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2,900 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
An Introduction to py-Goldsberry
py-Goldsberry is a Python package that makes it easy to interface with the http
Step1: py-goldsberry is designed to work in conjuntion with Pandas. Each fun... | Python Code:
import goldsberry
import pandas as pd
goldsberry.__version__
Explanation: An Introduction to py-Goldsberry
py-Goldsberry is a Python package that makes it easy to interface with the http://stats.nba.com and retrieve the data in a more analyzable format.
This is the first in a series of tutorials that walk... |
2,901 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Loading data from Cloudant or CouchDB
You can load data from CouchDB or a managed Cloudant instance using the Cloudant Spark connector.
Prerequisites
Collect your database connection informa... | Python Code:
import pixiedust
pixiedust.enableJobMonitor()
Explanation: Loading data from Cloudant or CouchDB
You can load data from CouchDB or a managed Cloudant instance using the Cloudant Spark connector.
Prerequisites
Collect your database connection information: the database host, user name, password and source da... |
2,902 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Decision Trees in Practice
In this assignment we will explore various techniques for preventing overfitting in decision trees. We will extend the implementation of the binary decision trees ... | Python Code:
import graphlab
Explanation: Decision Trees in Practice
In this assignment we will explore various techniques for preventing overfitting in decision trees. We will extend the implementation of the binary decision trees that we implemented in the previous assignment. You will have to use your solutions from... |
2,903 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Neural Network for Image Classification
Step1: 2 - Dataset
You will use the same "Cat vs non-Cat" dataset as in "Logistic Regression as a Neural Network" (Assignment 2). The model you ... | Python Code:
import time
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy
from PIL import Image
from scipy import ndimage
from dnn_app_utils import *
%matplotlib inline
plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.... |
2,904 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Matplotlib Exercise 3
Imports
Step2: Contour plots of 2d wavefunctions
The wavefunction of a 2d quantum well is
Step3: The contour, contourf, pcolor and pcolormesh functions of Matplotlib ... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
Explanation: Matplotlib Exercise 3
Imports
End of explanation
import math as math
def well2d(x, y, nx, ny, L=1.0):
Compute the 2d quantum well wave function.
wave_funct = (2/L)*np.sin((nx*math.pi*x)/L)*np.sin((ny*math.pi*y)/L)
... |
2,905 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Precisione dei numeri floats
A seguito di un paio di domande fatte a lezione, vediamo la precisione dei numeri "reali" in Python.
I float in python corrispondono ai double in C e quindi son... | Python Code:
0.1+0.1+0.1-0.3
Explanation: Precisione dei numeri floats
A seguito di un paio di domande fatte a lezione, vediamo la precisione dei numeri "reali" in Python.
I float in python corrispondono ai double in C e quindi sono numeri in doppio precisione, e occupano in memoria 64 bits. Questo comporta un errore ... |
2,906 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Defining the Problem
Here we will derive the equations of motion for the classic mass-spring-damper system under the influence of gravity. The following figure gives a pictorial description ... | Python Code:
from IPython.display import SVG
SVG(filename='mass_spring_damper.svg')
Explanation: Defining the Problem
Here we will derive the equations of motion for the classic mass-spring-damper system under the influence of gravity. The following figure gives a pictorial description of the problem.
End of explanatio... |
2,907 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Finding similar documents with Word2Vec and Soft Cosine Measure
Soft Cosine Measure (SCM) [1, 3] is a promising new tool in machine learning that allows us to submit a query and return the m... | Python Code:
