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5,100 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Assignment 3b
Step2: Encoding issues with txt files
For Windows users, the file “AnnaKarenina.txt” gets the encoding cp1252.
In order to open the file, you have to add encoding='utf... | Python Code:
# Downloading data - you get this for free :-)
import requests
import os
def download_book(url):
Download book given a url to a book in .txt format and return it as a string.
text_request = requests.get(url)
text = text_request.text
return text
book_urls = dict()
book_urls['HuckF... |
5,101 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data generation
Step1: Preparing data set sweep
First, we're going to define the data sets that we'll sweep over. The following cell does not need to be modified unless if you wish to chang... | Python Code:
from os.path import join, expandvars
from joblib import Parallel, delayed
from glob import glob
from os import system
from tax_credit.framework_functions import (parameter_sweep,
generate_per_method_biom_tables,
move_r... |
5,102 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
train-test split evaluation random forest on the housing dataset
| Python Code::
from pandas import read_csv
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
# load dataset
url = 'https://raw.githubusercontent.com/jbrownlee/Datasets/master/housing.csv'
dataframe = read_csv(url, heade... |
5,103 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Welcome to BASS!
Version
Step1: Instructions
For help, check out the wiki
Step2: OPTIONAL GRAPHS AND ANALYSIS
The following blocks are optional calls to other figures and analysis
Display ... | Python Code:
from BASS import *
Explanation: Welcome to BASS!
Version: Beta 2.0
Created by Abigail Dobyns and Ryan Thorpe
BASS: Biomedical Analysis Software Suite for event detection and signal processing.
Copyright (C) 2015 Abigail Dobyns
This program is free software: you can redistribute it and/or modify
it under t... |
5,104 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
GPU and CPU settings
If GPU is not available, comment out the bottom block.
Step1: Plot training and test accuracy | Python Code:
# If GPU is not available:
# GPU_USE = '/cpu:0'
# config = tf.ConfigProto(device_count = {"GPU": 0})
# If GPU is available:
config = tf.ConfigProto()
config.log_device_placement = True
config.allow_soft_placement = True
config.gpu_options.allocator_type = 'BFC'
# Limit the maximum memory used
config.gpu_... |
5,105 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hello World! Python Workshops @ Think Coffee
3-5pm, 7/30/17
Day 3, Alice NLP generator
@python script author (original content)
Step1: Setting params for model setup and build.
Step2: Load... | Python Code:
from __future__ import print_function
from keras.models import Model
from keras.layers import Dense, Activation, Embedding
from keras.layers import LSTM, Input
from keras.layers.merge import concatenate
from keras.optimizers import RMSprop, Adam
from keras.utils.data_utils import get_file
from keras.layers... |
5,106 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial 4
Step1: Plotting the results | Python Code:
import logging
import sys
import espressomd
import espressomd.accumulators
import espressomd.lb
import espressomd.observables
logging.basicConfig(level=logging.INFO, stream=sys.stdout)
# Constants
LOOPS = 40000
STEPS = 10
# System setup
system = espressomd.System(box_l=[16] * 3)
system.time_step = 0.01
sys... |
5,107 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pagination and encapsulation
Step1: Path parameters
OAS3 allows to specify path parameters
Step2: Exercise
Step3: Exercise
Step4: Now run the spec in a terminal using
cd /code/notebooks... | Python Code:
# Use this cell to test the output
!curl http://localhost:5000/datetime/v1/timezones -vk
Explanation: Pagination and encapsulation:
In our standardization policy, we defined a common set of pagination parameters.
Moreover we stated that responses should always be enclosed in json objects, eg:
always return... |
5,108 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step5: Project on frame buckling
Define a new class Frame_Buckling as a child of the class LinearFrame provided in frame.py. Add in this class methods to extract the normal stress $\bar N_0$... | Python Code:
%matplotlib inline
from sympy.interactive import printing
printing.init_printing()
from frame import *
import sympy as sp
import numpy as np
import scipy.sparse as sparse
import scipy.sparse.linalg as linalg
class Frame_Buckling(LinearFrame):
def N_local_stress(self,element):
Returns... |
5,109 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Teorema de Norton
Jupyter Notebook desenvolvido por Gustavo S.S.
O teorema de Norton afirma que um circuito linear de dois terminais pode
ser substituído por um circuito equivalente formado ... | Python Code:
print("Exemplo 4.11")
#Superposicao
#Analise Fonte de Tensao
#Req1 = 4 + 8 + 8 = 20
#i1 = 12/20 = 3/5 A
#Analise Fonte de Corrente
#i2 = 2*4/(4 + 8 + 8) = 8/20 = 2/5 A
#in = i1 + i2 = 1A
In = 1
#Req2 = paralelo entre Req 1 e 5
#20*5/(20 + 5) = 100/25 = 4
Rn = 4
print("Corrente In:",In,"A")
print("Resistênc... |
5,110 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lyzenga Method
I want to apply the Lyzenga 2006 method for comparison.
Step1: Preprocessing
That happened here.
Step2: Depth Limit
Lyzenga et al methods for determining shallow water don't... | Python Code:
%pylab inline
import geopandas as gpd
import pandas as pd
from OpticalRS import *
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.cross_validation import train_test_split
import itertools
import statsmodels.formula.api as smf
from collections im... |
5,111 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exploring netCDF Datasets Using the xarray Package
This notebook provides discussion, examples, and best practices for
working with netCDF datasets in Python using the xarray package.
Topics... | Python Code:
import numpy as np
import xarray as xr
Explanation: Exploring netCDF Datasets Using the xarray Package
This notebook provides discussion, examples, and best practices for
working with netCDF datasets in Python using the xarray package.
Topics include:
The xarray package
Reading netCDF datasets into Python ... |
5,112 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The Sequential model is a linear stack of layers.
