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7,400 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PyNNDescent with different metrics
In the initial tutorial we looked at how to get PyNNDescent running on your data, and how to query the indexes it builds. Implicit in all of that was the m... | Python Code:
import pynndescent
import numpy as np
import h5py
from urllib.request import urlretrieve
import os
def get_ann_benchmark_data(dataset_name):
if not os.path.exists(f"{dataset_name}.hdf5"):
print(f"Dataset {dataset_name} is not cached; downloading now ...")
urlretrieve(f"http://ann-benchm... |
7,401 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Study of Correlation Between Building Demolition and Associated Features
Capstone Project for Data Science at Scale on Coursera
Repo is located here
Chen Yang yangcnju@gmail.com
Step1: Obje... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import Image
%matplotlib inline
Explanation: Study of Correlation Between Building Demolition and Associated Features
Capstone Project for Data Science at Scale on Coursera
Repo is located here
Chen Yang yangcnju@gm... |
7,402 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
First BigQuery ML models for Taxifare Prediction
Learning Objectives
* Choose the correct BigQuery ML model type and specify options
* Evaluate the performance of your ML model
* Impro... | Python Code:
PROJECT = !gcloud config get-value project
PROJECT = PROJECT[0]
BUCKET = PROJECT
REGION = "us-central1"
%env PROJECT=$PROJECT
%env BUCKET=$BUCKET
%env REGION=$REGION
Explanation: First BigQuery ML models for Taxifare Prediction
Learning Objectives
* Choose the correct BigQuery ML model type and specify o... |
7,403 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: TV Script Generation
In this project, you'll generate your own Simpsons TV scripts using RNNs. You'll be using part of the Simpsons dataset of scripts from 27 seasons. The Neural Ne... | Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV script... |
7,404 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="http
Step1: Example 2 | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from landlab import RasterModelGrid
from landlab.components import SimpleSubmarineDiffuser
grid = RasterModelGrid((3, 51)) # grid has just one row of core nodes
# Close top and bottom boundaries
grid.set_closed_boundaries_at_grid_edges(False, True, False,... |
7,405 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Import
Step1: Reading initial data
Step2: Define label
label = signB * signVtx > 0
* same sign of B and vtx -> label = 1
* opposite sign of B and vtx -> label = 0
Step3: Define B-like eve... | Python Code:
import pandas
import numpy
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_curve, roc_auc_score
from rep.metaml import FoldingClassifier
from rep.data import LabeledDataStorage
from rep.report import ClassificationReport
from rep.report.metrics import RocAuc
from utils i... |
7,406 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
01 - Data Analysis and Preparation
This notebook covers the following tasks
Step1: Setup Google Cloud project
Step2: Set configurations
Step3: 1. Explore the data in BigQuery
Step4: 2. C... | Python Code:
import os
import pandas as pd
import tensorflow as tf
import tensorflow_data_validation as tfdv
from google.cloud import bigquery
import matplotlib.pyplot as plt
from google.cloud import aiplatform as vertex_ai
Explanation: 01 - Data Analysis and Preparation
This notebook covers the following tasks:
Perfor... |
7,407 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exploring the Lorenz System of Differential Equations
In this Notebook we explore the Lorenz system of differential equations
Step2: Computing the trajectories and plotting the result
We de... | Python Code:
%matplotlib inline
from ipywidgets import interact, interactive
from IPython.display import clear_output, display, HTML
import numpy as np
from scipy import integrate
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib.colors import cnames
from matplotlib import ani... |
7,408 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sigma to Pressure Interpolation
By using metpy.calc.log_interp, data with sigma as the vertical coordinate can be
interpolated to isobaric coordinates.
Step1: Data
The data for this example... | Python Code:
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
from netCDF4 import Dataset, num2date
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, add_timestamp
from metpy.units import units
Explanation: Sigma to Pre... |
7,409 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Index - Back - Next
Widget List
Complete list
For a complete list of the widgets available to you, you can list the classes in the widget namespace (as seen below). Widget and DOMWidget, no... | Python Code:
from IPython.html import widgets
[n for n in dir(widgets) if not n.endswith('Widget') and n[0] == n[0].upper() and not n[0] == '_']
Explanation: Index - Back - Next
Widget List
Complete list
For a complete list of the widgets available to you, you can list the classes in the widget namespace (as seen below... |
7,410 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<div style="width
Step1: Next we create an instance of the RadarServer object to point at one of these collections. This downloads some top level metadata and sets things up so we can easil... | Python Code:
from siphon.catalog import TDSCatalog
cat = TDSCatalog('http://thredds.ucar.edu/thredds/radarServer/catalog.xml')
list(cat.catalog_refs)
Explanation: <div style="width:1000 px">
<div style="float:right; width:98 px; height:98px;">
<img src="https://raw.githubusercontent.com/Unidata/MetPy/master/metpy/plots... |
7,411 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Project 2
Step1: Now, can you find out the following facts about the dataset?
- Total number of students
- Number of students who passed
- Number of students who failed
- Graduation rate of... | Python Code:
# Import libraries
import numpy as np
import pandas as pd
# Read student data
student_data = pd.read_csv("student-data.csv")
print "Student data read successfully!"