# Initialize logging.
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
Explanation: Finding similar documents with Word2Vec and Soft Cosine Measure
Soft Cosine Measure (SCM) [1, 3] is a promising new tool in machine learning that allows us to submit... |
2,908 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Word2Vec Tutorial
In case you missed the buzz, word2vec is a widely featured as a member of the “new wave” of machine learning algorithms based on neural networks, commonly referred to as "d... | Python Code:
# import modules & set up logging
import gensim, logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
sentences = [['first', 'sentence'], ['second', 'sentence']]
# train word2vec on the two sentences
model = gensim.models.Word2Vec(sentences, min_count=1)
Expla... |
2,909 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1> Machine Learning Model training and serving </h1>
The training architecture involves collecting data from the two sources, mashing them up with Dataflow and saving the results in BigQue... | Python Code:
%projects set ml-autoawesome
import os
PROJECT = 'ml-autoawesome' # CHANGE THIS
BUCKET = 'ml-autoawesome-cmle' # CHANGE THIS
REGION = 'us-central1' # CHANGE THIS
os.environ['PROJECT'] = PROJECT # for bash
os.environ['BUCKET'] = BUCKET # for bash
os.environ['REGION'] = REGION # for bash
%bash
echo "proj... |
2,910 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Q3
In this question, you'll go over some of the core terms and concepts in statistics.
Part A
Write a function, variance, which computes the variance of a list of numbers.
The function takes... | Python Code:
import numpy as np
np.random.seed(5987968)
x = np.random.random(8491)
v = x.var(ddof = 1)
np.testing.assert_allclose(v, variance(x))
np.random.seed(4159)
y = np.random.random(25)
w = y.var(ddof = 1)
np.testing.assert_allclose(w, variance(y))
Explanation: Q3
In this question, you'll go over some of the core... |
2,911 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Learn Posture
use machine learning to recognize robot's posture (following the example in scikit-learn-intro.ipynb )
1. Data collection
We have colleceted data before, you need to add new da... | Python Code:
%pylab inline
import pickle
from os import listdir, path
import numpy as np
from sklearn import svm, metrics
ROBOT_POSE_DATA_DIR = 'robot_pose_data'
classes = listdir(ROBOT_POSE_DATA_DIR)
print classes
def load_pose_data(i):
'''load pose data from file'''
data = []
target = []
# YOUR CODE H... |
2,912 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Predicting Who Will Leave and Why - Python Machine Learning
In this notebook, we do a brief exploration of our HR analytics data (found on Kaggle, which you can check for more info on the da... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
Explanation: Predicting Who Will Leave and Why - Python Machine Learning
In this notebook, we do a brief exploration of our HR analytics data (found on Kaggle, which you can check for more info o... |
2,913 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I want to process a gray image in the form of np.array. | Problem:
import numpy as np
im = np.array([[1,1,1,1,1,5],
[1,0,0,1,2,0],
[2,1,0,0,1,0],
[1,0,0,7,1,0],
[1,0,0,0,0,0]])
mask = im == 0
rows = np.flatnonzero((mask).sum(axis=1))
cols = np.flatnonzero((mask).sum(axis=0))
if rows.shape[0] == 0:
result = np.arr... |
2,914 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sensitivity analysis for L-Serine
In this example, the ammount of produced serine is increased in steps. The biomass production will decrease with increased accumulation of Serine. This is a... | Python Code:
ser__L = model.metabolites.ser__L_c
result = sensitivity_analysis(model, ser__L, is_essential=True, steps=10,
biomass=model.reactions.BIOMASS_Ec_iJO1366_core_53p95M)
result.data_frame
result.plot(width=700, height=500)
Explanation: Sensitivity analysis for L-Serine
In this exa... |
2,915 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
On Kepler 452
Kepler 452 is a solar-like star in the Kepler field that was recently announced to possess a planet with an orbit of 385 Earth days. Based on a stellar evolution model analysis... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
Explanation: On Kepler 452
Kepler 452 is a solar-like star in the Kepler field that was recently announced to possess a planet with an orbit of 385 Earth days. Based on a stellar evolution model analysis of the host star, the planet is f... |
2,916 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Point sources
In astromodels a point source is described by its position in the sky and its spectral features.