You can create a Sequential model by passing a list of layer instances to the constructor
Step1: You can also simply add layers via the .ad... | Python Code:
from keras.models import Sequential
from keras.layers import Dense, Activation
model = Sequential([
Dense(32, input_shape=(784,)),
Activation('relu'),
Dense(10),
Activation('softmax'),
])
Explanation: The Sequential model is a linear stack of layers.
You can create a Sequential model by pas... |
5,113 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Getting started with mne.Report
This tutorial covers making interactive HTML summaries with
Step1: Before getting started with
Step2: This report yields a textual summary of the
Step3: ... | Python Code:
import os
import mne
Explanation: Getting started with mne.Report
This tutorial covers making interactive HTML summaries with
:class:mne.Report.
:depth: 2
As usual we'll start by importing the modules we need and loading some
example data <sample-dataset>:
End of explanation
path = mne.datasets.sa... |
5,114 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
LAB 01
Step1: The source dataset
Our dataset is hosted in BigQuery. The taxi fare data is a publically available dataset, meaning anyone with a GCP account has access. Click here to acess t... | Python Code:
%%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
Explanation: LAB 01: Basic Feature Engineering in BQML
Learning Objectives
Create SQL statements to evaluate the model
Extract temporal features
Perform a feature cross on t... |
5,115 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Please find torch implementation of this notebook here
Step2: Implementation
Utility functions.
Step6: Main function.
Step7: Example
The shape of the multi-head attention output is (batch... | Python Code:
import jax
import jax.numpy as jnp # JAX NumPy
from jax import lax
import math
from IPython import display
try:
from flax import linen as nn # The Linen API
except ModuleNotFoundError:
%pip install -qq flax
from flax import linen as nn # The Linen API
from flax.training import train_state #... |
5,116 | Given the following text description, write Python code to implement the functionality described.
Description:
Count pairs of nodes having minimum distance between them equal to the difference of their distances from root
Stores the count of pairs ; Store the adjacency list of the connecting vertex ; Function for theto... | Python Code:
ans = 0
adj =[[ ] for i in range(10 ** 5 + 1 ) ]
def dfsUtil(u , par , depth ) :
global adj , ans
for it in adj[u ] :
if(it != par ) :
dfsUtil(it , u , depth + 1 )
ans += depth
def dfs(u , par , depth ) :
global ans
dfsUtil(u , par , depth )
print(ans )
def countPairs(ed... |
5,117 | 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... |
5,118 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
django-postgre-copy speed tests
By Ben Welsh
This notebook tests the effect of dropping database constraints and indexes prior to loading a large data file.
The official PostgreSQL documenta... | Python Code:
import os
import sys
import csv
import calculate
import scipy as sp
import pandas as pd
import scipy.optimize
from pprint import pprint
import matplotlib as mpl
from matplotlib import rcParams
rcParams['font.family'] = 'monospace'
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import mat... |
5,119 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
San Diego Burrito Analytics
Step1: Load data
Step2: Brief metadata
Step3: What types of burritos have been rated?
Step4: Progress in number of burritos rated
Step5: Burrito dimension di... | Python Code:
%config InlineBackend.figure_format = 'retina'
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set_style("white")
Explanation: San Diego Burrito Analytics: Data characterization
Scott Cole
21 May 2016
This notebook chara... |
5,120 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step2: PYT-DS
Step3: This is not quite how we want to see the data. We need to flip it, or swap axes. Transpose will do. Then lets change the column names. Finally, we'll sort. | Python Code:
import pandas as pd
import numpy as np
Created on Thu Jun 29 10:17:02 2017
Rewritten for get_data package on Oct 25, 2017
@author: Kirby Urner
Decorated generator used IN PLACE OF:
class Url:
def __init__(self, the_url):
self.url = the_url
def __enter__(self):
sel... |
5,121 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: <H1>Solving 1st order ODEs</H1>
The logistic equation is a first-order non-linear differential equation that describes the evolution of a population as a function of the population si... | Python Code:
def diff(p, generation):
Returns the as size of the population as a function of the generation
defined in the following differential equation:
dp/dg = p*(k-p)/tau,
where p is the population size, g is the generation index, k is
the maximal population size (fixed to 1000)... |
5,122 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Feature Engineering
|Session | Session |
|-----------|---------|
|Feature Engineering I | Feature Transformation and Dimension Reduction (PCA)|
|Feature Engineering II | Nonlinear Dim... | Python Code:
from IPython.display import Image
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import time
%matplotlib inline
Explanation: Feature Engineering
|Session | Session |
|-----------|---------|
|Feature Engineering I | Feature Transformation and Dimension Reduction (PCA)|
|Feature E... |
5,123 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cahn-Hilliard Example
This example demonstrates how to use PyMKS to solve the Cahn-Hilliard equation. The first section provides some background information about the Cahn-Hilliard equation ... | Python Code:
%matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
Explanation: Cahn-Hilliard Example
This example demonstrates how to use PyMKS to solve the Cahn-Hilliard equation. The first section provides some background information about the Cahn-Hilliard equation... |
5,124 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Land
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify do... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'sandbox-2', 'land')
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: MIROC
Source ID: SANDBOX-2
Topic: Land
Sub-Topics: Soil, Snow, Vegetation, Ene... |
5,125 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Display Exercise 1
Imports
Put any needed imports needed to display rich output the following cell
Step1: Basic rich display
Find a Physics related image on the internet and display it in t... | Python Code:
from IPython.display import HTML
from IPython.display import Image
from IPython.display import IFrame
assert True # leave this to grade the import statements
Explanation: Display Exercise 1
Imports
Put any needed imports needed to display rich output the following cell:
End of explanation
Image(url = "http... |
5,126 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1 align = 'center'> Neural Networks Demystified </h1>
<h2 align = 'center'> Part 7
Step1: Last time, we trained our Neural Network, and it made suspiciously good predictions of your test ... | Python Code:
from IPython.display import YouTubeVideo
YouTubeVideo('S4ZUwgesjS8')
Explanation: <h1 align = 'center'> Neural Networks Demystified </h1>
<h2 align = 'center'> Part 7: Overfitting, Testing, and Regularization </h2>
<h4 align = 'center' > @stephencwelch </h4>
End of explanation
%pylab inline
from partSix im... |
5,127 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Я запустил алгоритм на случайных тестах, размера начиная с 2 и он обсчитывает по 10 тестов одного размера.
