation from Vanunu et. al.
Author
Step1: Load the interactome from Barabasi paper
Interactome downloaded from supplemental materials of http
Step2: Lo... | Python Code:
# import some useful packages
import numpy as np
import matplotlib.pyplot as plt
import seaborn
import networkx as nx
import pandas as pd
import random
# latex rendering of text in graphs
import matplotlib as mpl
mpl.rc('text', usetex = False)
mpl.rc('font', family = 'serif')
import sys
#sys.path.append('/... |
12,730 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Functions from Stream to Stream
This module describes one way of creating functions from a single stream to a single stream. Other ways of mapping a single input stream to a single output st... | Python Code:
import os
import sys
sys.path.append("../")
from IoTPy.core.stream import Stream, run
from IoTPy.agent_types.op import map_element
from IoTPy.agent_types.basics import fmap_e
from IoTPy.helper_functions.recent_values import recent_values
@fmap_e
def f(v): return v+10
# f is a function that maps a stream to... |
12,731 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Planning Algorithms
Do you remember on lesson 2 and 3 we discussed algorithms that basically solve MDPs? That is, find a policy given a exact representation of the environment. In this secti... | Python Code:
import numpy as np
import pandas as pd
import tempfile
import pprint
import json
import sys
import gym
from gym import wrappers
from subprocess import check_output
from IPython.display import HTML
Explanation: Planning Algorithms
Do you remember on lesson 2 and 3 we discussed algorithms that basically solv... |
12,732 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Parallelization
emcee supports parallelization out of the box. The algorithmic details are given in the paper but the implementation is very simple. The parallelization is applied across the... | Python Code:
import emcee3
import numpy as np
def log_prob(x):
return -0.5 * np.sum(x ** 2)
ndim, nwalkers = 10, 100
with emcee3.pools.InterruptiblePool() as pool:
ensemble = emcee3.Ensemble(log_prob, np.random.randn(nwalkers, ndim), pool=pool)
sampler = emcee3.Sampler()
sampler.run(ensemble, 1000)
Expl... |
12,733 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to pandas
by Maxwell Margenot
Part of the Quantopian Lecture Series
Step1: With pandas, it is easy to store, visualize, and perform calculations on your data. With only a few l... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Explanation: Introduction to pandas
by Maxwell Margenot
Part of the Quantopian Lecture Series:
www.quantopian.com/lectures
github.com/quantopian/research_public
pandas is a Python library that provides a collection of powerful data stru... |
12,734 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
IPython & D3
Let's start with a few techniques for working with data in ipython and then build a d3 network graph.
Step1: JS with IPython?
The nice thing about IPython is that we can write ... | Python Code:
# import requirments
from IPython.display import Image
from IPython.display import display
from IPython.display import HTML
from datetime import *
import json
from copy import *
from pprint import *
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
from ggplot import *
imp... |
12,735 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This example demonstrated that Flexx apps can be run interactively in the notebook.
Step1: Any widget can be shown by using it as a cell output
Step2: Because apps are really just Widgets,... | Python Code:
from flexx import app, ui, react
app.init_notebook()
# A bit of boilerplate to import an example app
import sys
#sys.path.insert(0, r'C:\Users\almar\dev\flexx\examples\ui')
sys.path.insert(0, '/home/almar/dev/pylib/flexx/examples/ui')
from twente_temperature import Twente
Explanation: This example demonstr... |
12,736 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PyBPS Tutorial
1. Initialization
The first thing to do is obviously to import the pybps package.
At the same time, we also import other useful packages.
Step1: Once the pybps is imported in... | Python Code:
import pybps
import os
import sys
import re
import sqlite3
import pandas as pd
from pandas.io import sql
import matplotlib.pyplot as plt
Explanation: PyBPS Tutorial
1. Initialization
The first thing to do is obviously to import the pybps package.
At the same time, we also import other useful packages.