Creating a point source
A simple source with a power law spectrum can be create... | Python Code:
from astromodels import *
# Using J2000 R.A. and Dec (ICRS), which is the default coordinate system:
simple_source_icrs = PointSource('simple_source', ra=123.2, dec=-13.2, spectral_shape=powerlaw())
Explanation: Point sources
In astromodels a point source is described by its position in the sky and its spe... |
2,917 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Modifying an image
Basic example, rotates the image (rot_const) by 0.2 radians.
Step1: Full list of modifications and defaults
center
Step2: Perlin noise
Step3: A bunch of different confi... | Python Code:
img1 = image.load_image('https://upload.wikimedia.org/wikipedia/commons/6/6a/Mona_Lisa.jpg', (220, 350))
img2 = canvas.modify_canvas(img1, {'rot_const': 0.2})
img3 = image.concatenate_images([img1, img2], margin=2)
image.display(img3)
Explanation: Modifying an image
Basic example, rotates the image (rot_co... |
2,918 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lectura y manipulación de datos con Pandas
Autor
Step1: 1. The Pandas Series Object
A Pandas Series is a one-dimensional array of indexed data. It can be created from a list or array as fol... | Python Code:
import numpy as np
from __future__ import print_function
import pandas as pd
pd.__version__
Explanation: Lectura y manipulación de datos con Pandas
Autor: Roberto Muñoz <br />
E-mail: rmunoz@uc.cl
This notebook shows how to create Series and Datafram... |
2,919 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Project Euler
Contents
Useful Functions
Multiples of 3 and 5
Even Fibonacci numbers
Largest prime factor
Largest palindrome product
Smallest multiple
Sum square difference
10001st prime
Larg... | Python Code:
def is_prime(n):
if n == 1: return False
if n < 4: return True
if n % 2 == 0: return False
if n < 9: return True # excluded 4, 6, 8 already
if n % 3 == 0: return False
i = 5
while i < n**(0.5) + 1:
if n % i == 0:
return False
if n % (i + 2) == 0:
... |
2,920 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step4: Seminar
Step5: try out snapshots
Step16: MCTS
Step18: Main MCTS loop
With all we implemented, MCTS boils down to a trivial piece of code.
Step19: Plan and execute
In this section,... | Python Code:
from collections import namedtuple
from pickle import dumps, loads
from gym.core import Wrapper
# a container for get_result function below. Works just like tuple, but prettier
ActionResult = namedtuple(
"action_result", ("snapshot", "observation", "reward", "is_done", "info"))
class WithSnapshots(Wrap... |
2,921 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example
Step1: First we build a model representing the system of equations.
Step2: Generate mock data.
Step3: Perform the fit. Let's pretend that for experimental reasons, we can only mea... | Python Code:
from symfit import (
variables, parameters, ODEModel, D, Fit
)
from symfit.core.support import key2str
import numpy as np
import matplotlib.pyplot as plt
Explanation: Example: Multiple species Reaction Kinetics using ODEModel
In this example we shall fit to a complex system of ODEs, based on that publish... |
2,922 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Input
To do any computation, you need to have data. Getting the data in the framework of a workflow is therefore the first step of every analysis. Nipype provides many different modules... | Python Code:
from nipype import DataGrabber, Node
# Create DataGrabber node
dg = Node(DataGrabber(infields=['subject_id', 'ses_name', 'task_name'],
outfields=['anat', 'func']),
name='datagrabber')
# Location of the dataset folder
dg.inputs.base_directory = '/data/ds000114'
# Necessary de... |
2,923 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Content and Objectives
Show pulse shaping (rect and raised-cosine) for random data
Spectra are determined based on the theoretical pulse shape as well as for the random signals when applying... | Python Code:
# importing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 16}
plt.rc('font', **font)
plt.rc('text', usetex=matplotlib.checkdep_usetex(True))
matplotlib.rc('figure', figsize=(18, 8) )
Explanation: Conte... |
2,924 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Unit Tests
Overview and Principles
Testing is the process by which you exercise your code to determine if it performs as expected. The code you are testing is referred to as the code under t... | Python Code:
import numpy as np
# Code Under Test
def entropy(ps):
if any([(p < 0.0) or (p > 1.0) for p in ps]):
raise ValueError("Bad input.")