# Note: The last column 'passed' is the target/label, all other are feature columns
student_data.head()
Explanation: Project 2: Supervised Lea... |
7,412 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Motor Controller Sizing
There are a lot of things that go into sizing a motor controller. This will look at the torque needed to climb an incline. From that, and some data found on the inter... | Python Code:
from __future__ import division
from __future__ import print_function
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from math import sin, pi, tan
Explanation: Motor Controller Sizing
There are a lot of things that go into sizing a motor controller. This will look at the torque neede... |
7,413 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1> 2c. Loading large datasets progressively with the tf.data.Dataset </h1>
In this notebook, we continue reading the same small dataset, but refactor our ML pipeline in two small, but sign... | Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.5
from google.cloud import bigquery
import tensorflow as tf
import numpy as np
import shutil
print(tf.__version__)
Explanation: <h1> 2c. Loading large d... |
7,414 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Setup data directory
Step1: Download database files
Step2: download a small test dataset
ATT | Python Code:
cd /usr/local/notebooks
mkdir -p ./data
cd ./data
Explanation: Setup data directory
End of explanation
!wget https://s3.amazonaws.com/ssusearchdb/SSUsearch_db.tgz
!tar -xzvf SSUsearch_db.tgz
Explanation: Download database files
End of explanation
!wget https://s3.amazonaws.com/ssusearchdb/test.tgz
!tar -xz... |
7,415 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Scientific Computing with Python
This is a course about applying computer programming to problems of modeling physical systems and analysing data related to those systems. We'll be using a p... | Python Code:
#
# When the cursor is in this cell hit "shift enter" to execute the python code here
#
x=3 # x is assigned an integer value of 3
y=2.4 # y is assigned a floating point value of 2.4
print("x and y are:", x, 'and',y)
Explanation: Scientific Computing with Python
This is a course about applying compute... |
7,416 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Color palette with python
Germain Salvato Vallverdu germain.vallverdu@univ-pau.fr
This notebook aims to present several ways to manage color palette with python, mainly for plot purpose.
Imp... | Python Code:
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from IPython.display import HTML # intégration notebook
%matplotlib inline
Explanation: Color palette with python
Germain Salvato Vallverdu germain.vallverdu@univ-pau.fr
This notebook aims to present several ways to manage color pa... |
7,417 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ch 07
Step1: Instead of feeding all the training data to the training op, we will feed data in small batches
Step2: Define the autoencoder class
Step3: The Iris dataset is often used as a... | Python Code:
import tensorflow as tf
import numpy as np
Explanation: Ch 07: Concept 01
Autoencoder
All we'll need is TensorFlow and NumPy:
End of explanation
def get_batch(X, size):
a = np.random.choice(len(X), size, replace=False)
return X[a]
Explanation: Instead of feeding all the training data to the trainin... |
7,418 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Table of Contents
<p><div class="lev1 toc-item"><a href="#Building-an-ANN" data-toc-modified-id="Building-an-ANN-1"><span class="toc-item-num">1 </span>Building an ANN</a></div><d... | Python Code:
# Installing Theano
# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
# Installing Tensorflow
# pip install tensorflow
# Installing Keras
# pip install --upgrade keras
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Building-an-ANN" data-toc-modified-id="Buildi... |
7,419 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
RTA workload
The RTA or RTApp workload represents a type of workload obtained using the rt-app test application.
More details on the test application can be found at https
Step1: Test envir... | Python Code:
import logging
from conf import LisaLogging
LisaLogging.setup()
# Generate plots inline
%pylab inline
import json
import os
# Support to initialise and configure your test environment
import devlib
from env import TestEnv
# Support to configure and run RTApp based workloads
from wlgen import RTA, Periodic,... |
7,420 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example 1
Step1: To download data, we need to identify the relevant tables containing the variables of interest to us.
One way to do this would be to refer to the ACS documentation, in part... | Python Code:
import pandas as pd
import censusdata
pd.set_option('display.expand_frame_repr', False)
pd.set_option('display.precision', 2)
Explanation: Example 1: Downloading Block Group Data and Exporting to CSV
As a first example, let's suppose we're interested in unemployment and high school dropout rates
for block ... |
7,421 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example data
Step1: VectorAssembler
To fit a ML model in pyspark, we need to combine all feature columns into one single column of vectors
Step2: Assemble feature columns into one single f... | Python Code:
import pandas as pd
pdf = pd.DataFrame({
'x1': ['a','a','b','b', 'b', 'c'],
'x2': ['apple', 'orange', 'orange','orange', 'peach', 'peach'],
'x3': [1, 1, 2, 2, 2, 4],
'x4': [2.4, 2.5, 3.5, 1.4, 2.1,1.5],
'y1': [1, 0, 1, 0, 0, 1],
'y2': ['yes', 'no', 'no', 'yes... |
7,422 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Passband Luminosity
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab).
Step1: And w... | Python Code:
#!pip install -I "phoebe>=2.3,<2.4"
import phoebe
from phoebe import u # units
import numpy as np
logger = phoebe.logger()
b = phoebe.default_binary()
Explanation: Passband Luminosity
Setup
Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online... |
7,423 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<header class="w3-container w3-teal">
<img src="images/utfsm.png" alt="" height="100px" align="left"/>
<img src="images/mat.png" alt="" height="100px" align="right"/>
</header>
<br/><br/><br... | Python Code:
# Configuracion para recargar módulos y librerías
%reload_ext autoreload
%autoreload 2
%matplotlib inline
from IPython.display import Image as ShowImage
from IPython.core.display import HTML
HTML(open("style/mat281.css", "r").read())
from mat281_code.lab import greetings
alumno_1 = ("Sebastian Flores", "2... |
7,424 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Running Tune experiments with HEBOSearch
In this tutorial we introduce HEBO, while running a simple Ray Tune experiment. Tune’s Search Algorithms integrate with ZOOpt and, as a result, allow... | Python Code:
# !pip install ray[tune]
!pip install HEBO==0.3.2
Explanation: Running Tune experiments with HEBOSearch
In this tutorial we introduce HEBO, while running a simple Ray Tune experiment. Tune’s Search Algorithms integrate with ZOOpt and, as a result, allow you to seamlessly scale up a HEBO optimization proces... |
7,425 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Dual CRISPR Screen Analysis
Count Combination
Amanda Birmingham, CCBB, UCSD (abirmingham@ucsd.edu)
Instructions
To run this notebook reproducibly, follow these steps
Step1: CCBB Library Imp... | Python Code:
g_timestamp = ""
g_dataset_name = "20160510_A549"
g_count_alg_name = "19mer_1mm_py"
g_fastq_counts_dir = '/Users/Birmingham/Repositories/ccbb_tickets/20160210_mali_crispr/data/interim/20160510_D00611_0278_BHK55CBCXX_A549'
g_fastq_counts_run_prefix = "19mer_1mm_py_20160615223822"
g_collapsed_counts_dir = "/... |
7,426 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Train an Agent using Generative Adversarial Imitation Learning
The idea of generative adversarial imitation learning is to train a discriminator network to distinguish between expert traject... | Python Code:
from stable_baselines3 import PPO
from stable_baselines3.ppo import MlpPolicy
import gym
import seals
env = gym.make("seals/CartPole-v0")
expert = PPO(
policy=MlpPolicy,
env=env,
seed=0,
batch_size=64,
ent_coef=0.0,
learning_rate=0.0003,
n_epochs=10,
n_steps=64,
)
expert.lea... |
7,427 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sympy is a Python package used for solving equations using symbolic math.