Я хочу попробовать проанализировать данные, которые получу в результате его работы.... | Python Code:
class Node:
def __init__(self, number, cost, time, answer):
self.number = int(number)
self.cost = float(cost)
self.time = float(time) / 10**9
self.size = self.number / 100
self.answer = answer
def write(self):
print("n = ", self.number," \n")
... |
5,128 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h2>Assignment 1 – Problem 2
Step1: <h3>Michell Structure Geometry</h3>
I approach generating the Michell structure by repeating a series of steps <b><i>up</i></b> and <b><i>across</i></b>.... | Python Code:
import numpy as np
from scipy import integrate
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.colors import LinearSegmentedColormap
from scipy.optimize import fsolve
import scipy.spatial.distance as dist
import math
from frame3dd import Frame, NodeData, Reacti... |
5,129 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sebastian Raschka, 2015
Python Machine Learning Essentials
Chapter 11 - Working with Unlabeled Data – Clustering Analysis
Note that the optional watermark extension is a small IPython notebo... | Python Code:
%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scipy,scikit-learn
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
Explanation: Sebastian Raschka, 2015
Python Machine Lear... |
5,130 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Author
Step1: First let's check if there are new or deleted files (only matching by file names).
Step2: So we have the same set of files in both versions
Step3: Let's make sure the struct... | Python Code:
import collections
import glob
import os
from os import path
import matplotlib_venn
import pandas as pd
rome_path = path.join(os.getenv('DATA_FOLDER'), 'rome/csv')
OLD_VERSION = '337'
NEW_VERSION = '338'
old_version_files = frozenset(glob.glob(rome_path + '/*{}*'.format(OLD_VERSION)))
new_version_files = f... |
5,131 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Chapter 10 - Dictionaries
This notebook uses code snippets and explanation from this course
The last type of container we will introduce in this topic is dictionaries.... | Python Code:
%%capture
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Data.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/images.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Extra_Material.zip
!unzip Data.zip -d ../
!unzip images.zip -d .... |
5,132 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
First, we need to set up our test data. We'll use two relaxation modes that are themselves log-normally distributed.
Step1: Now, let's construct the moduli. We'll have both a true version a... | Python Code:
def H(tau):
g1 = 1; tau1 = 0.03; sd1 = 0.5;
g2 = 7; tau2 = 10; sd2 = 0.5;
term1 = g1/np.sqrt(2*sd1**2*np.pi) * np.exp(-(np.log10(tau/tau1)**2)/(2*sd1**2))
term2 = g2/np.sqrt(2*sd2**2*np.pi) * np.exp(-(np.log10(tau/tau2)**2)/(2*sd2**2))
return term1 + term2
Nfreq = 50
Nmodes = 30
w = np.... |
5,133 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Notebook Title <a class="tocSkip">
Make some notes here for the thought process, steps taken, TODOs.
Imports
Import deps
Map all dependencies under categories, for easier tracking / readabil... | Python Code:
# BASE ------------------------------------
from datetime import datetime as dt
nb_start = dt.now()
# Be mindful when you have this activated.
# import warnings
# warnings.filterwarnings('ignore')
import json
from pathlib import Path
from time import sleep
# Display libs
from IPython.display import display... |
5,134 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plot RBM maps
to start, just plot the RBM maps, shows what is going on here
Step1: Calculate On Counts and Acceptance
This is done by reading in the on counts and integral acceptance maps a... | Python Code:
if plot:
fig = plt.figure(figsize=(12, 12))
fig1 = aplpy.FITSFigure(fitsF, figure=fig, subplot=(2,3,1), hdu=5)
fig1.show_colorscale()
standard_setup(fig1)
fig1.set_title("Acceptance")
fig1 = aplpy.FITSFigure(fitsF, figure=fig, subplot=(2,3,2), hdu=7)
fig1.show_colorscale()
... |
5,135 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Executed
Step1: Load software and filenames definitions
Step2: Data folder
Step3: List of data files
Step4: Data load
Initial loading of the data
Step5: Load the leakage coefficient fro... | Python Code:
ph_sel_name = "None"
data_id = "17d"
# data_id = "7d"
Explanation: Executed: Mon Mar 27 11:38:52 2017
Duration: 7 seconds.
usALEX-5samples - Template
This notebook is executed through 8-spots paper analysis.
For a direct execution, uncomment the cell below.
End of explanation
from fretbursts import *
init_... |
5,136 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cyclical figurate numbers
Problem 61
Triangle, square, pentagonal, hexagonal, heptagonal, and octagonal numbers are all figurate (polygonal) numbers and are generated by the following formul... | Python Code:
from euler import timer, Seq, fst, snd
def p061():
values = ([lambda n: n*(n+1)/2, lambda n: n*n, lambda n: n*(3*n-1)/2,
lambda n: n*(2*n-1), lambda n: n*(5*n-3)/2, lambda n: n*(3*n-2)]
>> Seq.mapi(lambda n: n)
>> Seq.collect(lambda (i,fun):
... |
5,137 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MongoBase starting guide
Step1: ObjectId
First, let's talk about ObjectId.
