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4,000 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: TensorFlow Hub によるテキストの分類
Step2: IMDB データセットをダウンロードする
IMDB データセットは、imdb reviews または TensorFlow データセットで提供されています。次のコードを使って、IMDB データセットをマシン（または C... | 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... |
4,001 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Create a classifier to predict the wine color from wine quality attributes using this dataset
Step1: Query for the data and create a numpy array
Step2: Split the data into features (x) and... | Python Code:
import pg8000
conn = pg8000.connect(host='training.c1erymiua9dx.us-east-1.rds.amazonaws.com', database="training", port=5432, user='dot_student', password='qgis')
cursor = conn.cursor()
database=cursor.execute("SELECT * FROM winequality")
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inli... |
4,002 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Basic Principles of Machine Learning
Here we'll dive into the basic principles of machine learning, and how to
utilize them via the Scikit-Learn API.
After briefly introducing scikit-learn's... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
Explanation: Basic Principles of Machine Learning
Here we'll dive into the basic principles of machine learning, and how to
utilize them via the Scikit-Learn API.
After briefly introducing scikit-learn's Estimator object, we'll cover sup... |
4,003 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Machine Learning Basics with Sklearn
First some imports for the notebook and visualization.
Step1: Choosing a dataset
First of all you need a dataset to work on. To keep things simple we wi... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Machine Learning Basics with Sklearn
First some imports for the notebook and visualization.
End of explanation
from sklearn.datasets import load_iris
iris = load_iris()
Explanation: Choosing a dataset
First of all you need a... |
4,004 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Working with Text Data and Naive Bayes in scikit-learn
Agenda
Working with text data
Representing text as data
Reading SMS data
Vectorizing SMS data
Examining the tokens and their counts
Bon... | Python Code:
from sklearn.feature_extraction.text import CountVectorizer
# start with a simple example
simple_train = ['call you tonight', 'Call me a cab', 'please call me... PLEASE!', 'help']
# learn the 'vocabulary' of the training data
vect = CountVectorizer()
vect.fit(simple_train)
# vect.get_feature_names()
vect.v... |
4,005 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fundamentals of audio and music analysis
Open source libraries
Python
librosa (ISC / MIT licensed)
pyaudio (MIT licensed)
portaudio
Prepare sound for analysis
NOTE
Step1: [Optional] Record... | Python Code:
import pyaudio
import wave
Explanation: Fundamentals of audio and music analysis
Open source libraries
Python
librosa (ISC / MIT licensed)
pyaudio (MIT licensed)
portaudio
Prepare sound for analysis
NOTE: Either record your own voice or import a sample from file
End of explanation
# In this step, find out... |
4,006 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Предобработка данных и логистическая регрессия для задачи бинарной классификации
Programming assignment
В задании вам будет предложено ознакомиться с основными техниками предобработки данных... | Python Code:
import pandas as pd
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
matplotlib.style.use('ggplot')
%matplotlib inline
Explanation: Предобработка данных и логистическая регрессия для задачи бинарной классификации
Programming assignment
В задании вам будет предложено ознакомиться с ... |
4,007 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plotting with Folium
What is Folium?
Folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the leaflet.js library. This allows you to manipulate ... | Python Code:
# Import Libraries
import pandas as pd
import geopandas
import folium
import matplotlib.pyplot as plt
df1 = pd.read_csv('volcano_data_2010.csv')
# Keep only relevant columns
df = df1.loc[:, ("Year", "Name", "Country", "Latitude", "Longitude", "Type")]
df.info()
# Create point geometries
geometry = geopanda... |
4,008 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Select clean 83mKr events
KR83m cuts similar to Adam's note
Step1: Get S1s from these events
Step2: Save to disk
Pandas object array is very memory-ineficient. Takes about 25 MB/dataset to... | Python Code:
# Get SR1 krypton datasets
dsets = hax.runs.datasets
dsets = dsets[dsets['source__type'] == 'Kr83m']
dsets = dsets[dsets['trigger__events_built'] > 10000] # Want a lot of Kr, not diffusion mode
dsets = hax.runs.tags_selection(dsets, include='sciencerun0')
# Sample ten datasets randomly (with fixed seed,... |
4,009 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<center><h2>Scale your pandas workflows by changing one line of code</h2>
Exercise 2
Step1: Dataset
Step2: Optional
Step3: pandas.read_csv
Step4: Expect pandas to take >3 minutes on EC2,... | Python Code:
import modin.pandas as pd
import pandas
import time
from IPython.display import Markdown, display
def printmd(string):
display(Markdown(string))
Explanation: <center><h2>Scale your pandas workflows by changing one line of code</h2>
Exercise 2: Speed improvements
GOAL: Learn about common functionality t... |
4,010 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Brainstorm Elekta phantom tutorial dataset
Here we compute the evoked from raw for the Brainstorm Elekta phantom
tutorial dataset. For comparison, see [1]_ and
Step1: The data were collecte... | Python Code:
# Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import mne
from mne import find_events, fit_dipole
from mne.datasets.brainstorm import bst_phantom_elekta
from mne.io import read_raw_fif
print(__doc__)
Explanation: Brainstorm Elekta phanto... |
4,011 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Setting things up
Let's take a look at game.py, which we use to create games. Right now, Signal only does cheap-talk games with a chance player. That is, games in which the state the send... | Python Code:
sender = np.identity(3)
receiver = np.identity(3)
state_chances = np.array([1/3, 1/3, 1/3])
Explanation: 1. Setting things up
Let's take a look at game.py, which we use to create games. Right now, Signal only does cheap-talk games with a chance player. That is, games in which the state the sender observes ... |
4,012 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
机器学习工程师纳米学位
模型评价与验证
项目 1
Step1: 分析数据
在项目的第一个部分，你会对波士顿房地产数据进行初步的观察并给出你的分析。通过对数据的探索来熟悉数据可以让你更好地理解和解释你的结果。
由于这个项目的最终目标是建立一个预测房屋价值的模型，我们需要将数据集分为特征(features)和目标变量(target variable)。特征 'RM'， 'LSTA... | Python Code:
# Import libraries necessary for this project
# 载入此项目所需要的库
import numpy as np
import pandas as pd
import visuals as vs # Supplementary code
from sklearn.model_selection import ShuffleSplit
# Pretty display for notebooks
# 让结果在notebook中显示
%matplotlib inline
# Load the Boston housing dataset
# 载入波士顿房屋的数据集
da... |
4,013 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A well-used functionality in PySAL is the use of PySAL to conduct exploratory spatial data analysis. This notebook will provide an overview of ways to conduct exploratory spatial analysis in... | Python Code:
data = ps.pdio.read_files(ps.examples.get_path('NAT.shp'))
W = ps.queen_from_shapefile(ps.examples.get_path('NAT.shp'))
W.transform = 'r'
data.head()
Explanation: A well-used functionality in PySAL is the use of PySAL to conduct exploratory spatial data analysis. This notebook will provide an overview of w... |
4,014 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Microsoft Emotion API Data
Run 4_check_img_size.py
This script checks the images are not too large for Micrsofts API, which has a limit of 1kb to 4MB
When it finds an image that is too large... | Python Code:
def read_jsons(f, candidate):
tmp_dict = {}
with open(f) as json_file:
data = json.load(json_file)
data = json.loads(data)
print(data)
try:
tmp_dict['age'] = data[0]['faceAttributes']['age']
tmp_dict['gender'] = data[0]['faceAttr... |
4,015 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Monte Carlo Explorations
We will conduct some basic Monte Carlo explorations with the grmpy package. This allows us to revisit the key message of the course.