Let's solve the following problem with SymPy.
Given
Step1: We need to define six different symbols
Step2: Next w... | Python Code:
from sympy import symbols, nonlinsolve
Explanation: Sympy is a Python package used for solving equations using symbolic math.
Let's solve the following problem with SymPy.
Given:
The density of two different polymer samples $\rho_1$ and $\rho_2$ are measured.
$$ \rho_1 = 0.904 \ g/cm^3 $$
$$ \rho_2 = 0.... |
7,428 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
新增刪修格式
GET - 查詢 (Retrieve)
```
r = requests.get(url, params=API_KEY)
query by record ID
Step1: 查詢單筆資料
Step2: 新增資料
Step3: 刪除資料
Step4: 修改資料 | Python Code:
query_string = {'api_key':'keyshdNC8CZdj1xgo', 'maxRecords':'100', 'pageSize':'2',
'filterByFormula':'{屬種} = "new type"'} #pageSize:一個頁面顯示(取回)幾筆資料
r = requests.get(url, params=query_string)
r.status_code
r.text
Explanation: 新增刪修格式
GET - 查詢 (Retrieve)
```
r = requests.get(url, params=API_KEY... |
7,429 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
局部异常因子方法发现异常点
局部异常因子(Local Outlier Factor,LOF)也是一种异常检测算法，它对数据实例的局部密度和邻居进行比较，判断这个数据是否属于相似的密度的区域，它适合从那些簇个数未知，簇的密度和大小各不相同的数据中筛选出异常点。
从k近邻算法启发来
Step1: 局部异常因子计算出每个点的局部密度，通过它与K最近邻的点的距离来评估点的局部密度... | Python Code:
from collections import defaultdict
import numpy as np
import matplotlib.pyplot as plt
instance = np.matrix([[0,0],[0,1],[1,1],[1,0],[5,0]])
x = np.squeeze(np.asarray(instance[:,0]))
y = np.squeeze(np.asarray(instance[:,1]))
plt.cla()
plt.figure(1)
plt.scatter(x,y)
plt.show()
Explanation: 局部异常因子方法发现异常点
局部异... |
7,430 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Why EP-ABC can produce too narrow posteriors
When you use EP-ABC for inference you may notice that your posterior distributions appear suspiciously narrow, i.e., you may not belief the certa... | Python Code:
def plot_mean_with_std(mean, std, std_mult=2, xvals=None, ax=None):
if xvals is None:
xvals = np.arange(mean.shape[0])
if ax is None:
ax = plt.axes()
ax.plot(mean, 'k', lw=3)
ax.fill_between(xvals, mean + std_mult*std, mean - std_mult*std,
edgec... |
7,431 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Examples of propagating uncertainties in mocsy
<hr>
James Orr - 11 November 2018<br>
<img align="left" width="60%" src="http
Step1: 1.2 Import standard python libraries
Step2: 1.3 Import m... | Python Code:
%%bash
pwd
mkdir code
cd code
git clone https://github.com/jamesorr/mocsy.git
cd mocsy
make
pwd
Explanation: Examples of propagating uncertainties in mocsy
<hr>
James Orr - 11 November 2018<br>
<img align="left" width="60%" src="http://www.lsce.ipsl.fr/Css/img/banniere_LSCE_75.png" ><br><br>
LSCE/IPSL, CEA... |
7,432 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ATM 623
Step1: Contents
A single layer atmosphere
Introducing the two-layer grey gas model
Tuning the grey gas model to observations
Level of emission
Radiative forcing in the 2-layer grey ... | Python Code:
# Ensure compatibility with Python 2 and 3
from __future__ import print_function, division
Explanation: ATM 623: Climate Modeling
Brian E. J. Rose, University at Albany
Lecture 7: Elementary greenhouse models
Warning: content out of date and not maintained
You really should be looking at The Climate Labor... |
7,433 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Table of Contents
<p><div class="lev2 toc-item"><a href="#start" data-toc-modified-id="start-01"><span class="toc-item-num">0.1 </span>start</a></div><div class="lev2 toc-item"><a... | Python Code:
# which *sites
% ll ../*sites
Explanation: Table of Contents
<p><div class="lev2 toc-item"><a href="#start" data-toc-modified-id="start-01"><span class="toc-item-num">0.1 </span>start</a></div><div class="lev2 toc-item"><a href="#Bootstrap-regions-file" data-toc-modified-id="Bootstrap-regions-fi... |
7,434 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Table of Contents
<p><div class="lev1 toc-item"><a href="#Summary" data-toc-modified-id="Summary-1"><span class="toc-item-num">1 </span>Summary</a></div><div class="lev1 toc-item"... | Python Code:
%run ../../code/version_check.py
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Summary" data-toc-modified-id="Summary-1"><span class="toc-item-num">1 </span>Summary</a></div><div class="lev1 toc-item"><a href="#Version-Control" data-toc-modified-id="Version-Control-2"><s... |
7,435 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This IPython notebook illustrates how to down sample two large tables that are loaded in the memory
Step1: Down sampling is typically done when the input tables are large (e.g. each contain... | Python Code:
import py_entitymatching as em
Explanation: This IPython notebook illustrates how to down sample two large tables that are loaded in the memory
End of explanation
# Read the CSV files
A = em.read_csv_metadata('./citeseer.csv',low_memory=False) # setting the parameter low_memory to False to speed up loadin... |
7,436 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
FashionMNIST classification with Multilayer perceptrons
https
Step1: Preparing the dataset
Download and convert to float
Step2: IMPORTANT
Step3: Understanding the sizes of the data
Make s... | Python Code:
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from torchvision import datasets
import torch
import torch.nn as nn
import torch.optim as optim
Explanation: FashionMNIST classification with Multilayer perceptrons
https://github.com/zalandoresearch/fashion-mnist
Exercises
Try changi... |
7,437 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PyTimeSeries
Test for pytimeseries package
Importamos la librería
Step1: Preparación de los datos
pytimeseries recibe una serie de pandas para analizar. A continuación se muestra la prepara... | Python Code:
import pytimeseries
import pandas
import matplotlib
Explanation: PyTimeSeries
Test for pytimeseries package
Importamos la librería
End of explanation
tserie = pandas.read_csv('champagne.csv', index_col='Month')
print(tserie)
Explanation: Preparación de los datos
pytimeseries recibe una serie de pandas para... |
7,438 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hackathon de Nanterre
17 octobre 2015
Coder la ville...