Step2: Actually, ObjectId is usuful. It is unique, sortable and memory efficient.
http
Step3: The __structure__ ... | Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
import sys
import time
import threading
import multiprocessing
import datetime as dt
from mongobase.mongobase import MongoBase, db_context
from bson import ObjectId
Explanation: MongoBase starting guide
End of explanation
x = ObjectId()
time.sleep(1)
y ... |
5,138 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Vertex client library
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Restart the kernel
Once you've installed the Vertex client library and Google clo... | Python Code:
import os
import sys
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install -U google-cloud-aiplatform $USER_FLAG
Explanation: Vertex client library: AutoML image classification model for export to edge
<table al... |
5,139 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
In this notebook I'll show probabilistic interpretation of the nearest neighbours algorith as a mixture of Gaussians. Following Barber 2012. First I'll give an example of ... Next, I'll show... | Python Code:
import scipy.io as sio
nndata = sio.loadmat('/Users/gm/Downloads/BRMLtoolkit/data/NNdata.mat')
nndata
Explanation: In this notebook I'll show probabilistic interpretation of the nearest neighbours algorith as a mixture of Gaussians. Following Barber 2012. First I'll give an example of ... Next, I'll show h... |
5,140 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SPDX-FileCopyrightText
Step1: Initialize wind farm
To initialize a specific wind farm you need to provide a wind turbine fleet specifying the wind turbines and their number or total install... | Python Code:
import pandas as pd
import modelchain_example as mc_e
from windpowerlib import TurbineClusterModelChain, WindTurbineCluster, WindFarm
import logging
logging.getLogger().setLevel(logging.DEBUG)
# Get weather data
weather = mc_e.get_weather_data('weather.csv')
print(weather[['wind_speed', 'temperature', 'pre... |
5,141 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compare speed python list vs numpy ops
Let's implement standard deviation function. And compare computation time for 10 million numbers.
Step1: As we can see numpy function much fater than ... | Python Code:
n = 10 ** 7
# Implementation using python list
def std(x:list):
x_mean = sum(x)/len(x)
y = sum([(v - x_mean) ** 2 for v in x])/len(x)
return y**0.5
%time std(range(n))
# Implementation using numpy array function
def std_np(x):
x_mean = np.sum(x)/len(x)
return (((x - x_mean) ** 2).mean()... |
5,142 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Download lists of experiments by injection hemisphere, mouse cre line, and injection structure
Download Pvalb experiments injected in the left hemisphere VISp
Step1: Download grid data for ... | Python Code:
# Get the atlas id
def query_atlases(search_pattern):
return rma.build_query_url(rma.model_stage('Atlas',
criteria="[name$il'%s']" % (search_pattern),
only=['id', 'name']))
atlases = o.do_query(query_atlases, ... |
5,143 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The idea
In my previous blog post, we got to know the idea of "indentation-based complexity". We took a static view on the Linux kernel to spot the most complex areas.
This time, we wanna tr... | Python Code:
import pandas as pd
diff_raw = pd.read_csv(
"../../buschmais-spring-petclinic_fork/git_diff.log",
sep="\n",
names=["raw"])
diff_raw.head(16)
Explanation: The idea
In my previous blog post, we got to know the idea of "indentation-based complexity". We took a static view on the Linux kernel to sp... |
5,144 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Boundary conditions
This example shows solutions to time-dependent diffusion equations formulated with different boundary conditions. Isolated, Dirichlet (prescribed value) and periodic boun... | Python Code:
%matplotlib inline
import matplotlib.pylab as plt
from oedes import *
init_notebook()
from matplotlib import ticker
Explanation: Boundary conditions
This example shows solutions to time-dependent diffusion equations formulated with different boundary conditions. Isolated, Dirichlet (prescribed value) and p... |
5,145 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
DFF and TFF (Toggle Flip-Flop)
In this example we create a toggle flip-flop (TFF) from a d-flip-flop (DFF). In Magma, finite state machines can be constructed by composing combinational logi... | Python Code:
import magma as m
from mantle import DFF
class TFF(m.Circuit):
io = m.IO(O=m.Out(m.Bit)) + m.ClockIO()
# instance a dff to hold the state of the toggle flip-flop - this needs to be done first
dff = DFF()
# compute the next state as the not of the old state ff.O
io.O <= dff(~dff.O)
... |
5,146 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
NBA Player Statistics Workshop
Given a dataset of NBA players performance and salary in 2014, use Python to load the dataset and compute the summary statistics for the SALARY field
Step2: F... | Python Code:
# Imports - you'll need some of these later, but it's traditional to put them all at the beginning.
import os
import csv
import json
import urllib2
from collections import Counter
from operator import itemgetter
Explanation: NBA Player Statistics Workshop
Given a dataset of NBA players performance and sala... |
5,147 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
AI Explanations
Step1: Region
You can also change the REGION variable, which is used for operations
throughout the rest of this notebook. Make sure to choose a region where Cloud
AI Platfor... | Python Code:
PROJECT_ID = "[your-project-id]" #@param {type:"string"}
if PROJECT_ID == "" or PROJECT_ID is None or PROJECT_ID == "[your-project-id]":
# Get your GCP project id from gcloud
shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null
PROJECT_ID = shell_output[0]
print("Pr... |
5,148 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
apsis on the BRML cluster
Generally, apsis consists of a server, whose task it is to generate new candidates and receive updates, and several worker processes, who evaluate the actual machin... | Python Code:
from apsis_client.apsis_connection import Connection
conn = Connection(server_address="http://localhost:5000")
Explanation: apsis on the BRML cluster
Generally, apsis consists of a server, whose task it is to generate new candidates and receive updates, and several worker processes, who evaluate the actual... |
5,149 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Multivariate Regression
Let's grab a small little data set of Blue Book car values
Step1: We can use pandas to split up this matrix into the feature vectors we're interested in, and the val... | Python Code:
import pandas as pd
df = pd.read_excel('http://cdn.sundog-soft.com/Udemy/DataScience/cars.xls')
df.head()
Explanation: Multivariate Regression
Let's grab a small little data set of Blue Book car values:
End of explanation
import statsmodels.api as sm
df['Model_ord'] = pd.Categorical(df.Model).codes
X = df[... |
5,150 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Learning with TensorFlow
Credits
Step1: First reload the data we generated in notmist.ipynb.