Step1: Questions
What are the r... | Python Code:
import pickle as pkl
import numpy as np
import copy
from statsmodels.sandbox.regression.gmm import IV2SLS
from mc_exploration_functions import *
import statsmodels.api as sm
import seaborn.apionly as sns
import grmpy
model_base = get_model_dict('mc_exploration.grmpy.ini')
model_base['SIMULATION']['source'... |
4,016 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Easy Ab initio calculation with ASE-Siesta-Pyscf
No installation necessary, just download a ready to go container for any system, or run it into the cloud
Are we really on the Amazon cloud??... | Python Code:
cat /proc/cpuinfo
Explanation: Easy Ab initio calculation with ASE-Siesta-Pyscf
No installation necessary, just download a ready to go container for any system, or run it into the cloud
Are we really on the Amazon cloud??
End of explanation
# import libraries and set up the molecule geometry
from ase.units... |
4,017 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Spatiotemporal permutation F-test on full sensor data
Tests for differential evoked responses in at least
one condition using a permutation clustering test.
The FieldTrip neighbor templates ... | Python Code:
# Authors: Denis Engemann <denis.engemann@gmail.com>
# Jona Sassenhagen <jona.sassenhagen@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import mne
from mne.stats import spatio_temporal_cluster_test
... |
4,018 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Goal
The goal of this notebook is to explore better methods for the final l2 centorid match during registration in the pipeline.
Generate Data
Step1: Benchmark Current Approach
Step2: KD T... | Python Code:
def newRandomCentroids(n, l, u):
diff = u-l
return [[random()*diff+l for _ in range(3)] for _ in range(n)]
newRandomCentroids(10, 10, 100)
Explanation: Goal
The goal of this notebook is to explore better methods for the final l2 centorid match during registration in the pipeline.
Generate Data
End ... |
4,019 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Airfoil example
In this example we are building a NACA 2412 airfoil from a list of points.
Lets import everything we need
Step1: Now we build up an array of points from a NACA generator. It... | Python Code:
import tigl3.curve_factories
from OCC.gp import gp_Pnt
from OCC.Display.SimpleGui import init_display
Explanation: Airfoil example
In this example we are building a NACA 2412 airfoil from a list of points.
Lets import everything we need:
End of explanation
# list of points on NACA2412 profile
px = [1.00008... |
4,020 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Support Vector Machine (SVM)
(Maximal margin classifiers)
Support Vector Machines (SVM) separates classes of data by maximizing the "space" (margin) between pairs of these groups. Classifica... | Python Code:
from IPython.display import Image
Image(url="http://docs.opencv.org/2.4/_images/separating-lines.png")
Explanation: Support Vector Machine (SVM)
(Maximal margin classifiers)
Support Vector Machines (SVM) separates classes of data by maximizing the "space" (margin) between pairs of these groups. Classificat... |
4,021 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Content Based Filtering by hand
This lab illustrates how to implement a content based filter using low level Tensorflow operations.
The code here follows the technique explained in Module 2 ... | Python Code:
!pip install tensorflow==2.5
Explanation: Content Based Filtering by hand
This lab illustrates how to implement a content based filter using low level Tensorflow operations.
The code here follows the technique explained in Module 2 of Recommendation Engines: Content Based Filtering.
End of explanation
impo... |
4,022 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Carnegie Python Bootcamp
Welcome to the python bootcamp. This thing you're reading is called an ipython notebook and will be your first introduction to the Python programming language. Noteb... | Python Code:
import antigravity
Explanation: Carnegie Python Bootcamp
Welcome to the python bootcamp. This thing you're reading is called an ipython notebook and will be your first introduction to the Python programming language. Notebooks are a combination of text markup and code that you can run in real time.
Importi... |
4,023 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exploring MzML files with the MS Ontology
In this example, we will learn how to use pronto to extract a hierarchy from the MS Ontology, a controlled vocabulary developed by the Proteomics St... | Python Code:
import pronto
ms = pronto.Ontology.from_obo_library("ms.obo")
Explanation: Exploring MzML files with the MS Ontology
In this example, we will learn how to use pronto to extract a hierarchy from the MS Ontology, a controlled vocabulary developed by the Proteomics Standards Initiative to hold metadata about ... |
4,024 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
String Matching
The idea of string matching is to find strings that match a given pattern. We have seen that Pandas provides some useful functions to do that job.