... en Python
Step1: Lecture des données relatives aux acteurs du numérique (www.datea.pe)
Step2: Lecture des données relatives aux ... | Python Code:
import pandas as pnd
import matplotlib.pylab as plt
import matplotlib.patches as mpatches
from IPython.display import HTML
%matplotlib inline
img = plt.imread("rueildigital.jpg")
plt.axis('off')
plt.imshow(img);
Explanation: Hackathon de Nanterre
17 octobre 2015
Coder la ville...
... en Python
End of expla... |
7,439 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Fully-Connected Neural Nets
In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since t... | Python Code:
# As usual, a bit of setup
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solver import Solver
%matplot... |
7,440 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
If you don't care about the confidence interval of parameter
Step1: If you want the confidence intervals | Python Code:
from lmfit.models import GaussianModel
# initialize the gaussian model
gm = GaussianModel()
# take a look at the parameter names
print gm.param_names
# I get RuntimeError since my numpy version is a little old
# guess parameters
par_guess = gm.guess(n,x=xpos)
# fit data
result = gm.fit(n, par_guess, x=xpos... |
7,441 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The
Step1: Here for convenience we read the evoked dataset from a file.
Step2: Notice that the reader function returned a list of evoked instances. This is
because you can store multiple ... | Python Code:
import os.path as op
import mne
Explanation: The :class:Evoked <mne.Evoked> data structure: evoked/averaged data
The :class:Evoked <mne.Evoked> data structure is mainly used for storing
averaged data over trials. In MNE the evoked objects are usually created by
averaging epochs data with :func:... |
7,442 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Label and feature engineering
Learning objectives
Step1: Create time-series features and determine label based on market movement
Summary of base tables
Step2: Label engineering
Ultimately... | Python Code:
PROJECT = !(gcloud config get-value core/project)
PROJECT = PROJECT[0]
import pandas as pd
from google.cloud import bigquery
from IPython import get_ipython
from IPython.core.magic import register_cell_magic
bq = bigquery.Client(project=PROJECT)
# Allow you to easily have Python variables in SQL query.
@re... |
7,443 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Old version that was developed here (now probably out of sync with official version in PISA...)
Step1: Testing out a container class but that | Python Code:
print hash_obj([0, 1, 2])
bits = 64*2
n_elements = 200
np.log10(2*2**bits/(n_elements*(n_elements-1)))
Explanation: Old version that was developed here (now probably out of sync with official version in PISA...):
python
def hash_obj(obj):
hash_val, = struct.unpack('<q', hashlib.md5(
pick... |
7,444 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Galaxies that are missing from Simard+2011
Summary
* A total of 44 galaxies are not in galfit sample
* 31/44 are not in the SDSS catalog, so these would not have been targeted by Simard+2011... | Python Code:
%run ~/Dropbox/pythonCode/LCSanalyzeblue.py
t = s.galfitflag & s.lirflag & s.sizeflag & ~s.agnflag & s.sbflag
galfitnogim = t & ~s.gim2dflag
sum(galfitnogim)
Explanation: Galaxies that are missing from Simard+2011
Summary
* A total of 44 galaxies are not in galfit sample
* 31/44 are not in the SDSS catalog... |
7,445 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1.9 查找两字典的相同点
怎样在两字典中寻找相同点（相同的key or 相同的 value）
Step1: 为寻找两字典的相同点 可通过简单的在两字典keys() or items() method 中Return 结果 进行set 操作
Step2: 以上操作亦可用于修改or过滤dict element <br> if you want 以现有dict 来构造一个... | Python Code:
a = {
'x':1,
'y':2,
'z':3
}
b = {
'w':10,
'x':11,
'y':2
}
# In a ::: x : 1 ,y : 2
# In b ::: x : 11,y : 2
Explanation: 1.9 查找两字典的相同点
怎样在两字典中寻找相同点（相同的key or 相同的 value）
End of explanation
# Find keys in common
kc = a.keys() & b.keys()
print('a 和 b 共有的键',kc)
# Find keys in a that are n... |
7,446 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Purchase frequency
In this notebook we create a table grouping transaction information by customers’ purchase frequency. This is done with functions from the pandas librairie such as df.grou... | Python Code:
import pandas as pd
import contiamo
Explanation: Purchase frequency
In this notebook we create a table grouping transaction information by customers’ purchase frequency. This is done with functions from the pandas librairie such as df.groupby() and df.cut().
End of explanation
transactions = %contiamo quer... |
7,447 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: CMU Mocap Database
Motion capture data from the CMU motion capture data base (CMU Motion
Capture Lab, 2003).
You can download any subject and motion from the data set.... | Python Code:
import matplotlib.pyplot as plt
plt.style.use("seaborn-pastel")
%%capture
%pip install --upgrade git+https://github.com/lawrennd/ods
%pip install --upgrade git+https://github.com/SheffieldML/GPy.git
import GPy, pods
import numpy as np
np.random.seed(42)
Explanation: <a href="https://colab.research.google.c... |
7,448 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cookbook
Step1: Import Python libraries
The call to %matplotlib inline here enables support for plotting directly inside the notebook.