End ... |
12,737 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Preparation
Let's get a look on our Pokémons. The function below will plot all the sprites of a specific Pokémon on screen. That way we can have an idea of what kind of problem we can f... | Python Code:
%matplotlib inline
from utility.plot import plot_all
#Plotting Bulbassaur ID = 1
plot_all(1)
#Plotting Charmander ID = 4
plot_all(4)
#Plotting Squirtle ID = 7
plot_all(7)
Explanation: Data Preparation
Let's get a look on our Pokémons. The function below will plot all the sprites of a specific Pokémon on sc... |
12,738 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
K Means Clustering with Python
K Means Clustering is an unsupervised learning algorithm that tries to cluster data based on their similarity. Unsupervised learning means that there is no out... | Python Code:
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: K Means Clustering with Python
K Means Clustering is an unsupervised learning algorithm that tries to cluster data based on their similarity. Unsupervised learning means that there is no outcome to be predicted, and the a... |
12,739 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Playing with the Gaussian Distribution
There was a statement I saw online
Step1: I created a function that takes the observation, mean and standard deviation and returns the z-score. Notic... | Python Code:
def z_score(x, m, s):
return (x - m) / s
Explanation: Playing with the Gaussian Distribution
There was a statement I saw online: "I don't know anyone with an IQ above 7 that respects Hillary Clinton."
Of course, the person is trying to sound smart and snarky but I don't think they pull it off very well... |
12,740 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
VisPy colormaps
This notebook illustrates the colormap API provided by VisPy.
List all colormaps
Step1: Discrete colormaps
Discrete colormaps can be created by giving a list of colors, and ... | Python Code:
import numpy as np
from vispy.color import (get_colormap, get_colormaps, Colormap)
from IPython.display import display_html
for cmap in get_colormaps():
display_html('<h3>%s</h3>' % cmap, raw=True)
display_html(get_colormap(cmap))
Explanation: VisPy colormaps
This notebook illustrates the colormap ... |
12,741 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Estimation of a Categorical distribution
Maximum Likelihood Estimation
We observe a dataset ${x^{(n)}}_{n=1\dots N}$. The model for a single observation is a categorical distribution with pa... | Python Code:
# %load template_equations.py
from IPython.display import display, Math, Latex, HTML
import notes_utilities as nut
from importlib import reload
reload(nut)
Latex('$\DeclareMathOperator{\trace}{Tr}$')
L = nut.pdf2latex_dirichlet(x=r'\pi', a=r'a',N=r'I', i='i')
display(HTML(nut.eqs2html_table(L)))
Explanatio... |
12,742 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Batch Serving Design Pattern
This notebook demonstrates the Batch Serving design pattern using BigQuery
Simple text classification model
Let's use the same model that was used in serving_fun... | Python Code:
!find export/probs/
%%bash
LOCAL_DIR=$(find export/probs | head -2 | tail -1)
BUCKET=ai-analytics-solutions-kfpdemo
gsutil rm -rf gs://${BUCKET}/mlpatterns/batchserving
gsutil cp -r $LOCAL_DIR gs://${BUCKET}/mlpatterns/batchserving
gsutil ls gs://${BUCKET}/mlpatterns/batchserving
Explanation: Batch Serving... |
12,743 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lab 2 - Logistic Regression (LR) with MNIST
This lab corresponds to Module 2 of the "Deep Learning Explained" course. We assume that you have successfully completed Lab 1 (Downloading the MN... | 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 2 - Logistic Regression (LR) with MNIST
This lab corresponds to Module 2 of the "Deep Learning Explained" course. We assume that you have succ... |
12,744 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cython
The Cython language is a superset of the Python language that additionally
supports calling C functions and declaring C types on variables and class
attributes.
This allows the comp... | Python Code:
import numpy as np
Explanation: Cython
The Cython language is a superset of the Python language that additionally
supports calling C functions and declaring C types on variables and class
attributes.
This allows the compiler to generate very efficient C code from Cython code.
Write Python code that call... |
12,745 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data passing tutorial
Data passing is the most important aspect of Pipelines.