Step2: Reformat into a shape that's more adapted to the models we're going to train
Step3:... | Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
import cPickle as pickle
import numpy as np
import tensorflow as tf
Explanation: Deep Learning with TensorFlow
Credits: Forked from TensorFlow by Google
Setup
Refer to the setup instructions.
Exerci... |
5,151 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Latent Dirichlet Allocation for Text Data
In this assignment you will
apply standard preprocessing techniques on Wikipedia text data
use GraphLab Create to fit a Latent Dirichlet allocation ... | Python Code:
import graphlab as gl
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
'''Check GraphLab Create version'''
from distutils.version import StrictVersion
assert (StrictVersion(gl.version) >= StrictVersion('1.8.5')), 'GraphLab Create must be version 1.8.5 or later.'
# import wiki data
wik... |
5,152 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Stage 1
Step1: In the code bellow, resize image into the special resolution
Step2: 1.1 Create a standard training dataset
Step4: Generate tfrecords
Step5: Read a batch images
Step6: Exa... | Python Code:
%matplotlib inline
import glob
import os
import numpy as np
from scipy.misc import imread, imresize
import matplotlib.pyplot as plt
import tensorflow as tf
raw_image = imread('model/datasets/nudity_dataset/3.jpg')
# Define a tensor placeholder to store an image
image = tf.placeholder("uint8", [None, None, ... |
5,153 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step7: Arbres binaires
Le but de ce TP est d'implanter les fonctions usuelles telles que la génération exhaustive (fabriquer tous les éléments de l'ensemble), rank et unrank sur l'ensemble d... | Python Code:
class BinaryTree():
def __init__(self, children = None):
A binary tree is either a leaf or a node with two subtrees.
INPUT:
- children, either None (for a leaf), or a list of size excatly 2
of either two binary trees or 2 obje... |
5,154 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hello world!
The beginning of almost everything in computer programming
Step1: <a id='Jupyter'></a>
2. Interacting with Jupyter Notebook
This interface (what you are reading now) is know a... | Python Code:
!python -c "print('Hello world!')"
Explanation: Hello world!
The beginning of almost everything in computer programming :-)
Let's see different alternatives to run Python code.
Contents
"Batch" running of single commands.
Interacting with Jupyter Notebooks.
Interacting with Python interpreters.
"Batch" run... |
5,155 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1> Creating a custom Word2Vec embedding on your data </h1>
This notebook illustrates
Step2: Creating a training dataset
The training dataset simply consists of a bunch of words separated ... | Python Code:
# change these to try this notebook out
BUCKET = 'alexhanna-dev-ml'
PROJECT = 'alexhanna-dev'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
Explanation: <h1> Creating a custom Word2Vec embedding on your data </h1>
This notebook ... |
5,156 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
BEM++ overview
https
Step1: Q
Step2: What mean these arguments?
The first argument is always grid object
The second argument can be discontinious polynomial ("DP"), polynomial ("P") or som... | Python Code:
import bempp.api
import numpy as np
grid = bempp.api.shapes.regular_sphere(3)
grid.plot()
Explanation: BEM++ overview
https://bempp.com/
Overview
Overview presentation here.
Applicable only for 3D problems
Support Laplace, Helmholtz and Maxwell equations with Dirichlet and Neumann boundary conditions
Suppo... |
5,157 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
DV360 Automation
Step1: 0.2 Setup your GCP project
To utilise the DV360 API, you need a Google Cloud project. For the purpose of this workshop, we've done this for you, but normally you'd h... | Python Code:
!pip install google-api-python-client
!pip install google-cloud-vision
import csv
import datetime
import io
import json
import pprint
from google.api_core import retry
from google.cloud import vision
from google.colab import files
from google_auth_oauthlib.flow import InstalledAppFlow
from googleapiclient ... |
5,158 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sprachenvielfalt
Figures for https
Step1: Sprachenvielfalt
http
Step2: Prozentualer Anteil der Bevölkerung
Step3: Analphabetismus in Prozent
Step4: Karte Analphabetismus
Step5: Karte in... | Python Code:
import numpy as np
import travelmaps2 as tm
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib import cm, colors, rcParams
plt.style.use('ggplot')
# Adjust dpi, so figure on screen and savefig looks the same
dpi = 100
rcParams['figure.dpi'] = dpi
rcParams['savefig.dpi'... |
5,159 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Learning with MNIST
This is the mnist_mlp code with all the blanks
filled in for you. The original Keras example
is here
Step1: Each of our 60,000 handwritten digits comes prepackaged... | Python Code:
from __future__ import print_function
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD, Adam, RMSprop
from keras.utils import np_utils... |
5,160 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: 图像分类
在此项目中，你将对 CIFAR-10 数据集 中的图片进行分类。该数据集包含飞机、猫狗和其他物体。你需要预处理这些图片，然后用所有样本训练一个卷积神经网络。图片需要标准化（normalized），标签需要采用 one-hot 编码。你需要应用所学的知识构建卷积的、最大池化（max pooling）、丢弃（dropout）和完全连接（fully conne... | Python Code:
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
# Use Floyd's cifar-10 dataset if present
floyd_cifa... |
5,161 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sistema Nacional de Información e Indicadores de Vivienda
ID |Descripción
---|
Step1: 2. Descarga de datos
Los datos se descargan por medio de una conexión a un servicio SOAP proporcionado ... | Python Code:
descripciones = {
'P0405': 'Viviendas Verticales',
'P0406': 'Viviendas urbanas en PCU U1 y U2',
'P0411': 'Subsidios CONAVI'
}
# Librerias utilizadas
import pandas as pd
import sys
import urllib
import os
import csv
import zeep
import requests
from lxml import etree
import xmltodict
import ast
i... |
5,162 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture 2 continued
regular expressions
Provides a way to search text.