Step2: Quick guide (tldr;)
The following cell shows t... | Python Code:
## conda install ipyrad -c ipyrad
## conda install -c conda-forge scikit-allel
Explanation: Cookbook: PCA analyses
As part of the ipyrad.analysis toolkit we've created convenience functions for easily performing exploratory principal component analysis (PCA) on your data. PCA is a very standard dimension-r... |
7,449 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example 7 - Refining a triangulation
We have seen how the standard meshes can be uniformly refined to finer resolution. The routines used for this task are available to the stripy user for n... | Python Code:
import stripy as stripy
import numpy as np
Explanation: Example 7 - Refining a triangulation
We have seen how the standard meshes can be uniformly refined to finer resolution. The routines used for this task are available to the stripy user for non-uniform refinement as well.
Notebook contents
Uniform mes... |
7,450 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Rulefit Boston Housing Demo
Rulefit algorithm aims for a compromise between interpretability and complexity of the resulting model. While simpler ML algorithms usually miss interaction effec... | Python Code:
import h2o
from h2o.estimators.random_forest import H2ORandomForestEstimator
h2o.init()
train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/BostonHousing.csv")
x = train.columns
y = "medv"
x.remove(y)
x.remove("b")
train['medv'].hist()
train.head()
Explanation: Rulefit... |
7,451 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
3
Step1: Answer
Step2: 4
Step3: Answer
Step4: 5
Step5: 6
Step6: 7
Step7: 10 | Python Code:
import math
Explanation: 3: The math module
Instructions
Use the sqrt() function within the math module to assign the square root of 16.0 to a.
Use the ceil() function within the math module to assign the ceiling of 111.3 to b.
Use the floor() function within the math module to assign the floor of 89.9 to ... |
7,452 | Given the following text description, write Python code to implement the functionality described.
Description:
You'll be given a string of words, and your task is to count the number
of boredoms. A boredom is a sentence that starts with the word "I".
Sentences are delimited by '.', '?' or '!'.
For e... | Python Code:
def is_bored(S):
import re
sentences = re.split(r'[.?!]\s*', S)
return sum(sentence[0:2] == 'I ' for sentence in sentences) |
7,453 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Text Using Markdown
If you double click on this cell, you will see the text change so that all of the formatting is removed. This allows you to edit this block of text. This block of text is... | Python Code:
# Hit shift + enter or use the run button to run this cell and see the results
print('hello world')
# The last line of every code cell will be displayed by default,
# even if you don't print it. Run this cell to see how this works.
2 + 2 # The result of this line will not be displayed
3 + 3 # The result o... |
7,454 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
External data
Helper functions to download the fastai datasets
To download any of the datasets or pretrained weights, simply run untar_data by passing any dataset name mentioned above like s... | Python Code:
#|export
@lru_cache(maxsize=None)
def fastai_cfg() -> Config: # Config that contains default download paths for `data`, `model`, `storage` and `archive`
"`Config` object for fastai's `config.ini`"
return Config(Path(os.getenv('FASTAI_HOME', '~/.fastai')), 'config.ini', create=dict(
data = '... |
7,455 | 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... |
7,456 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Does RFT FDR control the nominal FDR value?
In this notebook, I made a small simulation, to verify that the RFT peak FDR procedure does not control the overall FDR but the conditional FDR (f... | Python Code:
% matplotlib inline
import os
import numpy as np
import nibabel as nib
from nipy.labs.utils.simul_multisubject_fmri_dataset import surrogate_3d_dataset
import nipy.algorithms.statistics.rft as rft
from __future__ import print_function, division
import math
import matplotlib.pyplot as plt
import palettable.... |
7,457 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Root Finding and Optimization
GOAL
Step3: Fixed Point Iteration
How do we go about solving this?
Could try to solve at least partially for $r$
Step4: Guess at $r_0$ and check to see... | Python Code:
def total_value(P, m, r, n):
Total value of portfolio given parameters
Based on following formula:
A = \frac{P}{(r / m)} \left[ \left(1 + \frac{r}{m} \right)^{m \cdot n}
- 1 \right ]
:Input:
- *P* (float) - Payment amount per compounding period
- *m... |
7,458 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Use Case 3
Step1: Open MODIS/Aqua files with chlorophyll in the North Sea and fetch data
Step2: Plot chlorophyll-a maps in swath projection
Step3: Colocate data. Reproject both images ont... | Python Code:
# download sample files
!wget -P data -nc ftp://ftp.nersc.no/nansat/test_data/obpg_l2/A2015121113500.L2_LAC.NorthNorwegianSeas.hdf
!wget -P data -nc ftp://ftp.nersc.no/nansat/test_data/obpg_l2/A2015122122000.L2_LAC.NorthNorwegianSeas.hdf
import numpy as np
import matplotlib.pyplot as plt
from IPython.displ... |
7,459 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cloud Data Center
Step1: Data Center IP Traffic | Python Code:
import matplotlib.pyplot as plt
import csv
a = []
b = []
with open('data/dc_hyperscale.csv','r') as csvfile:
plots = csv.reader(csvfile, delimiter=';')
for row in plots:
a.append(int(row[0]))
b.append(int(row[1]))
plt.bar(a,b, label='Hyperscale Data Centers')
plt.xlabel('A... |
7,460 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hybrid integrations with MERCURIUS
REBOUND comes with several integrators, each of which has its own advantages and disadvantages. MERCURIUS is a hybrid integrastor that is very similar to t... | Python Code:
import math
import rebound, rebound.data
%matplotlib inline
sim = rebound.Simulation()
rebound.data.add_outer_solar_system(sim) # add some particles for testing
for i in range(1,sim.N):
sim.particles[i].m *= 50.
sim.integrator = "WHFast" # This will end badly!
sim.dt = sim.particles[1].P * 0.002 # Time... |
7,461 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Traverse a Square - Part 1 - Simply Does It
In this notebook and the next, you will explore various strategies for programming a robot to traverse a regular two dimensional shape, such as a ... | Python Code:
%run 'Set-up.ipynb'
%run 'Loading scenes.ipynb'
%run 'vrep_models/PioneerP3DX.ipynb'
Explanation: Traverse a Square - Part 1 - Simply Does It
In this notebook and the next, you will explore various strategies for programming a robot to traverse a regular two dimensional shape, such as a square.