In Kubeflow Pipelines, the pipeline authors compose pipelines by creating component instances (tasks) and connec... | Python Code:
from typing import NamedTuple
import kfp
from kfp.components import InputPath, InputTextFile, OutputPath, OutputTextFile
from kfp.components import func_to_container_op
from kfp_tekton.compiler import TektonCompiler
import os
os.environ["DEFAULT_ACCESSMODES"] = "ReadWriteMany"
os.environ["DEFAULT_STORAGE_S... |
12,746 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
First, here's the SPA power function
Step1: Here are two helper functions for computing the dot product over space, and for plotting the results
Step2: So, that lets us take a vector and t... | Python Code:
def power(s, e):
x = np.fft.ifft(np.fft.fft(s.v) ** e).real
return spa.SemanticPointer(data=x)
Explanation: First, here's the SPA power function:
End of explanation
def spatial_dot(v, X, Y, Z, xs, ys, transform=1):
if isinstance(v, spa.SemanticPointer):
v = v.v
vs = np.zeros((len(ys... |
12,747 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
DV360 Report To BigQuery
Move existing DV360 reports into a BigQuery table.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use... | Python Code:
!pip install git+https://github.com/google/starthinker
Explanation: DV360 Report To BigQuery
Move existing DV360 reports into a BigQuery table.
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 the License.... |
12,748 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
i am trying to do hyperparemeter search with using scikit-learn's GridSearchCV on XGBoost. During gridsearch i'd like it to early stop, since it reduce search time drastically and (... | Problem:
import numpy as np
import pandas as pd
import xgboost.sklearn as xgb
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import TimeSeriesSplit
gridsearch, testX, testY, trainX, trainY = load_data()
assert type(gridsearch) == sklearn.model_selection._search.GridSearchCV
assert type(tr... |
12,749 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Iterables
Some steps in a neuroimaging analysis are repetitive. Running the same preprocessing on multiple subjects or doing statistical inference on multiple files. To prevent the creation ... | Python Code:
from nipype import Node, Workflow
from nipype.interfaces.fsl import BET, IsotropicSmooth
# Initiate a skull stripping Node with BET
skullstrip = Node(BET(mask=True,
in_file='/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz'),
name="skullstrip")
Explanat... |
12,750 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
About
This notebook demonstrates classifiers, which are provided by Reproducible experiment platform (REP) package. <br /> REP contains following classifiers
* scikit-learn
* TMVA
* XGBoost... | Python Code:
!cd toy_datasets; wget -O MiniBooNE_PID.txt -nc MiniBooNE_PID.txt https://archive.ics.uci.edu/ml/machine-learning-databases/00199/MiniBooNE_PID.txt
import numpy, pandas
from rep.utils import train_test_split
from sklearn.metrics import roc_auc_score
data = pandas.read_csv('toy_datasets/MiniBooNE_PID.txt', ... |
12,751 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regression
Timothy Helton
<a id='toc'></a>
Table of Contents
Imports
Framework
Correlation Grid Function
Correlation Heatmap Function
Plot Regression Function
Plot Residuals Function
Predict... | Python Code:
from collections import OrderedDict
import itertools
import os
import os.path as osp
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
import statsmodels.formula.api as smf
import statsmodels.graphics.regressionplots as smrp
import sta... |
12,752 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ASE Analysis
Step1: General
This is Table S8 from the 2015 GTEx paper.
Total sites ≥30 reads | Sites 30 reads ASE p < 0.005 | Sites 30 reads ASE p < 0.005 (%)
Minimum ... | Python Code:
import cPickle
import glob
import gzip
import os
import random
import shutil
import subprocess
import sys
import cdpybio as cpb
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
pd.options.mode.chained_assignment = None
import pybedtools as pbt
from scipy.stats import fisher_exact
impo... |
12,753 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Population rate model of generalized integrate-and-fire neurons
This script simulates a finite network of generalized integrate-and-fire (GIF) neurons directly on the mesoscopic population l... | Python Code:
%matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import nest
Explanation: Population rate model of generalized integrate-and-fire neurons
This script simulates a finite network of generalized integrate-and-fire (GIF) neurons directly on the mesoscopic population level... |
12,754 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
First approach
Write a function that reads an xyz trajectory file in. We are going to need to be able to separate numbers from atomic symbols; an XYZ trajectory file looks like
Step1: CODIN... | Python Code:
def skeleton_naive_xyz_parser(path):
'''
Simple xyz parser.
'''
# Read in file
lines = None
with open(path) as f:
lines = f.readlines()
# Process lines
# ...
# Return processed lines
# ...
return lines
lines = skeleton_naive_xyz_parser(xyz_path)
lines... |
12,755 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 14</font>
Download
Step1: Web Scraping | Python Code:
# Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 14</font>
Download: http://github.com/dsacademybr
End of explanation
# Bi... |
12,756 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cython, que no CPython
No, no nos hemos equivocado en el título, hoy vamos a hablar de Cython.