if sum(ps) > 1:
raise ValueError("Bad input.")
items = ps * np.log(ps)
new_items = []
for item in items:
if np.isnan(item):
... |
2,925 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Flowers Image Classification with TensorFlow on Cloud ML Engine TPU
This notebook demonstrates how to do image classification from scratch on a flowers dataset using the Estimator API. Unlik... | Python Code:
%%bash
pip install apache-beam[gcp]
Explanation: Flowers Image Classification with TensorFlow on Cloud ML Engine TPU
This notebook demonstrates how to do image classification from scratch on a flowers dataset using the Estimator API. Unlike flowers_fromscratch.ipynb, here we do it on a TPU.
Therefore, this... |
2,926 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1A.algo - Des problèmes de graphes
Découvrir les graphes avec des problèmes pas trop compliqués. Composantes connexes, plus court chemin et...
Step1: Un graphe | Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
Explanation: 1A.algo - Des problèmes de graphes
Découvrir les graphes avec des problèmes pas trop compliqués. Composantes connexes, plus court chemin et...
End of explanation
# tutoriel_graphe
noeuds = {0: 'le', 1: 'silences', 2: 'quelques', 3... |
2,927 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The Epochs data structure
Step1:
Step2: As we saw in the tut-events-vs-annotations tutorial, we can extract an
events array from
Step3: <div class="alert alert-info"><h4>Note</h4><p>We ... | Python Code:
import os
import mne
Explanation: The Epochs data structure: discontinuous data
This tutorial covers the basics of creating and working with :term:epoched
<epochs> data. It introduces the :class:~mne.Epochs data structure in
detail, including how to load, query, subselect, export, and plot data from ... |
2,928 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
US energy consumption
This example is based on the Sankey diagrams of US energy consumption from the Lawrence Livermore National Laboratory (thanks to John Muth for the suggestion and transc... | Python Code:
from floweaver import *
Explanation: US energy consumption
This example is based on the Sankey diagrams of US energy consumption from the Lawrence Livermore National Laboratory (thanks to John Muth for the suggestion and transcribing the data). We jump straight to the final result – for more explanation of... |
2,929 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Photo-z Determination for SpIES High-z Candidates
Notebook that actually applies the algorithms from SpIESHighzQuasarPhotoz.ipynb to the quasar candidates.
Step1: Since we are running on se... | Python Code:
## Read in the Training Data and Instantiating the Photo-z Algorithm
%matplotlib inline
from astropy.table import Table
import numpy as np
import matplotlib.pyplot as plt
#data = Table.read('GTR-ADM-QSO-ir-testhighz_findbw_lup_2016_starclean.fits')
#JT PATH ON TRITON to training set after classification
#d... |
2,930 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
For ecoinvent3.4 and ecoinvent3.5, the LCIA_implmentation.xls file does not include units for emissions of the pollutants anymore. This is a requirement however, in ecopold2matrix. This note... | Python Code:
import pandas as pd
old_LCIA = pd.read_excel('put_the_path_to_your_old_LCIA_implementation_file.xls_here','CFs')
incomplete_LCIA = pd.read_excel('put_the_path_to_your_incomplete_LCIA_implementation_file.xls_here','CFs')
complete_LCIA = incomplete_LCIA.merge(old_LCIA,how='left')
# drop obsolete columns
comp... |
2,931 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Our test set here includes the 16 million molecules from the old ZINC clean set that could be successfully processed by the RDKit.