Looking for matching patterns
Step3: Object-oriented programming
Creating classes of objects with data and methods(fun... | Python Code:
import re
print all([
not re.match("a","cat"),
re.search("a","cat"),
not re.search("c","dog"),
3 == len(re.split("[ab]","carbs")),
"R-D-" == re.sub("[0-9]","-","R2D2")
]) # prints true if all are true
Explanation: Lecture 2 continued
regular expressions
Provides a way to search text.
L... |
5,163 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I have my data in a pandas DataFrame, and it looks like the following: | Problem:
import pandas as pd
df = pd.DataFrame({'cat': ['A', 'B', 'C'],
'val1': [7, 10, 5],
'val2': [10, 2, 15],
'val3': [0, 1, 6],
'val4': [19, 14, 16]})
def g(df):
df = df.set_index('cat')
res = df.div(df.sum(axis=1), axis=0)
retu... |
5,164 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I have dfs as follows: | Problem:
import pandas as pd
df1 = pd.DataFrame({'id': [1, 2, 3, 4, 5],
'city': ['bj', 'bj', 'sh', 'sh', 'sh'],
'district': ['ft', 'ft', 'hp', 'hp', 'hp'],
'date': ['2019/1/1', '2019/1/1', '2019/1/1', '2019/1/1', '2019/1/1'],
'value': [1, 5, 9,... |
5,165 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Logistic Regression
In this note, I am going to train a logistic regression model with gradient decent estimation.
Logistic regression model can be thought as a neural network without hidden... | Python Code:
import numpy as np # Matrix and vector computation package
np.seterr(all='ignore') # ignore numpy warning like multiplication of inf
import matplotlib.pyplot as plt # Plotting library
from matplotlib.colors import colorConverter, ListedColormap # some plotting functions
from matplotlib import cm # Colorma... |
5,166 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Homework 6
Step1: Problem set #2
Step2: Problem set #3
Step3: Problem set #4
Step4: Problem set #5
Step5: Specifying a field other than name, area or elevation for the sort parameter sh... | Python Code:
import requests
data = requests.get('http://localhost:5000/lakes').json()
print(len(data), "lakes")
for item in data[:10]:
print(item['name'], "- elevation:", item['elevation'], "m / area:", item['area'], "km^2 / type:", item['type'])
Explanation: Homework 6: Web Applications
For this homework, you're ... |
5,167 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 Google LLC
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 Licens... | Python Code:
import pandas as pd
import tensorflow as tf
import numpy as np
pd.set_option('expand_frame_repr', True)
pd.set_option("display.max_rows", 100)
pd.set_option('max_colwidth',90)
Explanation: Copyright 2021 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file ex... |
5,168 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Graph Gmail inbox data with IPython notebook
Step1: Download your Gmail inbox as a ".mbox" file by clicking on "Account" under your Gmail user menu, then "Download data"
Install the Python ... | Python Code:
from IPython.display import Image
Image('http://i.imgur.com/SYija2N.png')
Explanation: Graph Gmail inbox data with IPython notebook
End of explanation
import mailbox
from email.utils import parsedate
from dateutil.parser import parse
import itertools
import plotly.plotly as py
from plotly.graph_objs import... |
5,169 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Zillow’s Home Value Prediction (Zestimate)
Zillow's Zestimate is created to give consumers as much information as possible about homes and the housing market, marking consumers had access to... | Python Code:
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import missingno as msno
import xgboost as xgb
from sklearn.preprocessing import scale
from xgboost.sklearn import XGBRegressor
from sklearn.linear_model import Ridge
from sklearn import cross_validation, metrics #A... |
5,170 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
License
Copyright 2017 J. Patrick Hall, jphall@gwu.edu
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "... | Python Code:
# imports
import h2o
import numpy as np
import pandas as pd
from h2o.estimators.gbm import H2OGradientBoostingEstimator
# start h2o
h2o.init()
h2o.remove_all()
Explanation: License
Copyright 2017 J. Patrick Hall, jphall@gwu.edu
Permission is hereby granted, free of charge, to any person obtaining a copy o... |
5,171 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Inverted encoding model
Step1: In this example, we will assume that the stimuli are patches of different motion directions. These stimuli span a 360-degree, circular feature space. We will ... | Python Code:
import numpy as np
from brainiak.reconstruct import iem as IEM
import matplotlib.pyplot as plt
import numpy.matlib as matlib
import scipy.signal
Explanation: Inverted encoding model
End of explanation
# Set up parameters
n_channels = 6
cos_exponent = 5
range_start = 0
range_stop = 360
feature_resolution = ... |
5,172 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Week 1
Step1: Make a vector with 6 elements
Step2: Get some information about the vector
Step3: Create a matrix like this
Step4: Get some information about the matrix
Step5: A very powe... | Python Code:
import numpy as np
Explanation: Week 1: Getting Started with Jupyter Notebooks
In this notebook, we will make sure all the packages required for this course are properly installed and working.