¿Qué es Cython?
Cython son dos cosas
Step1: Creamos una matriz cuadrada relativamente grande (... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Cython, que no CPython
No, no nos hemos equivocado en el título, hoy vamos a hablar de Cython.
¿Qué es Cython?
Cython son dos cosas:
Por una parte, Cython es un lenguaje de programación (un superconjunto de Python) que une P... |
12,757 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to Non-Personalized Recommenders
The recommendation problem
Recommenders have been around since at least 1992. Today we see different flavours of recommenders, deployed across d... | Python Code:
from IPython.core.display import Image
Image(filename='./imgs/recsys_arch.png')
Explanation: Introduction to Non-Personalized Recommenders
The recommendation problem
Recommenders have been around since at least 1992. Today we see different flavours of recommenders, deployed across different verticals:
Am... |
12,758 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A Simple Autoencoder
We'll start off by building a simple autoencoder to compress the MNIST dataset. With autoencoders, we pass input data through an encoder that makes a compressed represen... | Python Code:
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to c... |
12,759 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
After Funding
Funding이 시작되기 전에는 예측이 어려웠다면 Funding이 시작된 이후에는 예측을 할 수 있을까?
Funding이 시작된 이후 5일까지 각 날짜별로 얼마만큼의 금액이 펀딩되어야 최종적으로 성공할 것인지 예측
Attributes
Step1: 1. Distribution Test
Step2: 성공/실패 프... | Python Code:
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import cross_val_score
from sklearn.cross_validation import KFold
from sklearn.cross_validation import StratifiedKFold
from sklearn... |
12,760 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial
Step1: We create a three-dimansional vector field with domain that spans between
Step2: Now, we can create a vector field object and initialise it so that
Step3: Please note, tha... | Python Code:
from oommffield import Field, read_oommf_file
Explanation: Tutorial: Manipulating OOMMF vector field files
In this tutorial, reading and writing of OOMMF vector field files (omf and ohf) are demonstrated. As usual, we need to import the Field class, but this time also the read_oommf_file function.
End of e... |
12,761 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Transform QCLCD Data Function
Step1: Load The Data as CSV
This is QCLCD data for PDX. It is what will be used to train the model.
Step2: Run The Model and Fit Predictions | Python Code:
# downloaded weather data from http://www.ncdc.noaa.gov/qclcd/QCLCD
def load_weather_frame(filename):
#load the weather data and make a date
data_raw = pd.read_csv(filename, dtype={'Time': str, 'Date': str})
data_raw['WetBulbCelsius'] = data_raw['WetBulbCelsius'].astype(float)
times = []
... |
12,762 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Matplotlib
Introduction
Matplotlib is a library for producing publication-quality figures. mpl (for short) was designed from the bottom-up to serve dual-purposes. First, to allow for interac... | Python Code:
import matplotlib
print(matplotlib.__version__)
print(matplotlib.get_backend())
Explanation: Matplotlib
Introduction
Matplotlib is a library for producing publication-quality figures. mpl (for short) was designed from the bottom-up to serve dual-purposes. First, to allow for interactive, cross-platform con... |
12,763 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 5
Step1: 5-1-3. モデルの評価
性能を測るといっても，その目的によって指標を変える必要がある.
どのような問題で，どのような指標を用いることが一般的か？という問いに対しては，先行研究を確認することを勧める．
また，指標それぞれの特性(数学的な意味)を知っていることもその役に立つだろう．
参考文献
Step2: 5-2. 問題に合わせたコーディ... | Python Code:
# 1. データセットを用意する
from sklearn import datasets
iris = datasets.load_iris() # ここではIrisデータセットを読み込む
print(iris.data[0], iris.target[0]) # 1番目のサンプルのデータとラベル
# 2.学習用データとテスト用データに分割する
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target)
# 3... |
12,764 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
In this notebook, we will use Long Short Term Memory RNN to develop a time series forecasting model.