Step1: Imagine I want to h... | Python Code:
import pandas as pd
names = pd.DataFrame({"name" : ["Alice","Bob","Charlie","Dennis"],
"surname" : ["Doe","Smith","Sheen","Quaid"]})
names
names.name.str.match("A\w+")
debts = pd.DataFrame({"debtor":["D.Quaid","C.Sheen"],
"amount":[100,10000]})
debts
Explanation: S... |
4,025 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Create TensorFlow Wide and Deep Model
Learning Objective
- Create a Wide and Deep model using the high-level Estimator API
- Determine which features to use as wide columns and which to use... | Python Code:
PROJECT = "cloud-training-demos" # Replace with your PROJECT
BUCKET = "cloud-training-bucket" # Replace with your BUCKET
REGION = "us-central1" # Choose an available region for Cloud MLE
TFVERSION = "1.14" # TF version for CMLE to use
import os
os.environ["BUCKET"] = BUCKET
os.e... |
4,026 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
6 - Data Compression
This short Notebook will introduce you to how to efficiently compress your data within SampleData datasets.
<div class="alert alert-info">
**Note**
Throughout t... | Python Code:
from config import PYMICRO_EXAMPLES_DATA_DIR # import file directory path
import os
dataset_file = os.path.join(PYMICRO_EXAMPLES_DATA_DIR, 'example_microstructure') # test dataset file path
tar_file = os.path.join(PYMICRO_EXAMPLES_DATA_DIR, 'example_microstructure.tar.gz') # dataset archive path
Explanatio... |
4,027 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Probabilistic Programming in Python
Author
Step1: Summary
Step2: Summary
Random variables are abstract objects. Methods are available for operating on them algebraically. The probabilit... | Python Code:
from lea import *
# the canonical random variable : a fair coin
faircoin = Lea.fromVals('Head', 'Tail')
# toss the coin a few times
faircoin.random(10)
# Amitabh Bachan's coin from Sholay
sholaycoin = Lea.fromVals('Head', 'Head')
# Amitabh always wins (and, heroically, sacrifices himself for Dharamendra!)
... |
4,028 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regularization
Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that overfitting can be a serious problem, if the training dataset is... | Python Code:
# import packages
import numpy as np
import matplotlib.pyplot as plt
from reg_utils import sigmoid, relu, plot_decision_boundary, initialize_parameters, load_2D_dataset, predict_dec
from reg_utils import compute_cost, predict, forward_propagation, backward_propagation, update_parameters
import sklearn
impo... |
4,029 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Le perceptron multicouche avec scikit-learn
Documentation officielle
Step1: Classification
C.f. http
Step2: Une fois le réseau de neurones entrainé, on peut tester de nouveaux exemples
Ste... | Python Code:
import sklearn
# version >= 0.18 is required
version = [int(num) for num in sklearn.__version__.split('.')]
assert (version[0] >= 1) or (version[1] >= 18)
Explanation: Le perceptron multicouche avec scikit-learn
Documentation officielle: http://scikit-learn.org/stable/modules/neural_networks_supervised.htm... |
4,030 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Clase 3
Step1: Una vez cargados los paquetes, es necesario definir los tickers de las acciones que se usarán, la fuente de descarga (Yahoo en este caso, pero también se puede desde Google) ... | Python Code:
#importar los paquetes que se van a usar
import pandas as pd
import pandas_datareader.data as web
import numpy as np
import datetime
from datetime import datetime
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
#algunas opciones para Python
pd.set_option... |
4,031 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Mechpy Tutorials
a mechanical engineering toolbox
source code - https
Step1: Reading raw test data example 1
This example shows how to read multiple csv files and plot them together
Step2: ... | Python Code:
# setup
import numpy as np
import sympy as sp
import pandas as pd
import scipy
from pprint import pprint
sp.init_printing(use_latex='mathjax')
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = (12, 8) # (width, height)
plt.rcParams['font.size'] = 14
plt.rcParams['legend.fontsize'] = 16
fro... |
4,032 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interact Exercise 6
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.
Step1: Exploring the Fermi distribution
In quantum statistics, the ... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import Image
from IPython.html.widgets import interact, interactive, fixed
Explanation: Interact Exercise 6
Imports
Put the standard imports for Matplotlib, Numpy and the IPython widgets in the following cell.
End of... |
4,033 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Propagate uncertainties with the errors add-on for CO2SYS-Matlab
James Orr<br>
<img align="left" width="50%" src="http
Step1: Specify the directory where you have put the Matlab routines CO... | Python Code:
%load_ext oct2py.ipython
Explanation: Propagate uncertainties with the errors add-on for CO2SYS-Matlab
James Orr<br>
<img align="left" width="50%" src="http://www.lsce.ipsl.fr/Css/img/banniere_LSCE_75.png"><br><br>
LSCE/IPSL, CEA-CNRS-UVSQ, Gif-sur-Yvette, France
27 February 2018 <br><br>
updated: 29 June ... |
4,034 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: pandas DataFrame を読み込む
<table class="tfo-notebook-buttons" align="left">
<td>... | 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... |
4,035 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
人人汽车推荐系统调研综述
这里是一个推荐引擎，使用经典数据集movielens，可以将movies数据替换为人人的车型数据，rating数据替换为从日志系统中收集的所有用户对车的点击次数，浏览时间（权重）。这样可以实现C端对车型的推荐
架构：
①日志系统：搜集用户行为提供离线数据
②推荐引擎：A
Step1: 在_产品-产品协同过滤_中的产品之间的相似性值是通过观察所有对两个... | Python Code:
import numpy as np
import pandas as pd
import os
# 使用pandas加载csv数据
movies = pd.read_csv(os.path.expanduser("~/ml-latest-small/movies.csv"))
ratings = pd.read_csv(os.path.expanduser("~/ml-latest-small/ratings.csv"))
# 去掉无用的维度
ratings.drop(['timestamp'],axis=1,inplace=True)
movies.head()
ratings.head()
# 将mo... |
4,036 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Surfaces in pyOpTools
The basic object to create optical components in pyOpTools are the surfaces. They are used to define the border that separates 2 materials (for example air-glass) in an... | Python Code:
from pyoptools.all import *
from numpy import pi
Explanation: Surfaces in pyOpTools
The basic object to create optical components in pyOpTools are the surfaces. They are used to define the border that separates 2 materials (for example air-glass) in an optical component.
Below are some of the Surface Objec... |
4,037 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
FCLA/FNLA Fast.ai Numerical/Computational Linear Algebra
Lecture 3
Step1: So if A is approx equal to Q•Q.T•A .. but not equal.. then Q is not the identity, but is very close to it.