These strat... |
7,462 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
Here is my code: | Problem:
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
words = load_data()
count = CountVectorizer(lowercase=False, token_pattern='[a-zA-Z0-9$&+:;=@#|<>^*()%-]+')
vocabulary = count.fit_transform([words])
feature_names = count.get_feature_names_out() |
7,463 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Importing data
Getting data from kaggle first
Step1: Baseline model
Step2: Deep Forest
By Hand
Step3: This is not very handy, not at all. We already see a lot of code duplication, and on... | Python Code:
import pkg_resources
raw_data = pd.read_csv(pkg_resources.resource_stream('deepforest', 'data/train.csv'))
clean_data = raw_data.drop(["Cabin", "Name", "PassengerId", "Ticket"], axis=1)
clean_data = pd.get_dummies(clean_data).fillna(-1)
train, test = train_test_split(clean_data)
def split_x_y(dataframe, ta... |
7,464 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to Basic Stellar Photometry
Measuring Flux in 1D
Version 0.1
In this notebook we will introduce some basic concepts related to measuring the flux of a point source. As this is a... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
Explanation: Introduction to Basic Stellar Photometry
Measuring Flux in 1D
Version 0.1
In this notebook we will introduce some basic concepts related to measuring the flux of a point source. As this is an introduction, several challeng... |
7,465 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step2: Compare CoNLL files against themselves
We're now using the official CoNLL scorer to compare each
MAZ176 coreference annotated document against itself.
All comparisons should result in... | Python Code:
import sys
def has_valid_annotation(mmax_file, scorer_path, metric, verbose=False):
Parameters
----------
metric : str
muc, bcub, ceafm, ceafe, blanc
verbose : bool or str
True, False or 'very'
scorer = sh.Command(scorer_path)
mdg = MMAXDocumentGraph(mmax_f... |
7,466 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exponential Weighted Moving Average (EWMA)
Pro jednoduchost výpočtu patří exponenciální vážený klouzavý průměr (EWMA) k hojně využívaným nástrojům pro analýzu dat, machine learning, atd. Nar... | Python Code:
import datetime
start = datetime.datetime(2018, 1, 1)
end = datetime.datetime(2019, 1, 1)
spy_data = pdr.data.DataReader('SPY', 'yahoo', start, end)
spy_data.drop(['High', 'Low', 'Open', 'Close', 'Volume'], axis=1, inplace=True) # these columns are not needed
spy_data.head(5)
Explanation: Exponential Weigh... |
7,467 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Doppler shifts
Exploring doppler shift on precision and quality. Specifically in the K-band
Step1: Applying doppler shifts of $+/- 200$ km/s only produce changes of $< \pm0.1$ m/s for condi... | Python Code:
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
import PyAstronomy.pyasl as pyasl
from astropy import constants as const
from eniric import config
import eniric
# config.cache["location"] = None # Disable caching for these tests
config.cache["location"] = ".joblib" # Enable cachi... |
7,468 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Initialize set up
Amplifyer is fet -15V and = +85V
Calibrate strain gauge to zero with the kinesis software
Pull fiber back
Set nicard to -3.75
Step1: Move close with fiber | Python Code:
mynicard._write_cavity_ao(np.array([0.0],dtype=float), start=True)
Explanation: Initialize set up
Amplifyer is fet -15V and = +85V
Calibrate strain gauge to zero with the kinesis software
Pull fiber back
Set nicard to -3.75
End of explanation
first_resonances = cavitylogic.get_nth_full_sweep(sweep_number=... |
7,469 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sentiment
Polyglot has polarity lexicons for 136 languages.
The scale of the words' polarity consisted of three degrees
Step1: Polarity
To inquiry the polarity of a word, we can just call i... | Python Code:
from polyglot.downloader import downloader
print(downloader.supported_languages_table("sentiment2", 3))
from polyglot.text import Text
Explanation: Sentiment
Polyglot has polarity lexicons for 136 languages.
The scale of the words' polarity consisted of three degrees: +1 for positive words, and -1 for nega... |
7,470 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Для удобного получения твитов пользователей будем использовать библиотеку tweepy.
Step1: Вводим данные разработчика.
Step2: Получаем доступ к Twitter API.
Step3: Теперь считаем твиты кажд... | Python Code:
import warnings
warnings.filterwarnings('ignore')
import tweepy
Explanation: Для удобного получения твитов пользователей будем использовать библиотеку tweepy.
End of explanation
access_token = ""
access_token_secret = ""
consumer_key = ""
consumer_secret = ""
# Чтобы получить данные разработчика, нужно зар... |
7,471 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Parcel loading
Given a set of parcels (assumes GDB format) from the parcel provider, this notebook will load individual features (from the parcel provider -- currently each feature-type is a... | Python Code:
import psycopg2 as pg
import pandas as pd
import os
conn = pg.connect('service=parcels')
conn_str = os.environ.get('PARCELS_CONNECTION')
Explanation: Parcel loading
Given a set of parcels (assumes GDB format) from the parcel provider, this notebook will load individual features (from the parcel provider --... |
7,472 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Simple Rotations
Author
Step1: Use the Q8 class that places these 4 numbers in 8 slots like so
Step3: If you are unfamiliar with this notation, the $I^2 = -1,\, i^3=-i,\, j^3=-j,\, k^3=-k$... | Python Code:
%%capture
from Q_tool_devo import Q8;
U=Q8([1,2,-3,4])
V=Q8([4,-2,3,1])
R=Q8([5,6,7,-8])
Explanation: Simple Rotations
Author: Doug sweetser@alum.mit.edu
A deep leason from special relativity is that all me... |
7,473 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Spectral clustering for image segmentation
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the
Step... | Python Code:
print(__doc__)
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.feature_extraction import image
from sklearn.cluster import spectral_clust... |
7,474 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SPINSpy Tutorial (2D)
Step1: If we want to use our shiny python scripts, we'll need to import them too.