The dataset used for the examples of this notebook is on air pollution measured by concen... | Python Code:
from __future__ import print_function
import os
import sys
import pandas as pd
import numpy as np
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import datetime
#Read the dataset into a pandas.DataFrame
df = pd.read_csv('datasets/PRSA_data_2010.1.1-2014.12.31.csv')
print('Sha... |
12,765 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
FDMS TME3
Kaggle How Much Did It Rain? II
Florian Toque & Paul Willot
Notes
We tried different model, like SVM regression, MLP, Random Forest and KNN as recommanded by the winning team of ... | Python Code:
# from __future__ import exam_success
from __future__ import absolute_import
from __future__ import print_function
%matplotlib inline
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import random
import pandas as pd
import scipy.stats as stats
# Sk cheatsfrom sklearn... |
12,766 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: 훈련 후 정수 양자화
<table class="tfo-notebook-buttons" align="left">
<td> <a target="_blank" href="https
Step2: TensorFlow 모델 생성하기
MNIST 데이터세트에... | 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... |
12,767 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Lesson 6 - Supervised Learning
Machine learning (ML)
The sentiment analysis program we wrote earlier (in session 1) adopts a non-machine learning algorithm. That is, it tries to defin... | Python Code:
def feature_extractor(word):
Extract the features for a given word and return a dictonary of the features
start_letter = word[0]
last_letter = word[-1]
return {'start_letter' : start_letter,'last_letter' : last_letter}
def main():
print(feature_extractor('poonacha'))
main()
E... |
12,768 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Calculation of Equilibrium Concentrations in Competitive Binding Experiment
This notebook uses analytical solution of equilibrium expressions for 2 ligands competing for 1 population of prot... | Python Code:
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from IPython.display import display, Math, Latex #Do we even need this anymore?
%pylab inline
Explanation: Calculation of Equilibrium Concentrations in Competitive Binding Experiment
This notebook uses analytical solution of equilibri... |
12,769 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Simply use the metric we created to define the quality of a app. If the weighted rating is no less than 4.0, it can be seen as a good app. If the weighted rating is no more than 2.5, it is a... | Python Code:
good_app = app.loc[app['weighted_rating'] >=4.0]
bad_app = app.loc[app['weighted_rating'] <=2.5]
good_app = good_app.reset_index(drop=True)
bad_app = bad_app.reset_index(drop=True)
category = app['category']
cate_list = []
for i in category.unique():
cate = i.lower()
cate_list.append(cate)
Explanat... |
12,770 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SINGA Core Classes
<img src="http
Step1: NOTE
Step2: Tensor
A tensor instance represents a multi-dimensional array allocated on a device instance.
It provides linear algbra operations, lik... | Python Code:
from singa import device
default_dev = device.get_default_device()
gpu = device.create_cuda_gpu() # the first gpu device
gpu
Explanation: SINGA Core Classes
<img src="http://singa.apache.org/en/_static/images/singav1-sw.png" width="500px"/>
Device
A device instance represents a hardware device with multip... |
12,771 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<img src="../../../../images/qiskit-heading.gif" alt="Note
Step1: In this section, we first judge the version of Python and import the packages of qiskit, math to implement the following co... | Python Code:
# import math lib
from math import pi
# import Qiskit
from qiskit import Aer, IBMQ, execute
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
# import basic plot tools
from qiskit.tools.visualization import plot_histogram
# To use local qasm simulator
backend = Aer.get_backend('qasm_sim... |
12,772 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Logic
This Jupyter notebook acts as supporting material for topics covered in Chapter 6 Logical Agents, Chapter 7 First-Order Logic and Chapter 8 Inference in First-Order Logic of the book A... | Python Code:
from utils import *
from logic import *
from notebook import psource
Explanation: Logic
This Jupyter notebook acts as supporting material for topics covered in Chapter 6 Logical Agents, Chapter 7 First-Order Logic and Chapter 8 Inference in First-Order Logic of the book Artificial Intelligence: A Modern Ap... |
12,773 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
先说明一下，特殊方法的存在是为了被 Python 解释器调用的，我们自己不需要调用它。也就是说没有 my_object.__len__() 这种写法，而是应该使用 len(my_object)。一般来说，通过内置函数（len, iter, str 等等）来使用特殊方法是最好的选择，这些内置函数不仅会调用特殊方法，通常还会提供特殊的好处。而且对于内置类来说，它的速度更快。
实现向... | Python Code:
from math import hypot
class Vector:
def __init__(self, x = 0, y = 0):
self.x = x
self.y = y
def __repr__(self):
# %r 获取对象各个属性标准字符串表现形式，这是个好习惯，它说明了一个关键点，Vector(1,2) 和 vector('1','2') 是不一样的
# 后者会在定义的时候报错，因为对象的构造只接收数值，不接受字符串
return "Vector(%r, %r)" % (self... |
12,774 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Estructuras de datos
Python posee además de los tipos de datos básicos, otros tipos de datos más complejos. Se trata de las tuplas, las listas y los diccionarios.