We use the Standard InChI that comes with ChEMBL and a non-... | Python Code:
%sql postgresql://localhost/inchi_split \
select count(*) from zinc_clean_nonstandard;
Explanation: Our test set here includes the 16 million molecules from the old ZINC clean set that could be successfully processed by the RDKit.
We use the Standard InChI that comes with ChEMBL and a non-standard InCh... |
2,932 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Importar librerías
Step2: Cargar base de datos
Step3: Desechar imágenes no cuadradas
Step4: Normalizar
Step5: Balance de Clases, definir conjuntos de train, val, y... | Python Code:
from google.colab import drive
drive.mount('/content/drive')
Explanation: <a href="https://colab.research.google.com/github/kevinracso/01Tarea/blob/master/Copia_de_Copia_de_Preprocesamiento_y_Red_test.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In C... |
2,933 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Reproducible Experiments with pynoddy
All pynoddy experiments can be defined in a Python script, and if all settings are appropriate, then this script can be re-run to obtain a reproduction ... | Python Code:
from IPython.core.display import HTML
css_file = 'pynoddy.css'
HTML(open(css_file, "r").read())
%matplotlib inline
# here the usual imports. If any of the imports fails,
# make sure that pynoddy is installed
# properly, ideally with 'python setup.py develop'
# or 'python setup.py install'
import sys, os
... |
2,934 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h2> ppm = 2.5 </h2>
There weren't engouh retention-tie correlation groups here, so I should either switch to linear interpolation (not loess). So this is probably kinda meh data in real lif... | Python Code:
import time
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.cross_validation import cross_val_score
#fr... |
2,935 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Classification
Timothy Helton
<br>
<font color="red">
NOTE
Step1: Exercise 1
This question should be answered using the Weekly data set. This data is similar in nature to the Smarket da... | Python Code:
from k2datascience import classification
from k2datascience import plotting
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
%matplotlib inline
Explanation: Classification
Timothy Helton
<br>
<font color="red">
NOTE:
<br>
This notebook u... |
2,936 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using data from this FiveThirtyEight post, write code to calculate the correlation of the responses from the poll.
Respond to the story in your PR. Is this a good example of data journalism?... | Python Code:
import pandas as pd
%matplotlib inline
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
df = pd.read_excel("Iran_data_3.xlsx")
df
Explanation: Using data from this FiveThirtyEight post, write code to calculate the correlation of the responses from the poll.
Respond to the story in your... |
2,937 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Source localization with MNE/dSPM/sLORETA/eLORETA
The aim of this tutorial is to teach you how to compute and apply a linear
minimum-norm inverse method on evoked/raw/epochs data.
Step1: Pr... | Python Code:
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from mne.minimum_norm import make_inverse_operator, apply_inverse
Explanation: Source localization with MNE/dSPM/sLORETA/eLORETA
The aim of this tutorial is to teach you how to compute and app... |
2,938 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pandas objects
So far, we have manipulated data which were stored in NumPy arrays. Let us consider 2D data.
Step1: We could visualize it with Matplotlib.
Step2: Raw data could look like th... | Python Code:
import numpy as np
ar = 0.5 * np.eye(3)
ar[2, 1] = 1
ar
Explanation: Pandas objects
So far, we have manipulated data which were stored in NumPy arrays. Let us consider 2D data.