Oh, righ... | Python Code:
import torch
import numpy as np
Q = np.eye(3)
print(Q)
print(Q.T)
print(Q @ Q.T)
# construct I matrix
Q = torch.eye(3)
# torch matrix multip
# torch.mm(Q, Q.transpose)
Q @ torch.t(Q)
Explanation: FCLA/FNLA Fast.ai Numerical/Computational Linear Algebra
Lecture 3: New Perspectives on NMF, Randomized SVD
Not... |
4,038 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Communication between components
Purpose
Step1: The simplest thing we can do with the Redis server is to set and get key values. The keys are strings and the values are strings, so you can... | Python Code:
import redis
r = redis.StrictRedis(host='localhost')
r.set('key', 'value')
print r.get('key')
Explanation: Communication between components
Purpose: If we are connecting to hardware or otherwise doing something involving multiple computers or even separate processes on the same computer, we need some easy ... |
4,039 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Un ipython notebook garde ses résultats depuis la dernière fois, mais pas son état. Il convient donc re-exécuter depuis le début.
Step1: Example of sampling from a probability distribution... | Python Code:
import numpy as np
import scipy.stats as ss
import matplotlib.pyplot as plt
import sklearn
import pandas as pd
%matplotlib inline
x = np.linspace(-100, 100, 201)
plt.plot(x, x * x)
Explanation: Un ipython notebook garde ses résultats depuis la dernière fois, mais pas son état. Il convient donc re-exécuter... |
4,040 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Tensors
Tensors are similar to NumPy’s ndarrays, with the addition being that Tensors can also be used on a GPU to accelerate computing.
Step2: Operation on Tensors
S... | Python Code:
import torch
Explanation: <a href="https://colab.research.google.com/github/rishuatgithub/MLPy/blob/master/PyTorchStuff.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
All about Pytorch
End of explanation
x = torch.empty(5,3) ## empty
x
... |
4,041 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Simulate RAD-seq data
The simulations software simrrls is available at github.com/dereneaton/simrrls. First we create a directory called ipsimdata/ and then simulate data and put it in this ... | Python Code:
## name for our sim data directory
DIR = "./ipsimdata"
## A mouse MT genome used to stick our data into.
INPUT_CHR = "/home/deren/Downloads/MusMT.fa"
## number of RAD loci to simulate
NLOCI = 1000
## number of inserts to reference genome and insert size
N_INSERTS = 100
INSERT_SIZE = 50
Explanation: Simulat... |
4,042 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
抽象数据类型和 Python 类
抽象数据类型
Abstract data Type, ADT
数据类型
Python 基本数据类型：逻辑类型bool，数值类型int和float，字符串str和组合数据类型
str, tuple, frozenset 是不变数据类型，list, set, dict 是可变数据类型
Python类
在Python中，利用class定义（类定义）实... | Python Code:
class Student(object):
skills = []
def __init__(self, name):
self.name = name
stu = Student('ly')
print Student.skills # 访问类数据属性
Student.skills.append('Python')
print Student.skills
print stu.skills # 通过实例也能访问类数据属性
print dir(Student)
Student.age = 25 # 通过类名动态添加类... |
4,043 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Welcome to the LIGO data visualization tutorial!
Installation
Please make sure you have GWpy installed before you begin!
Only execute the below cell if you have not already installed GWpy
S... | Python Code:
#! python3 -m pip install gwpy
Explanation: Welcome to the LIGO data visualization tutorial!
Installation
Please make sure you have GWpy installed before you begin!
Only execute the below cell if you have not already installed GWpy
End of explanation
from gwosc.datasets import find_datasets
find_datasets(... |
4,044 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Think Like a Machine - Chapter 5
Logistic Regression
ACKNOWLEDGEMENT
A lot of the code in this notebook is from John D. Wittenauer's notebooks that cover the exercises in Andrew Ng's course ... | Python Code:
# Use the functions from another notebook in this notebook
%run SharedFunctions.ipynb
# Import our usual libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import os
path = os.getcwd() + '/Data/ex2data1.txt'
data = pd.read_csv(path, header=None, names=['Exam... |
4,045 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<div align="right">Python 3.6 Jupyter Notebook</div>
Introduction to Funf
Your completion of the notebook exercises will be graded based on your ability to do the following
Step1: 1. Friend... | Python Code:
import pandas as pd
import numpy as np
import folium
import matplotlib.pylab as plt
import matplotlib
%matplotlib inline
matplotlib.rcParams['figure.figsize'] = (10, 8)
Explanation: <div align="right">Python 3.6 Jupyter Notebook</div>
Introduction to Funf
Your completion of the notebook exercises will be g... |
4,046 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook contains examples related to survival analysis, based on Chapter 13 of Think Stats, 2nd Edition, by Allen Downey, available from thinkstats2.com
Step1: The following code look... | Python Code:
from __future__ import print_function, division
import marriage
import thinkstats2
import thinkplot
import pandas
import numpy
from lifelines import KaplanMeierFitter
from collections import defaultdict
import itertools
import math
import matplotlib.pyplot as pyplot
from matplotlib import pylab
%matplotlib... |
4,047 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Traffic Sign Classification with Keras
Keras exists to make coding deep neural networks simpler. To demonstrate just how easy it is, you’re going to use Keras to build a convolutional neural... | Python Code:
from urllib.request import urlretrieve
from os.path import isfile
from tqdm import tqdm
class DLProgress(tqdm):
last_block = 0
def hook(self, block_num=1, block_size=1, total_size=None):
self.total = total_size
self.update((block_num - self.last_block) * block_size)
self.las... |
4,048 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<div align='center' ><img src='https
Step1: 5. Impulse response functions
Impulse response functions (IRFs) are a standard tool for analyzing the short run dynamics of dynamic macroeconomic... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sympy as sym
import solowpy
# define model parameters
ces_params = {'A0': 1.0, 'L0': 1.0, 'g': 0.02, 'n': 0.03, 's': 0.15,
'delta': 0.05, 'alpha': 0.33, 'sigma': 1.01}
# create an instance of the ... |
4,049 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
OkNLP
This notebook demonstrates the algorithm we used in our project. It shows an example of how we clustered using Nonnegative Matrix Factorization. We manually inspect the output of NMF t... | Python Code:
import warnings
import numpy as np
import pandas as pd
from scipy.sparse import hstack
from sklearn.cross_validation import cross_val_predict
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, ... |
4,050 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Basic test of the wflow BMI interface
Step1: Startup two models
Step2: <h3>Now we can investigate some model parameters
Step3: <h3>Start and end times
Step4: <h3>Now start the models... | Python Code:
import wflow.wflow_bmi as bmi
import logging
reload(bmi)
%pylab inline
import datetime
from IPython.html.widgets import interact
Explanation: <h1>Basic test of the wflow BMI interface
End of explanation
# This is the LAnd Atmophere (LA) model
LA_model = bmi.wflowbmi_csdms()
LA_model.initialize('../example... |
4,051 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Divide continuous data into equally-spaced epochs
This tutorial shows how to segment continuous data into a set of epochs spaced
equidistantly in time. The epochs will not be created based o... | Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.preprocessing import compute_proj_ecg
from mne_connectivity import envelope_correlation
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
... |
4,052 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will build a logistic regression classifier to recognize cats. This assignment... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset
%matplotlib inline
Explanation: Logistic Regression with a Neural Network mindset
Welcome to your first (required) programming assignment! You will b... |
4,053 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fixing BEM and head surfaces
Sometimes when creating a BEM model the surfaces need manual correction because
of a series of problems that can arise (e.g. intersection between surfaces).