Estos tres tipos, pueden al... | Python Code:
# Ejemplo de lista, los valores van entre corchetes
una_lista = [4, "Hola", 6.0, 99 ]
# Ejemplo de tupla, los valores van entre paréntesis
una_tupla = (4, "Hola", 6.0, 99)
print ("Lista: " , una_lista)
print ("Tupla: " , una_tupla)
# Las tuplas y las listas aceptan operadores de comparación y devuelven un ... |
12,775 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tree
定义
一棵二叉树的定义如下。key可以存储任意的对象，亦即每棵树也可以是其他树的子树。
Step1: 遍历
前序
中序
后序
Step2: 二叉堆实现优先队列
二叉堆是队列的一种实现方式。
二叉堆可以用完全二叉树来实现。所谓完全二叉树（complete binary tree），有定义如下：
A complete binary tree is a binary t... | Python Code:
class BinaryTree():
def __init__(self, root_obj):
self.key = root_obj
self.left_child = None
self.right_child = None
def insert_left(self, new_node):
# if the tree do not have a left child
# then create a node: one tree without children
if self.l... |
12,776 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Is Augmentation Necessary?
In this notebook, we will check how the network trained on ordinary data copes with the augmented data and what will happen if it is learned from the augmented dat... | Python Code:
import sys
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqn
%matplotlib inline
sys.path.append('../../..')
sys.path.append('../../utils')
import utils
from secondbatch import MnistBatch
from simple_conv_model import ConvModel
from batchflow import V, B
from batchflow... |
12,777 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
In this notebook, we will show how to load pre-trained models and draw things with sketch-rnn
Step3: define the path of the model you want to load, and also the path of the dataset
Step4: ... | Python Code:
# import the required libraries
import numpy as np
import time
import random
import cPickle
import codecs
import collections
import os
import math
import json
import tensorflow as tf
from six.moves import xrange
# libraries required for visualisation:
from IPython.display import SVG, display
import PIL
fro... |
12,778 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Domain DPAPI Backup Key Extraction
Metadata
| | |
|
Step1: Download & Process Mordor Dataset
Step2: Analytic I
Monitor for any SecretObject with the string BCKUPKEY in... | Python Code:
from openhunt.mordorutils import *
spark = get_spark()
Explanation: Domain DPAPI Backup Key Extraction
Metadata
| | |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2019/06/20 |
| modification date | 2020/09/20 |
| playbook rel... |
12,779 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook is a brief sketch of how to use Grover's algorithm.
We start by declaring all necessary imports.
Step1: Grover's algorithm can be used to amplify the probability of an oracle-... | Python Code:
from itertools import product
from mock import patch
from grove.amplification.grover import Grover
Explanation: This notebook is a brief sketch of how to use Grover's algorithm.
We start by declaring all necessary imports.
End of explanation
target_bitstring = '010'
bit = ("0", "1")
bitstring_map = {}
targ... |
12,780 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bayesian Linear Regression
Computational bayes final project.
Nathan Yee
Uma Desai
First example to gain understanding is taken from Cypress Frankenfeld.
http
Step1: Load data from csv fi... | Python Code:
from __future__ import print_function, division
% matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import math
import numpy as np
from thinkbayes2 import Pmf, Cdf, Suite, Joint, EvalNormalPdf
import thinkplot
import pandas as pd
import matplotlib.pyplot as plt
Explanation: Bayesian Linea... |
12,781 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This script shows how to use the existing code in opengrid to create a baseload electricity consumption benchmark.