To use this notebook, select the input cells (shown as In [x]) in order and press Shift-Enter to execute the code... |
5,173 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Content and Objectives
Show effects of multipath on a pulse and on a pulse-shaped data signal for random data
Import
Step1: Function for determining the impulse response of an RC filter
Ste... | Python Code:
# importing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(18, 10) )
Explanation: Content and Objectives
Show effe... |
5,174 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Testing Nsaba Functionality
Current Methods
get_aba_ge()
ge_ratio()
get_ns_act()
make_ge_ns_mat()
coords_to_ge()
Step1: Coordinates to gene expression
Step2: Visualization Methods (testing... | Python Code:
%matplotlib inline
from nsaba.nsaba import Nsaba
from nsaba.nsaba.visualizer import NsabaVisualizer
import numpy as np
import os
import matplotlib.pyplot as plt
import pandas as pd
import itertools
%load_ext line_profiler
# Simon Path IO
data_dir = '../../data_dir'
os.chdir(data_dir)
Nsaba.aba_load()
Nsaba... |
5,175 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
xlsxWriter
tutorial
install
sudo pip install XlsxWriter
아나콘다 설치시 기본적으로 내장되어 있음
Step1: tutorial 1
Step2: <img src="https
Step3: Tutorial 2
Step4: Tutorial 3
Step5: write() method
여기에 더 ... | Python Code:
import xlsxwriter
workbook = xlsxwriter.Workbook('hello.xlsx')
worksheet = workbook.add_worksheet()
worksheet.write('A1', 'Hello world')
workbook.close()
Explanation: xlsxWriter
tutorial
install
sudo pip install XlsxWriter
아나콘다 설치시 기본적으로 내장되어 있음
End of explanation
expenses = (
['Rent', 1000],
['Gas... |
5,176 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Analysis of Seattle Fremont Bridge Bike Traffic
Step1: Get Data
Step2: Shows a graph of data on a weekly basis. Let's investigate what the pattern is when we look at hourly rates on indivi... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt;
from jubiiworkflow.data import get_data
import pandas as pd
import numpy as np
from sklearn.decomposition import PCA
from sklearn.mixture import GaussianMixture
plt.style.use('seaborn');
Explanation: Analysis of Seattle Fremont Bridge Bike Traffic
End of ... |
5,177 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python Tuples Reference
Table of contents
<a href="#1.-Creation">Creation</a>
<a href="#2.-Basic-Operations">Basic Operations</a>
<a href="#3.-Unpacking">Unpacking</a>
<a href="#4.-Comparing... | Python Code:
# Create a tuple directly
digits = (0, 1, 'two')
digits
# Create a tuple from a list
digits = tuple([0, 1, 'two'])
digits
# For a single item tuple, a trailing comma is required to tell the intepreter its a tuple
zero = (0,)
zero
Explanation: Python Tuples Reference
Table of contents
<a href="#1.-Creation"... |
5,178 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Homework 4
Step1: Now, let's connect to your Postgres database. On your Heroku Postgres details,
look at the credentials for the database. Take the long URI in the credentials and
replace t... | Python Code:
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import sqlalchemy
!pip install -U okpy
from client.api.notebook import Notebook
ok = Notebook('hw4.ok')
Explanation: Homework 4: SQL, FEC Data, and Small Donors
Due: 11:59pm Tuesday, March 14
Not... |
5,179 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example plot for LFPy
Step1: Fetch Mainen&Sejnowski 1996 model files
Step2: Main script, set parameters and create cell, synapse and electrode objects
Step3: Plot simulation output | Python Code:
import LFPy
import numpy as np
import os
import sys
from urllib.request import urlopen
import ssl
import zipfile
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
from os.path import join
Explanation: Example plot for LFPy: Single-synapse contribution to the LFP
Copyright (C... |
5,180 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook provides an interactive overview to some if the ideas developed in a paper entitled "Error Statistics", by Mayo and Spanos.
Goals
For a sample of data
Step1: All hypotheses di... | Python Code:
from scipy.stats import norm # properties of the distribution
from numpy.random import normal # samples from the distribution
import numpy as np
import scipy
from matplotlib import pyplot as plt
%matplotlib inline
Explanation: This notebook provides an interactive overview to some if the ideas developed in... |
5,181 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Script to copy files to Anaconda paths so can import and use scripts
Step1: find main path for install
Step2: make list of paths need to copy files to & add main path
Step3: add paths to ... | Python Code:
module_name = 'bradlib'
Explanation: Script to copy files to Anaconda paths so can import and use scripts
End of explanation
from distutils.sysconfig import get_python_lib #; print(get_python_lib())
path_main = get_python_lib()
path_main
path_main.split('Anaconda3')
Explanation: find main path for install
... |
5,182 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
scikit-learn-linear-reg
Credits
Step1: Linear Regression
Linear Regression is a supervised learning algorithm that models the relationship between a scalar dependent variable y and one or m... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import seaborn;
from sklearn.linear_model import LinearRegression
import pylab as pl
seaborn.set()
Explanation: scikit-learn-linear-reg
Credits: Forked from PyCon 2015 Scikit-learn Tutorial by Jake VanderPlas
Linear Regression
End of ex... |
5,183 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
Given the following example: | Problem:
import numpy as np
from sklearn.feature_selection import SelectKBest
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
import pandas as pd
data, target = load_data()
pipe = Pipeline(steps=[
('select', SelectKBest(k=2)),
('clf', LogisticRegression())]
)
select_out... |
5,184 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced Functions Test Solutions
For this test, you should use the built-in functions to be able to write the requested functions in one line.
Problem 1
Use map to create a function which f... | Python Code:
def word_lengths(phrase):
return list(map(len, phrase.split()))
word_lengths('How long are the words in this phrase')
Explanation: Advanced Functions Test Solutions
For this test, you should use the built-in functions to be able to write the requested functions in one line.