Here... | Python Code:
# Authors: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Ezequiel Mikulan <e.mikulan@gmail.com>
# Manorama Kadwani <manorama.kadwani@gmail.com>
#
# License: BSD-3-Clause
import os
import shutil
import mne
data_path = mne.datasets.sample.data_path()
subjects_dir = data_path / 'subjects'
bem_... |
4,054 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2D plots
Demonstration of the 2D plot capabilities
The plot2d plot method make plots of 2-dimensional scalar data
using matplotlibs pcolormesh or the contourf functions.
Note that this metho... | Python Code:
import psyplot.project as psy
import xarray as xr
%matplotlib inline
%config InlineBackend.close_figures = False
import numpy as np
Explanation: 2D plots
Demonstration of the 2D plot capabilities
The plot2d plot method make plots of 2-dimensional scalar data
using matplotlibs pcolormesh or the contourf fun... |
4,055 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interactions and ANOVA
Note
Step1: Take a look at the data
Step2: Fit a linear model
Step3: Have a look at the created design matrix
Step4: Or since we initially passed in a DataFrame, w... | Python Code:
%matplotlib inline
from urllib.request import urlopen
import numpy as np
np.set_printoptions(precision=4, suppress=True)
import pandas as pd
pd.set_option("display.width", 100)
import matplotlib.pyplot as plt
from statsmodels.formula.api import ols
from statsmodels.graphics.api import interaction_plot, abl... |
4,056 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The Fermi-Hubbard Model
This notebook shows how to use the tensor_basis constructor to build the Hamiltonian of interacting spinful fermions in 1d, desctibed by the Fermi-Hubbard model (FHM)... | Python Code:
from quspin.operators import hamiltonian # Hamiltonians and operators
from quspin.basis import spinless_fermion_basis_1d, tensor_basis # Hilbert space fermion and tensor bases
import numpy as np # generic math functions
##### define model parameters #####
L=4 # system size
J=1.0 # hopping
U=np.sqrt(2.0) # ... |
4,057 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Blowing up things!
So far we learned about functions, conditions and loops. Lets use our knowledge so far to do something fun - blow up things!
In this adventure we will build structures wit... | Python Code:
# Run this once before starting your tasks
import mcpi.minecraft as minecraft
import mcpi.block as block
import time
import thread
mc = minecraft.Minecraft.create()
Explanation: Blowing up things!
So far we learned about functions, conditions and loops. Lets use our knowledge so far to do something fun - b... |
4,058 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Part 5
Step1: Configure GCP environment settings
Update the following variables to reflect the values for your GCP environment
Step2: Authenticate your GCP account
This is required if you ... | Python Code:
import numpy as np
import tensorflow as tf
Explanation: Part 5: Deploy the solution to AI Platform Prediction
This notebook is the fifth of five notebooks that guide you through running the Real-time Item-to-item Recommendation with BigQuery ML Matrix Factorization and ScaNN solution.
Use this notebook to ... |
4,059 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
State space models - concentrating the scale out of the likelihood function
Step1: Introduction
(much of this is based on Harvey (1989); see especially section 3.4)
State space models can g... | Python Code:
import numpy as np
import pandas as pd
import statsmodels.api as sm
dta = sm.datasets.macrodata.load_pandas().data
dta.index = pd.PeriodIndex(start='1959Q1', end='2009Q3', freq='Q')
Explanation: State space models - concentrating the scale out of the likelihood function
End of explanation
class LocalLevel(... |
4,060 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Face Generation
In this project, you'll use generative adversarial networks to generate new images of faces.
Get the Data
You'll be using two datasets in this project
Step3: Explore ... | Python Code:
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
DON'T MODIFY ANYTHING IN THIS CELL
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Explanation: Face Generation
In this project, you'll use generative adversa... |
4,061 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Github
https
Step3: List Comprehensions
Step4: Dictionaries
Python dictionaries are awesome. They are hash tables and have a lot of neat CS properties. Learn and use them well. | Python Code:
# Create a [list]
days = ['Monday', # multiple lines
'Tuesday', # acceptable
'Wednesday',
'Thursday',
'Friday',
'Saturday',
'Sunday',
] # trailing comma is fine!
days
# Simple for-loop
for day in days:
print(day)
# Double for-loop
for day in da... |
4,062 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
← Back to Index
Novelty Functions
To detect note onsets, we want to locate sudden changes in the audio signal that mark the beginning of transient regions. Often, an increase in the sig... | Python Code:
x, sr = librosa.load('audio/simple_loop.wav')
print(x.shape, sr)
Explanation: ← Back to Index
Novelty Functions
To detect note onsets, we want to locate sudden changes in the audio signal that mark the beginning of transient regions. Often, an increase in the signal's amplitude envelope will denote an... |
4,063 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a id='top'></a>
Random Forests
May 2017
<br>
This is a study for a blog post to appear on Data Simple. It will focus on the theory and scikit-learn implementation of the Random Forest machi... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
# Generate data
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=300, n_features=2, centers=3,
cluster_std=4, random_state=42)
# Plot data
plt.plot(X[:, 0][y==0], X[:, 1][y==0], "yo", marker='.')
plt.plot(X[:, 0][y==1],... |
4,064 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2.2 DataFrame
Content
Step1: 2.1.1 DataFrame Structure
Initializing a Dataframe.