Step2: If we want a quick man-page style summary, we can call help(spy). We can also... | Python Code:
%matplotlib inline
# Tells the system to plot in-line, only necessary for iPython notebooks,
# not regular command-line python
import numpy as np
import os
import sys
import matplotlib.pyplot as plt
import time
# Now that we have our packages, we need data. The file 'make_2d_data.py' will
# generate a sam... |
7,475 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Beaming and Boosting
Due to concerns about accuracy, support for Beaming & Boosting has been disabled in the 2.2 release of PHOEBE.
It may come as surprise that support for Doppler boosting ... | Python Code:
import phoebe
import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits
Explanation: Beaming and Boosting
Due to concerns about accuracy, support for Beaming & Boosting has been disabled in the 2.2 release of PHOEBE.
It may come as surprise that support for Doppler boosting has been dr... |
7,476 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
Find core samples of high density and expand clusters from them.
The minimum number of samples in a neighborhood for a po... | Python Code:
import pandas as pd
df1=pd.read_csv('team_out_1.csv')
df2=pd.read_csv('team_out_a2.csv')
df=df1.append(df2)
df.dropna(inplace=True)
df.reset_index(inplace=True,drop=True)
df=df[df.Total_Expenses>0]
df=df[df.Program_Exp<=1]
df
Explanation: DBSCAN (Density-Based Spatial Clustering of Applications with Noise)... |
7,477 | 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@inria.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__)
Explanation: Non-... |
7,478 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Quickstart
Step1: DataFrame Creation
A PySpark DataFrame can be created via pyspark.sql.SparkSession.createDataFrame typically by passing a list of lists, tuples, dictionaries and pyspark.s... | Python Code:
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
Explanation: Quickstart: DataFrame
This is a short introduction and quickstart for the PySpark DataFrame API. PySpark DataFrames are lazily evaluated. They are implemented on top of RDDs. When Spark transforms data, it does not... |
7,479 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Importing data from LOBO-Buoy server
Objectives
Step1: NOTE
Step2: Exploring the data
Lets see what is in "data"...
Step3: Now lets disect "data" a bit... lets find the title (or "keys") ... | Python Code:
# URL quering the LOBO server for data (in this case, temperature data)
URL = 'http://lobo.satlantic.com/cgi-data/nph-data.cgi?min_date=20090610&max_date=20090706&y=temperature'
Explanation: Importing data from LOBO-Buoy server
Objectives:
* Download data from LOBO server
* (if needed) transform data into ... |
7,480 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Welcome to on road lane detection program
Programm has image processing pipeline that support both RGB images and BGR video input.
RGB image processing consists of next steps
Step1: Next cl... | Python Code:
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
import pandas
import os
def getPathFor(file_path):
current_directory = %pwd
path = os.path.join(current_directory, file_path)
print("About to open file: {}\n".format(path))
return path
Explan... |
7,481 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
write a program to train a machine learning model using support vector machines algorithm and print the predictions
| Python Code::
from sklearn import svm
model = svm.SVC()
model.fit(X_train, y_train)
pred = model.predict(X_test)
print(pred)
|
7,482 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Indico.io sentiment score analysis
Step1: Compute average sentiment score per week
make it 0.5 if no news that week.
Step2: read bitcoin price data
Step3: add news volume data
Step4: Alc... | Python Code:
score_data = pd.read_csv("../data/indico_nyt_bitcoin.csv", index_col='time',
parse_dates=[0], date_parser=lambda x: datetime.datetime.strptime(x, time_format))
score_data.head()
Explanation: Indico.io sentiment score analysis
End of explanation
weekly_score = score_data.resample('w', how... |
7,483 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Twitter Bots
A workshop by Dillon Niederhut and Juan Shishido.
Interacting with <a href="https
Step1: What you see
Step2: What you get
Step3: You have access to more than just the text.
J... | Python Code:
import json
with open('data/first_tweet.json','r') as f:
a_tweet = json.loads(f.read())
Explanation: Twitter Bots
A workshop by Dillon Niederhut and Juan Shishido.
Interacting with <a href="https://twitter.com/tob_pohskrow" target="_blank">tob_pohskrow</a>.
Bots
The W's: What, why, and probably even ho... |
7,484 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Showing some of the dstoolbox features
The purpose of this notebook is to demonstrate some of the features of dstoolbox using a toy dataset that contains different categories of features.
Im... | Python Code:
from functools import partial
import operator
import random
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import PolynomialFeatures
fro... |
7,485 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This tutorial will show you how to find the suitable habitat range for Bristlecone pine using GeoPySpark
This tutorial will focus on GeoPySpark functionality, but you can find more resources... | Python Code:
import geopyspark as gps
from pyspark import SparkContext
Explanation: This tutorial will show you how to find the suitable habitat range for Bristlecone pine using GeoPySpark
This tutorial will focus on GeoPySpark functionality, but you can find more resources and tutorials about GeoNotebooks here.
Suitab... |
7,486 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Continuous training with TFX and Google Cloud AI Platform
Learning Objectives
Use the TFX CLI to build a TFX pipeline.
Deploy a TFX pipeline version without tuning to a hosted AI Platform Pi... | Python Code:
import yaml
# Set `PATH` to include the directory containing TFX CLI and skaffold.
PATH=%env PATH
%env PATH=/home/jupyter/.local/bin:{PATH}
Explanation: Continuous training with TFX and Google Cloud AI Platform
Learning Objectives
Use the TFX CLI to build a TFX pipeline.
Deploy a TFX pipeline version witho... |
7,487 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Wasserstein Discriminant Analysis
This example illustrate the use of WDA as proposed in [11].