Problem 1
Use map to cr... |
5,185 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 11. Null Hypothesis Significance Testing
Exercise 11.1
Exercise 11.2
Exercise 11.3
Exercise 11.1
Purpose
Step1: Part B
fixed z
Step2: Exercise 11.2
Purpose
Step3: Part A
fixed N
S... | Python Code:
import numpy as np
from scipy.misc import factorial
N = 45
z = 3
theta = 1/6
def binomial(theta, N, z):
coef = factorial(N) / factorial(N-z) / factorial(z)
p = coef * theta**z * (1 - theta)**(N-z)
return p
tail = np.arange(z+1)
tail
p = binomial(theta, N, tail).sum() * 2 # left and right tail p... |
5,186 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
https
Step1: https
Step2: Symmetric Difference
https | Python Code:
# Это единственный комментарий который имеет смысл
# I s
def find_index(m,a):
try:
return a.index(m)
except :
return -1
def find_two_sum(a, s):
'''
>>> (3, 5) == find_two_sum([1, 3, 5, 7, 9], 12)
True
'''
if len(a)<2:
return (-1,-1)
idx = d... |
5,187 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PROV Templates in Python
Author
Step1: Generate an example prov template and print (and store) it's provn representation
Step2: Generate an example binding from a python dictionaries
(one... | Python Code:
# Define namespaces used and generate a new empty
# python prov template instance
# Define the variable settings in the template as a dictionary
from provtemplates import provconv
import prov.model as prov
import six
import itertools
ns_dict = {
'prov':'http://www.w3.org/ns/prov#',
'var':'http://o... |
5,188 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: 检查 TensorFlow 图
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: 定义一个 Keras 模型
在此示例中，分类器是一个简... | 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... |
5,189 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Non-parametric 1 sample cluster statistic on single trial power
This script shows how to estimate significant clusters
in time-frequency power estimates. It uses a non-parametric
statistical... | Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequency import tfr_morlet
from mne.stats import permutation_cluster_1samp_test
from mne.datasets import sample
print(__doc__)
Exp... |
5,190 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Inital setup
Step1: loading summary file that I previously created by scanning through L1A darks.
Step2: These are the columns I have available in this dataset
Step3: Correct mean DN for ... | Python Code:
%matplotlib inline
plt.rcParams['figure.figsize'] = (10,10)
from matplotlib.pyplot import subplots
Explanation: Inital setup
End of explanation
import pandas as pd
df = pd.read_hdf('/home/klay6683/to_keep/l1a_dark_stats.h5','df')
df.DET_TEMP.plot(
Explanation: loading summary file that I previously created... |
5,191 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Objective
Overview of ML Model Build Process
Logistic Regression Introduction
Model Evaluations
Step1: Model Building Process
Step2: Dataset
Step3: Logistic Regression - Model
Take a weig... | Python Code:
from __future__ import print_function # Python 2/3 compatibility
from IPython.display import Image
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Objective
Overview of ML Model Build Process
Logistic Regression Introduction
Model Evaluations
End of e... |
5,192 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Training Collaborative Experts on MSR-VTT
This notebook shows how to download code that trains a Collaborative Experts model with GPT-1 + NetVLAD on the MSR-VTT Dataset.
Setup
Download Code ... | Python Code:
%tensorflow_version 2.x
!git clone https://github.com/googleinterns/via-content-understanding.git
%cd via-content-understanding/videoretrieval/
!pip install -r requirements.txt
!pip install --upgrade tensorflow_addons
Explanation: Training Collaborative Experts on MSR-VTT
This notebook shows how to downloa... |
5,193 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
Hi I've read a lot of question here on stackoverflow about this problem, but I have a little different task. | Problem:
import pandas as pd
df = pd.DataFrame({'DateTime': ['2000-01-04', '2000-01-05', '2000-01-06', '2000-01-07', '2000-01-08'],
'Close': [1460, 1470, 1480, 1480, 1450]})
df['DateTime'] = pd.to_datetime(df['DateTime'])
def g(df):
label = []
for i in range(len(df)-1):
if df.loc[i, '... |
5,194 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sentiment Analysis - Text Classification with Universal Embeddings
Textual data in spite of being highly unstructured, can be classified into two major types of documents.
- Factual documen... | Python Code:
!pip install tensorflow-hub
Explanation: Sentiment Analysis - Text Classification with Universal Embeddings
Textual data in spite of being highly unstructured, can be classified into two major types of documents.
- Factual documents which typically depict some form of statements or facts with no specific ... |
5,195 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Speed
Step1: prior to trying to fix #217 | Python Code:
%%timeit -n 15
lasio.examples.open("6038187_v1.2.las")
Explanation: Speed
End of explanation
import pickle
las = lasio.examples.open("logging_levels.las")
len(pickle.dumps(las))
las = lasio.examples.open("6038187_v1.2.las")
len(pickle.dumps(las))
Explanation: prior to trying to fix #217: 6038187_v1... |
5,196 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Logistic-Regression" data-toc-modified-id="Logistic-Regression-1"><span clas... | Python Code:
# code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', 'notebook_format'))
from formats import load_style
load_style(plot_style = False)
os.chdir(path)
# 1. magic for inline plot
# 2. magic to print... |
5,197 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Decision Trees and Random Forests in Python
Learning Objectives
Explore and analyze data using a Pairplot
Train a single Decision Tree
Predict and evaluate the Decision Tree
Compare the Deci... | Python Code:
!pip install scikit-learn==0.22.2
Explanation: Decision Trees and Random Forests in Python
Learning Objectives
Explore and analyze data using a Pairplot
Train a single Decision Tree
Predict and evaluate the Decision Tree
Compare the Decision Tree model to a Random Forest
Introduction
In this lab, you explo... |
5,198 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Atmos
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify d... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'cnrm-esm2-1', 'atmos')
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: CNRM-ESM2-1
Topic: Atmos
Sub-Topics: Dynami... |
5,199 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture 1. Introduction to Tensorflow
Deep Learning Library
Theano, Torch, Tensorflow가 가장 많이 사용되는 lib
| Software | Creator |Opensource | I... | Python Code:
# Load Module
import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
import tensorflow as tf
# Load dataset.
iris = datasets.load_iris() # 총 150개의 붓꽃 사진과 class load
x_train, x_test, y_train, y_test = model_selection.train_test_split(iris.data, iris.t... |
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