Step2: 2.2.2 Working with columns
Step3: Exercise
Step4: There are many commonly used column-wide methods... | Python Code:
import numpy as np
import pandas as pd
Explanation: 2.2 DataFrame
Content:
- 2.2.1 DataFrame Structure
- 2.2.2 Working with Columns
- 2.2.3 Working with Rows
- 2.2.4 Conditional Selection
- 2.2.5 Case Study: Olympic Games
A DataFrame is a two dimensional data structure with columns of potentially... |
4,065 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
GUI creation and interaction in IPython
Step1: A pop up will appear saying
The kernel appears to have died and will restart automatically
In the terminal, you can also see the following me... | Python Code:
%gui
from PyQt5 import QtWidgets
b1 = QtWidgets.QPushButton("Click Me")
Explanation: GUI creation and interaction in IPython
End of explanation
%gui qt5
from PyQt5 import QtWidgets
b1 = QtWidgets.QPushButton("Click Me")
b1.show()
Explanation: A pop up will appear saying
The kernel appears to have died and... |
4,066 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example of taking 'views' from simulated populations
Step1: Get the mutations that are segregating in each population
Step2: Look at the raw data in the first element of each list
Step3: ... | Python Code:
from __future__ import print_function
import fwdpy as fp
import pandas as pd
from background_selection_setup import *
Explanation: Example of taking 'views' from simulated populations
End of explanation
mutations = [fp.view_mutations(i) for i in pops]
Explanation: Get the mutations that are segregating in ... |
4,067 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ordinary Least Squares
Step1: OLS estimation
Artificial data
Step2: Our model needs an intercept so we add a column of 1s
Step3: Fit and summary
Step4: Quantities of interest can be extr... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.sandbox.regression.predstd import wls_prediction_std
np.random.seed(9876789)
Explanation: Ordinary Least Squares
End of explanation
nsample = 100
x = np.linspace(0, 10, 10... |
4,068 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using groupby(), plot the number of films that have been released each decade in the history of cinema.
Step1: Use groupby() to plot the number of "Hamlet" films made each decade.
Step2: H... | Python Code:
titles.groupby(titles['year']//10 *10)['year'].size().plot(kind='bar')
Explanation: Using groupby(), plot the number of films that have been released each decade in the history of cinema.
End of explanation
titles[titles['title']=='Hamlet'].groupby(titles[titles['title']=='Hamlet']['year']//10 *10)['year']... |
4,069 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
原始 SPN 实现
原理与算法
这里我们实现了教材上的原始 SPN 算法，相关的数据（秘钥，秘钥编排算法，S盒，P盒，轮数）均保持一致，并在程序内部定义。
程序设计
常量规定
| 变量 | 意义 | 类型 |
| ------------ | ---- | ---- |
| x | 明文 | 整形 |
| piS ... | Python Code:
m, l = 4, 4 # m S-Boxes, l bits in each
piS = {0: 14, 1: 4, 2: 13, 3: 1,
4: 2, 5: 15, 6: 11, 7: 8,
8: 3, 9: 10, 10: 6, 11: 12,
12: 5, 13: 9, 14: 0, 15: 7}
piP = {1: 1, 2: 5, 3: 9, 4: 13,
5: 2, 6: 6, 7: 10, 8: 14,
9: 3, 10: 7, 11: 11, 12: 15,
13: 4, 14: 8, 15: 12, 1... |
4,070 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
In the last few years speed dating popularity has grown quickly. Despite its popularity lots of people don't seem as satisfied as they'd like. Most users don't end up finding wh... | Python Code:
speedDatingDF = pd.read_csv("Speed Dating Data.csv",encoding = "ISO-8859-1")
#speedDatingDF.dtypes #We can see which type has each attr.
Explanation: Introduction
In the last few years speed dating popularity has grown quickly. Despite its popularity lots of people don't seem as satisfied as they'd like. M... |
4,071 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Wolf-Sheep-Grass Model with Soil Creep
This notebook demonstrates coupling of an ABM implemented in Mesa and a grid-based numerical model written in Landlab. The example is the canoni... | Python Code:
try:
from mesa import Model
except ModuleNotFoundError:
print(
Mesa needs to be installed in order to run this notebook.
Normally Mesa should be pre-installed alongside the Landlab notebook collection.
But it appears that Mesa is not already installed on the system on which you are
runnin... |
4,072 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Handy small functions related to astronomical research
Step3: Defining function
Emission related
1. Dust
Step8: 2. Opacity
Step10: Motions
1. free-fall timescale
Step13: 2. Jeans Length ... | Python Code:
import math
import numpy as np
from numpy import size
Explanation: Handy small functions related to astronomical research
End of explanation
def Planckfunc_cgs(freq, temperature):
Calculate Planck function.