End of explanation
import matplotlib.pyplot as plt
%matplotlib inline
plt.imshow(ar, cmap=plt.cm.gray)
Explanation: We could visua... |
2,939 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Checking the Equivalence of Regular Expressions
In order to check whether two regular expressions $r_1$ and $r_2$ are equivalent, perform the
following steps
Step1: NFA-2-DFA.ipynb contain... | Python Code:
%run Regexp-2-NFA.ipynb
Explanation: Checking the Equivalence of Regular Expressions
In order to check whether two regular expressions $r_1$ and $r_2$ are equivalent, perform the
following steps:
- convert $r_1$ and $r_2$ into non-deterministic <span style="font-variant:small-caps;">Fsm</span>s
$F_1$ an... |
2,940 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Enter your settings here
Step1: Load the data
Step2: Smoothing
Smoothing in the lattitudinal direction (N-S) is not affected by the projection; distances do not vary since the circles of e... | Python Code:
data_folder = Path("/home/bathiany/Sebastian/datamining/edges/obsscan")
data_set = DataSet([data_folder / 'ice_conc_nh_ease2-250_cdr-v2p0_remapbilt200_September.nc'], 'ice_conc')
#dataset goes from 1979 - 2015
sigma_d = unit('120 km') #200
sigma_t = unit('1 year')
gamma = 1e10
scaling_factor = gamma * un... |
2,941 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Now to create the needed tables we need to execute our first SQL statements. It is important to call the commit() function if you want your changes to appear in the file. It is recommended t... | Python Code:
conn.execute('''CREATE TABLE FILM
(ID INTEGER PRIMARY KEY,
TITLE CHAR(200) NOT NULL,
YEAR INTEGER NOT NULL,
GENRE CHAR(50) NOT NULL,
UNIQUE(TITLE, YEAR, GENRE));''')
conn.execute('... |
2,942 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Données multidimensionnelles SQL - correction
Correction de la séance sur l'utilisation du SQL depuis un notebook.
Step1: Exercice 1
Step2: Exercice 2
Step3: Que faut-il écrire ici pour... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
from pyquickhelper.helpgen import NbImage
from jyquickhelper import add_notebook_menu
add_notebook_menu()
Explanation: Données multidimensionnelles SQL - correction
Correction de la séance sur l'utilisation du SQL depuis un notebook... |
2,943 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial
Let's work through an example of single-cell data analysis using Uncurl, using many of its features. For a much briefer example using the same dataset, see examples/zeisel_subset_ex... | Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
import uncurl
Explanation: Tutorial
Let's work through an example of single-cell data analysis using Uncurl, using many of its features. For a much briefer example using the same dataset, see examples/zeisel_subset_exampl... |
2,944 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Procrustes Analysis
Step1: PCA
Step2: Solving b vector
$$b = \Phi^T \left(x - \bar{x}\right)$$ | Python Code:
import pandas
df = pandas.read_csv('muct76_stasm-output.csv', header=None, usecols=np.arange(2,156), dtype=float)
#df = pandas.read_csv('muct76-opencv.csv', header=0, usecols=np.arange(2,154), dtype=float)
df.head()
X = df.iloc[:, ::2].values
Y = df.iloc[:, 1::2].values
d = np.hstack((X,Y))
d.shape
import ... |
2,945 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Step 1
Step2: Step 2
Step3: Step 3
Step4: The CmdStan installation includes a simple example program bernoulli.stan and test data bernoulli.data.json. These are in ... | Python Code:
# Load packages used in this notebook
import os
import json
import shutil
import urllib.request
import pandas as pd
Explanation: <a href="https://colab.research.google.com/github/probml/probml-notebooks/blob/main/notebooks/CmdStanPy_Example_Notebook.ipynb" target="_parent"><img src="https://colab.research.... |
2,946 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Prepare CDL Data
In this notebook, we prepare the 2016 from the USDA 2016 Crop Data Layer (CDL) for Iowa for use in the crop classification notebooks. This involves taking the CDL data for I... | Python Code:
import os
import pathlib
from subprocess import check_output, STDOUT, CalledProcessError
import tempfile
import rasterio
Explanation: Prepare CDL Data
In this notebook, we prepare the 2016 from the USDA 2016 Crop Data Layer (CDL) for Iowa for use in the crop classification notebooks. This involves taking t... |
2,947 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example 1
Step1: Read the counts table
This is bacterial mRNA-Seq with samples at 4 different temperatures with or without the addition of BCM. Each condition is sequenced in triplicates. A... | Python Code:
def parse_barcodes(bcfile, bc_id='BC'):
res = {}
with open(bcfile, 'r') as fi:
for line in fi:
fields = line.strip().split(',')
if fields[0].startswith(bc_id):
res[fields[0]] = fields[1]
return res
def parse_exp_config(expfile, bc_dict):
res =... |
2,948 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Model Evaluation
How to measure a model? How to find out that the model is doing well or just predicting useless?