Step1: Script settings
Step2: We create one big dataframe, the columns ar... | Python Code:
import os, sys
import inspect
import numpy as np
import datetime as dt
import time
import pytz
import pandas as pd
import pdb
script_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
# add the path to opengrid to sys.path
sys.path.append(os.path.join(script_dir, os.pardir, os.... |
12,782 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fitting Models Exercise 1
Imports
Step1: Fitting a quadratic curve
For this problem we are going to work with the following model
Step2: First, generate a dataset using this model using th... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
Explanation: Fitting Models Exercise 1
Imports
End of explanation
a_true = 0.5
b_true = 2.0
c_true = -4.0
Explanation: Fitting a quadratic curve
For this problem we are going to work with the following model:... |
12,783 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Analysis of DSLWP-B 2018-08-12 SSDV transmission
This notebook analyzes SSDV transmissions made by DSLWP-B from the Moon.
Step1: We load a file containing the relevant GMSK transmission. Th... | Python Code:
%matplotlib inline
import numpy as np
import scipy.signal
import matplotlib.pyplot as plt
Explanation: Analysis of DSLWP-B 2018-08-12 SSDV transmission
This notebook analyzes SSDV transmissions made by DSLWP-B from the Moon.
End of explanation
x = np.fromfile('/home/daniel/Descargas/DSLWP-B_PI9CAM_2018-08-... |
12,784 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: <H2>Distance from a point to a line</H2>
\begin{equation}
\frac{|Ax+By+C|}{\sqrt{A^2+B^2}}.
\end{equation}
Step3: <H2>Distance from a point the identity line</H2>
\begin{equation}
\... | Python Code:
def distance(mypoint, myline):
Calculates the distance from a point to a line
x, y = mypoint
A, B, C = myline
return np.abs(A*x + B*y + C) / np.sqrt(np.power(A,2)+np.power(B,2))
mypoint = (5,1)
myline = (3,-1, 1)
distance(mypoint, myline) # 15/np.sqrt(10) ... |
12,785 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Fully-Connected Neural Nets
In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since t... | Python Code:
# As usual, a bit of setup
from __future__ import print_function
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
fro... |
12,786 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Comparing collections (Part Two)
Motivation
Review Part I
Describe what we want
Kendall's tau
Rank-biased overlap
Apply to data
Inspired by "A Similarity Measure for Indefinite Rankings", ht... | Python Code:
import yaml
import time
import operator
import string
import re
import csv
import random
import nltk.tokenize
from sklearn.feature_extraction import text
import twitter
import scipy
Explanation: Comparing collections (Part Two)
Motivation
Review Part I
Describe what we want
Kendall's tau
Rank-biased overla... |
12,787 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<div class="alert alert-block alert-info" style="margin-top
Step1: Set the random seed
Step2: Use this function for plotting
Step3: <a id="ref0"></a>
<h2 align=center>Make Some Data </h2>... | Python Code:
from torch import nn,optim
import torch
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from torch.utils.data import Dataset, DataLoader
Explanation: <div class="alert alert-block alert-info" style="margin-top: 20px">
<a href="http://cocl.us/pytorch_link_top"><im... |
12,788 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Correlation of DCO2 and weight-corrected DCO2 with PCO2
This file contains the code used for data processing, statistical analysis and visualization described in the following paper
Step1: ... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import re
import operator
import warnings
from pandas import Series, DataFrame
from scipy.stats.stats import pearsonr
from scipy.stats import ttest_rel
from datetime import datetime, timedelta
from pprint import pprint
%matplo... |
12,789 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The Lorenz63 model implemented in FABM
The equations read
Step1: Import pyfabm - the python module that contains the Fortran based FABM
Step2: Configuration
The model configuration is done... | Python Code:
import numpy
import scipy.integrate
Explanation: The Lorenz63 model implemented in FABM
The equations read:
$ \frac{dx}{dt} = \sigma ( y - x ) - \beta x y$
$ \frac{dy}{dt} = x ( \rho - z ) - y$
$ \frac{dz}{dt} = x y - \beta z$
For further information see
Import standard python packages and pyfabm
End of ex... |
12,790 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Validation Playground
Watch a short tutorial video or read the written tutorial
This notebook assumes that you created at least one expectation suite in your project.