[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016).
Wasserstein Discriminant A... | Python Code:
# Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
import numpy as np
import matplotlib.pylab as pl
from ot.dr import wda, fda
Explanation: Wasserstein Discriminant Analysis
This example illustrate the use of WDA as proposed in [11].
[11] Flamary, R., Cuturi, M., Courty, N., & Rakotoma... |
7,488 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Comparing different movie rating systems
In this notebook, I use simple statistical metrics (mean, median, standard deviation and some quantiles) to analyze different movie rating systems. I... | Python Code:
import pandas as pd
import numpy as np
import scipy.stats as sps
import matplotlib.pyplot as plt
%matplotlib inline
movies = pd.read_csv('fandango_score_comparison.csv')
movies.describe()
movies.info()
# Check the movie data structure
movies.head()
Explanation: Comparing different movie rating systems
In t... |
7,489 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Drawdown caused by groundwater extraction
Developed by R.A. Collenteur & M. Bakker
In this example notebook it is shown how to simulate the effect of a pumping well on the groundwater levels... | Python Code:
import pandas as pd
import pastas as ps
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Drawdown caused by groundwater extraction
Developed by R.A. Collenteur & M. Bakker
In this example notebook it is shown how to simulate the effect of a pumping well on the groundwater levels. We will fir... |
7,490 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
2A.ml - Clustering
Ce notebook utilise les données des vélos de Chicago Divvy Data. Il s'inspire du challenge créée pour découvrir les habitudes des habitantes de la ville City Bike. L'idée ... | Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
Explanation: 2A.ml - Clustering
Ce notebook utilise les données des vélos de Chicago Divvy Data. Il s'inspire du challenge créée pour découvrir les habitudes des habitantes de la ville City Bike. L'idée est d'explorer plusie... |
7,491 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Creating MNE objects from data arrays
In this simple example, the creation of MNE objects from
numpy arrays is demonstrated. In the last example case, a
NEO file format is used as a source f... | Python Code:
# Author: Jaakko Leppakangas <jaeilepp@student.jyu.fi>
#
# License: BSD (3-clause)
import numpy as np
import neo
import mne
print(__doc__)
Explanation: Creating MNE objects from data arrays
In this simple example, the creation of MNE objects from
numpy arrays is demonstrated. In the last example case, a
NE... |
7,492 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
View in Colaboratory
Classifying Handwritting using Convolutional Neural Networks
In this example we are going to use PyTorch, a commonly used Deep Learning Framework to classify Handwritten... | Python Code:
!pip3 install http://download.pytorch.org/whl/cu80/torch-0.3.0.post4-cp36-cp36m-linux_x86_64.whl
!pip3 install torchvision
!pip3 install numpy
!pip3 install matplotlib
!pip3 install seaborn
Explanation: View in Colaboratory
Classifying Handwritting using Convolutional Neural Networks
In this example we are... |
7,493 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example 1 - Overfitting Sample Data
Here, we will use a simple model to overfit a set of randomly generated data points.
First, we import Numpy to hold the data, and we import Learny McLearn... | Python Code:
import numpy as np
import LearnyMcLearnface as lml
Explanation: Example 1 - Overfitting Sample Data
Here, we will use a simple model to overfit a set of randomly generated data points.
First, we import Numpy to hold the data, and we import Learny McLearnface.
End of explanation
test_data = np.random.randn(... |
7,494 | Given the following text description, write Python code to implement the functionality described.
Description:
Divide given numeric string into at most two increasing subsequences which form an increasing string upon concatenation
Function to check for valid subsequences ; Stores which element belongs to which subseque... | Python Code:
def findSubsequence(str ) :
n = len(str )
res =['0' for i in range(n ) ]
for pos in range(10 ) :
lst1 = '0'
flag = 1
lst2 = chr(pos + 48 )
for i in range(n ) :
if(lst2 <= str[i ] ) :
res[i ] = '2'
lst2 = str[i ]
elif(lst1 <= str[i ] ) :
res[i ] = '1'
lst1 = str[i ]
else... |
7,495 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Assignment 6
Step1: Importing data
Unfortunately, the USGS does not have any data from this year available from the Feather River or its tributaries. (For this river, it only makes data ava... | Python Code:
# Import numerical tools
import numpy as np
#Import pandas for reading in and managing data
import pandas as pd
# Import pyplot for plotting
import matplotlib.pyplot as plt
#Import seaborn (useful for plotting)
import seaborn as sns
# Magic function to make matplotlib inline; other style specs must come AF... |
7,496 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href='http
Step1: Bracket Indexing and Selection
The simplest way to pick one or some elements of an array looks very similar to python lists
Step2: Broadcasting
Numpy arrays differ fro... | Python Code:
import numpy as np
#Creating sample array
arr = np.arange(0,11)
#Show
arr
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
NumPy Indexing and Selection
In this lecture we will discuss how to select elements or groups of elements from an array.
End of explanation... |
7,497 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Corpora and Vector Spaces
Demonstrates transforming text into a vector space representation.
Also introduces corpus streaming and persistence to disk in various formats.
Step1: First, let’s... | Python Code:
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
Explanation: Corpora and Vector Spaces
Demonstrates transforming text into a vector space representation.
Also introduces corpus streaming and persistence to disk in various formats.
End of explanatio... |
7,498 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Risk Sharing and Moral Hazard
Step1: Risk-sharing review
A simple employment contract
Employer hires worker to perform a task that has a stochastic outcome. Project can either
Step2: Mora... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from ipywidgets import interact, fixed
Explanation: Risk Sharing and Moral Hazard
End of explanation
alpha = 0.25
def u(c, alpha=alpha):
return (1/alpha)*c**alpha
def E(x,p):
return p*x[1] + (1-p)*x[0]
def EU(c, p):
return p*... |
7,499 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2021 The TensorFlow Authors.
Step1: TFX Python function component tutorial
Note
Step2: Upgrade Pip
To avoid upgrading Pip in a system when running locally, check to make sure
tha... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dist... |
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