This job is done by metrics. There are a bunch of metrics explained in sciki... | Python Code:
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
print('X.shape =', X.shape)
print('y.shape =', y.shape)
print()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=True)
print('X_train.shape =', X_tra... |
2,949 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href='http
Step1: Get the Data
We'll work with the Ecommerce Customers csv file from the company. It has Customer info, suchas Email, Address, and their color Avatar. Then it also has nu... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Linear Regression - Project Exercise
Congratulations! You just got some contract work with an Ecommer... |
2,950 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
VARMAX models
This is a brief introduction notebook to VARMAX models in statsmodels. The VARMAX model is generically specified as
Step1: Model specification
The VARMAX class in statsmodels ... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
dta = sm.datasets.webuse('lutkepohl2', 'https://www.stata-press.com/data/r12/')
dta.index = dta.qtr
dta.index.freq = dta.index.inferred_freq
endog = dta.loc['1960-04-01':'1978-10-01', ['dl... |
2,951 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to Statistics in Python
https
Step1: Hypothesis Testing
Step2: 1-sample t-test
Step3: 2-sample t-test
Step4: Paired tests
Step5: However this doesn't account for individual... | Python Code:
#import pandas and use magic function
import pandas as pd
%matplotlib inline
# import our data using pandas read_csv() function where delimiter = ';', index_col = 0, na_values = '.'
data = pd.read_csv('https://www.scipy-lectures.org/_downloads/brain_size.csv',
delimiter=';', index_col=0... |
2,952 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Environment Preparation
Install Java 8
Run the cell on the Google Colab to install jdk 1.8.
Note
Step2: Install BigDL Orca
You can install the latest pre-release vers... | 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# d... |
2,953 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lab 7
Step1: Today's lab reviews Maximum Likelihood Estimation, and introduces interctive plotting in the jupyter notebook.
Part 1
Step2: Question 2
Step3: Question 3
Step4: Question 4
S... | Python Code:
# Run this cell to set up the notebook.
import numpy as np
import pandas as pd
import seaborn as sns
import scipy as sci
import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib import patches, cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
from mpl_toolkits.... |
2,954 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
BigQuery Query Run
Run query on a project.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance w... | Python Code:
!pip install git+https://github.com/google/starthinker
Explanation: BigQuery Query Run
Run query on a project.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the Lic... |
2,955 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Form Parsing using Google Cloud Document AI
This notebook shows how to use Google Cloud Document AI to parse a campaign disclosure form.
It accompanies this Medium article
Step2: Doc... | Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/imported/formparsing.ipynb
from IPython.display import Markdown as md
### change to reflect your notebook
_nb_repo = 'training-data-analyst'
_nb_loc = "blogs/form_parser/formparsing.ipynb"
_nb_title = "Form Parsing Using Google Cloud Document AI"
### no need to ... |
2,956 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Optimization Methods
Until now, you've always used Gradient Descent to update the parameters and minimize the cost. In this notebook, you will learn more advanced optimization methods that c... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
import math
import sklearn
import sklearn.datasets
from opt_utils import load_params_and_grads, initialize_parameters, forward_propagation, backward_propagation
from opt_utils import compute_cost, predict, predict_dec, plot_decision_boundar... |
2,957 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Characterwise Double-Stacked LSTM as Author
Step1: Defining the Model
Actually, it's a single layer of GRU for now... (rather than a double-stacked LSTM)
Step2: That's the underlying netw... | Python Code:
import numpy
import theano
from theano import tensor
from blocks.bricks import Tanh
from blocks.bricks.recurrent import GatedRecurrent
from blocks.bricks.sequence_generators import (SequenceGenerator, Readout, SoftmaxEmitter, LookupFeedback)
from blocks.graph import ComputationGraph
import blocks.algorithm... |
2,958 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A Decision Tree of Observable Operators
Part 2
Step1: ... and emitting all of the items from all of the Observables, one Observable at a time
Step2: ... by combining the items from two or ... | Python Code:
reset_start_time(O.merge)
l = []
def excepting_f(obs):
for i in range(10):
l.append(1)
obs.on_next(1 / (3 - len(l)))
stream1 = O.from_(('a', 'b', 'c'))
stream2 = O.create(excepting_f)