Inputs:
freq: frequency, in Hz
temperature: temperature in Kelvin
Return... |
4,073 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Efficiently searching for optimal tuning parameters
From the video series
Step1: More efficient parameter tuning using GridSearchCV
Allows you to define a grid of parameters that will be se... | Python Code:
from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
from sklearn.cross_validation import cross_val_score
import matplotlib.pyplot as plt
%matplotlib inline
# read in the iris data
iris = load_iris()
# create X (features) and y (response)
X = iris.data
y = iris.target
#... |
4,074 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Callables in Research
The main purpose of Research is to run pipleines with different configs in parallel but you also can add callables and realize very flexible plans of experiments even w... | Python Code:
import sys
import os
import shutil
import warnings
warnings.filterwarnings('ignore')
from tensorflow import logging
logging.set_verbosity(logging.ERROR)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import matplotlib
%matplotlib inline
import numpy as np
sys.path.append('../../..')
from batchflow import Pipelin... |
4,075 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bayesian Parametric Regression
Notebook version
Step1: 1. Model-based parametric regression
1.1. The regression problem.
Given an observation vector ${\bf x}$, the goal of the regression pr... | Python Code:
# Import some libraries that will be necessary for working with data and displaying plots
# To visualize plots in the notebook
%matplotlib inline
from IPython import display
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.io # To read matlab files
import pylab
impor... |
4,076 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Note that you have to execute the command jupyter notebook in the parent directory of
this directory for otherwise jupyter won't be able to access the file style.css.
Step1: This example h... | Python Code:
from IPython.core.display import HTML
with open ("../style.css", "r") as file:
css = file.read()
HTML(css)
Explanation: Note that you have to execute the command jupyter notebook in the parent directory of
this directory for otherwise jupyter won't be able to access the file style.css.
End of explanat... |
4,077 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Manipulation with Numpy and Pandas
Handling with large data is easy in Python. In the simplest way using arrays. However, they are pretty slow. Numpy and Panda are two great libraries f... | Python Code:
import numpy as np
# Generating a random array
X = np.random.random((3, 5)) # a 3 x 5 array
print(X)
Explanation: Data Manipulation with Numpy and Pandas
Handling with large data is easy in Python. In the simplest way using arrays. However, they are pretty slow. Numpy and Panda are two great libraries for... |
4,078 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<center><h2>Scale your pandas workflows by changing one line of code</h2>
Exercise 1
Step1: Concept for exercise
Step2: Now that we have created a toy example for playing around with the D... | Python Code:
# Modin engine can be specified either by config
import modin.config as cfg
cfg.Engine.put("dask")
# or by setting the environment variable
# import os
# os.environ["MODIN_ENGINE"] = "dask"
Explanation: <center><h2>Scale your pandas workflows by changing one line of code</h2>
Exercise 1: How to use Modin
G... |
4,079 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Simple Reinforcement Learning in Tensorflow Part 1
Step1: The Bandit
Here we define our bandit. For this example we are using a four-armed bandit. The pullBandit function generates a random... | Python Code:
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
Explanation: Simple Reinforcement Learning in Tensorflow Part 1:
The Multi-armed bandit
This tutorial contains a simple example of how to build a policy-gradient based agent that can solve the multi-armed bandit problem. For ... |
4,080 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Pulling
Step1: to find a particular class, open the page using chrome,
select the particular subpart of page and click inspect
Name of Movie
Step2: Ratings from Rotten Tomatoes | Python Code:
r = urllib.request.urlopen('https://www.rottentomatoes.com/franchise/batman_movies').read()
#Using Beautiful Soup Library to parse the data
soup = BeautifulSoup(r, "lxml")
type(soup)
len(str(soup.prettify()))
soup
soup.prettify()
#We convert the data to a string format using str.
#Note in R we use str for... |
4,081 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Illustrating common terms usage using Wikinews in english
getting data
We get the cirrussearch dump of wikinews (a dump meant for elastic-search indexation).
Step1: Preparing data
we arrang... | Python Code:
LANG="english"
%%bash
fdate=20170327
fname=enwikinews-$fdate-cirrussearch-content.json.gz
if [ ! -e $fname ]
then
wget "https://dumps.wikimedia.org/other/cirrussearch/$fdate/$fname"
fi
# iterator
import gzip
import json
FDATE = 20170327
FNAME = "enwikinews-%s-cirrussearch-content.json.gz" % FDATE
def ... |
4,082 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Permutation T-test on sensor data
One tests if the signal significantly deviates from 0
during a fixed time window of interest. Here computation
is performed on MNE sample dataset between 40... | Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import numpy as np
import mne
from mne import io
from mne.stats import permutation_t_test
from mne.datasets import sample
print(__doc__)
Explanation: Permutation T-test on sensor data
One tests if the signal significantly... |
4,083 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Authors.
Step1: Eager execution basics
<table class="tfo-notebook-buttons" align="left"><td>
<a target="_blank" href="https
Step2: Tensors
A Tensor is a multi... | 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... |
4,084 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exercise 1 - Basic SQL DML and DDL
Part 1 - Data manipulation (DML)
Get the survey.db SQLite3 database file from the Software Carpentry lesson and connect to it.
Step1: Basic queries
Step2:... | Python Code:
!wget http://files.software-carpentry.org/survey.db
%load_ext sql
%sql sqlite:///survey.db
Explanation: Exercise 1 - Basic SQL DML and DDL
Part 1 - Data manipulation (DML)
Get the survey.db SQLite3 database file from the Software Carpentry lesson and connect to it.
End of explanation
%%sql
SELECT personal,... |
4,085 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
Here, you'll use window functions to answer questions about the Chicago Taxi Trips dataset.
Before you get started, run the code cell below to set everything up.
Step1: The fol... | Python Code:
# Set up feedback system
from learntools.core import binder
binder.bind(globals())
from learntools.sql_advanced.ex2 import *
print("Setup Complete")
Explanation: Introduction
Here, you'll use window functions to answer questions about the Chicago Taxi Trips dataset.