Here you will learn how... | Python Code:
import json
import great_expectations as ge
import great_expectations.jupyter_ux
from great_expectations.datasource.types import BatchKwargs
import datetime
Explanation: Validation Playground
Watch a short tutorial video or read the written tutorial
This notebook assumes that you created at least one expec... |
12,791 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualization with Matplotlib
Learning Objectives
Step1: Overview
The following conceptual organization is simplified and adapted from Benjamin Root's AnatomyOfMatplotlib tutorial.
Figures ... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
Explanation: Visualization with Matplotlib
Learning Objectives: Learn how to make basic plots using Matplotlib's pylab API and how to use the Matplotlib documentation.
This notebook focuses only on the Matplotlib API, rather that the bro... |
12,792 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I've read several posts about how to convert Pandas columns to float using pd.to_numeric as well as applymap(locale.atof). | Problem:
import pandas as pd
s = pd.Series(['2,144.78', '2,036.62', '1,916.60', '1,809.40', '1,711.97', '6,667.22', '5,373.59', '4,071.00', '3,050.20', '-0.06', '-1.88', '', '-0.13', '', '-0.14', '0.07', '0', '0'],
index=['2016-10-31', '2016-07-31', '2016-04-30', '2016-01-31', '2015-10-31', '2016-01-31', ... |
12,793 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Computing various MNE solutions
This example shows example fixed- and free-orientation source localizations
produced by the minimum-norm variants implemented in MNE-Python
Step1: Fixed orie... | Python Code:
# Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
# Read data (just MEG here for speed, t... |
12,794 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Building your Deep Neural Network
Step2: 2 - Outline of the Assignment
To build your neural network, you will be implementing several "helper functions". These helper functions will be used... | Python Code:
import numpy as np
import h5py
import matplotlib.pyplot as plt
from testCases_v2 import *
from dnn_utils_v2 import sigmoid, sigmoid_backward, relu, relu_backward
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rc... |
12,795 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Performing Scenario Discovery in Python
The purpose of example is to demonstrate how one can do scenario discovery in python. I will demonstrate how we can perform both PRIM in an interactiv... | Python Code:
import pandas as pd
data = pd.read_csv("./data/bryant et al 2010 data.csv", index_col=False)
x = data.iloc[:, 2:11]
y = data.iloc[:, 15].values
Explanation: Performing Scenario Discovery in Python
The purpose of example is to demonstrate how one can do scenario discovery in python. I will demonstrate how w... |
12,796 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Análisis de los datos obtenidos
Uso de ipython para el análsis y muestra de los datos obtenidos durante la producción.Se implementa un regulador experto. Los datos analizados son del día 13 ... | Python Code:
#Importamos las librerías utilizadas
import numpy as np
import pandas as pd
import seaborn as sns
#Mostramos las versiones usadas de cada librerías
print ("Numpy v{}".format(np.__version__))
print ("Pandas v{}".format(pd.__version__))
print ("Seaborn v{}".format(sns.__version__))
#Abrimos el fichero csv co... |
12,797 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
課題1
1900年から2200年までのうるう年を教えてくれるプログラムを書いてください。
ただし、うるう年は下記のルールで決定されるものとします。
西暦が4で割り切れる年は閏年である。
ただし、100で割り切れる年は閏年ではない。
ただし、400で割り切れる年は閏年である。
課題2
任意の西暦年を渡すと、うるう年か否かを判定してくれる関数を、def機能を使って実装してく... | Python Code:
def leap_year(y):
if y < 0:
print("Please input positive number.")
return
if y%4 == 0:
if y%400 == 0:
print("%d is leap year."%y)
return
elif y%100 == 0:
print("%d is NOT leap year."%y)
return
print("%d is lea... |
12,798 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<img src="../../images/qiskit-heading.gif" alt="Note
Step1: First we set up an empty program for one qubit.
Step2: We don't want to do anything to the qubit, so we'll skip straight to read... | Python Code:
import qiskit
Explanation: <img src="../../images/qiskit-heading.gif" alt="Note: In order for images to show up in this jupyter notebook you need to select File => Trusted Notebook" width="500 px" align="left">
Doing nothing with Qiskit Terra
We are going to use Qiskit to do nothing.
End of explanation
qr ... |
12,799 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: Text classification with TensorFlow Lite Model Maker
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="http... | 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... |
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