Before you get started, run the code cel... |
4,086 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Momentum
A stock that's going up tends to keep going up...until it doesn't. Momentum is the theory that stocks that have recently gone up will keep going up disproportionate to their underl... | Python Code:
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import pinkfish as pf
import strategy
# format price data
pd.options.display.float_format = '{:0.2f}'.format
%matplotlib inline
# Set size of inline plots
'''note: rcParams can't be in same cell as import matplotlib
or %matplotlib inlin... |
4,087 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sochastic simulation algorithm (SSA)
Jens Hahn - 06/06/2016
Last time we have talked about ODE modelling, which is a deterministic and continuous way of modelling. This time, we'll talk ab... | Python Code:
import math
import numpy as np
import matplotlib.pyplot as pyp
%matplotlib inline
# S -> P*S - B*S*Z - d*S
S = 500
# Z -> B*S*Z + G*R - A*S*Z
Z = 0
# R -> d*S - G*R
R = 0
P = 0.0001 # birth rate
d = 0.01 # 'natural' death percent (per day)
B = 0.0095 # transmission percent (per day)
G = 0.001 # resure... |
4,088 | 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 7</font>
Download
Step1: Missão
Step2: Teste da Solução | 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 7</font>
Download: http://github.com/dsacademybr
End of explanation
impor... |
4,089 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Classifying Blobs
Step1: Digets examples
Classification using (linear) PCA and (nonlinear) Isometric Maps
Step2: Unsuperised learning
Notice that the training labels are unused. The digits... | Python Code:
from sklearn.datasets import make_blobs
X, y = make_blobs(random_state=42, centers=3)
X[:,1] += 0.25*X[:,0]**2
# print(X.shape)
# print(y)
# plt.scatter(X[:, 0], X[:, 1], 20, y, edgecolor='none')
plt.plot(X[:, 0], X[:, 1], 'ok')
from sklearn.cluster import KMeans, AffinityPropagation, SpectralClustering
# ... |
4,090 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Replication Archive for "Measuring causes of death in populations
Step1: Table 1
Confusion matrices for physician-certified verbal autopsy and random-allocation verbal autopsy. Panel A show... | Python Code:
import numpy as np, pandas as pd, matplotlib.pyplot as plt, seaborn as sns
%matplotlib inline
sns.set_style('whitegrid')
sns.set_context('poster')
Explanation: Replication Archive for "Measuring causes of death in populations: a new metric that corrects cause-specific mortality fractions for chance"
End of... |
4,091 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1) Make a request from the Forecast.io API for where you were born (or lived, or want to visit!).
Tip
Step1: 2) What's the current wind speed? How much warmer does it feel than it actually ... | Python Code:
import requests
# api request for bethesda, maryland
response = requests.get('https://api.forecast.io/forecast/a197f06e1906b1a937ad31d4378b8939/38.9847, -77.0947')
data = response.json()
current = data['currently']
current
Explanation: 1) Make a request from the Forecast.io API for where you were born (o... |
4,092 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TensorFlow
Step1: Download the Data
Step2: Dataset Metadata
Step3: Building a TensorFlow Custom Estimator
Creating feature columns
Creating model_fn
Create estimator using the model_fn
De... | Python Code:
import math
import os
import pandas as pd
import numpy as np
from datetime import datetime
import tensorflow as tf
from tensorflow import data
print "TensorFlow : {}".format(tf.__version__)
SEED = 19831060
Explanation: TensorFlow: Optimizing Learning Rate
End of explanation
DATA_DIR='data'
# !mkdir $DATA_D... |
4,093 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Evolutionary algorithm to calibrate model
1 get data from S&P500
Step1: Define parameter space bounds
We define the parameter bounds as follows.
| Parameter | Values (start, stop, step) |
... | Python Code:
start_date = '2010-01-01'
end_date = '2016-12-31'
spy = data.DataReader("SPY",
start=start_date,
end=end_date,
data_source='google')['Close']
spy_returns = spy.pct_change()[1:]
spy_volume = data.DataReader("SPY",
... |
4,094 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Implementing an LR-Table-Generator
A Grammar for Grammars
As the goal is to generate an LR-table-generator we first need to implement a parser for context free grammars.
The file arith.g con... | Python Code:
!type Examples\c-grammar.g
!cat Examples/arith.g
Explanation: Implementing an LR-Table-Generator
A Grammar for Grammars
As the goal is to generate an LR-table-generator we first need to implement a parser for context free grammars.
The file arith.g contains an example grammar that describes arithmetic expr... |
4,095 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The Efficient Frontier
Step1: Assume that we have 4 assets, each with a return series of length 1000. We can use numpy.random.randn to sample returns from a normal distribution.
Step2: The... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import cvxopt as opt
from cvxopt import blas, solvers
import pandas as pd
np.random.seed(123)
# Turn off progress printing
solvers.options['show_progress'] = False
Explanation: The Efficient Frontier: Markowitz Portfolio optimization in Python
Authors: Dr... |
4,096 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Attempting human-like speach
Step1: Define our markiv hcain functions. First to create the dics. First attempt only takes triplets of words a b c and adds {'a b'
Step2: Load the books and ... | Python Code:
import pensieve as pens
import textacy
from collections import defaultdict
from random import random
Explanation: Attempting human-like speach: Markov chains
In orderto make the activitiy sentences in our memory more human-like, we can attempt to build a simple chatbot from the tex as well. A simple, and m... |
4,097 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
lesson1-rxt50-CA.ipynb -- Code Along of
Step1: Just looking at the fastai library source code while the above works
Step2: Hah! How about that. That settles that mystery. Up above you'll s... | Python Code:
# Put these at the top of every notebook, to get automatic reloading and inline plotting
%reload_ext autoreload
%autoreload 2
%matplotlib inline
# This file contains all the main external libs we'll use
from fastai.imports import *
from fastai.transforms import *
from fastai.conv_learner import *
from fast... |
4,098 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Adding Parameters With REBOUNDx
We start by creating a simulation, attaching REBOUNDx, and adding the effects of general relativity
Step1: The documentation page https
Step2: We would now ... | Python Code:
import rebound
import reboundx
sim = rebound.Simulation()
sim.add(m=1.)
sim.add(a=1.)
ps = sim.particles
rebx = reboundx.Extras(sim)
gr = rebx.load_force('gr')
rebx.add_force(gr)
Explanation: Adding Parameters With REBOUNDx
We start by creating a simulation, attaching REBOUNDx, and adding the effects of ge... |
4,099 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Метод главных компонент
В данном задании вам будет предложено ознакомиться с подходом, который переоткрывался в самых разных областях, имеет множество разных интерпретаций, а также несколько... | Python Code:
import numpy as np
import pandas as pd
import matplotlib
from matplotlib import pyplot as plt
import matplotlib.patches as mpatches
matplotlib.style.use('ggplot')
%matplotlib inline
Explanation: Метод главных компонент
В данном задании вам будет предложено ознакомиться с подходом, который переоткрывался в ... |
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