# merged stream stops in any case at first exception!
# No guarantee of order of those immediately crea... |
2,959 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bubble Breaker in Python / Javascript
The key 'board' data structure is a numpy array, which is (for efficiency) stored on its side (with the bottom-right phone cell being the board[0,0] cel... | Python Code:
import os
import numpy as np
import shutil, requests
import pickle
Explanation: Bubble Breaker in Python / Javascript
The key 'board' data structure is a numpy array, which is (for efficiency) stored on its side (with the bottom-right phone cell being the board[0,0] cell):
End of explanation
models_dir = '... |
2,960 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Aprendizaje computacional en grandes volúmenes de texto
Mario Graff (mgraffg@ieee.org, mario.graff@infotec.mx)
Sabino Miranda (sabino.miranda@infotec.mx)
Daniela Moctezuma (dmoctezuma@centro... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import gzip
import json
import numpy as np
def read_data(fname):
with gzip.open(fname) as fpt:
d = json.loads(str(fpt.read(), encoding='utf-8'))
return d
%matplotlib inline
plt.figure(figsize=(20, 10))
mx_pos = read_data('spanish/polari... |
2,961 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Back to the main Index
Inline OMEX and COMBINE archives
Tellurium provides a way to easily edit the contents of COMBINE archives in a human-readable format called inline OMEX. To create a CO... | Python Code:
import tellurium as te, tempfile, os
te.setDefaultPlottingEngine('matplotlib')
%matplotlib inline
antimony_str = '''
model myModel
S1 -> S2; k1*S1
S1 = 10; S2 = 0
k1 = 1
end
'''
phrasedml_str = '''
model1 = model "myModel"
sim1 = simulate uniform(0, 5, 100)
task1 = run sim1 on model1
plot "Fi... |
2,962 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Here we examine whether publishing volume has an impact on overall, article, or place traffic, specifically whether total, average, or median traffic is affected by increased publishing volu... | Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.read_csv('All content.csv', index_col='Published',parse_dates=True)
df['count']=1
df = df[(df['Page Views'] > 200)]
df_resampled = df.resample('D',how ='sum')
Explanation: Here we examine whether publishing vo... |
2,963 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Creating a Simple Autoencoder
By
Step1: Problem 1a
Step2: Problem 1b.
Split the training and test set with a 66/33 split.
Problem 2
Step3: Problem 3. Training
This is going to be a lot of... | Python Code:
!pip install astronn
import torch
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import IsolationForest
from astroNN.datasets import load_galaxy10
from astroNN.datasets.galaxy10 import galaxy10cls_lookup
from sklearn.ensemble im... |
2,964 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Create new train folders with only the files from train_and_test_data_labels_safe.csv
Step1: Create TFRecords | Python Code:
import shutil
# Read files list. Header: file, class (0: interictal, 1: preictal), safe (or not to use)
files_list = np.genfromtxt('./train_and_test_data_labels_safe.csv',
dtype=("|S15", np.int32, np.int32), delimiter=',', skip_header=1)
# Get only files which are safe to use
fi... |
2,965 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Orbital Elements
Note
Step1: Any components not passed automatically default to 0. REBOUND can also accept orbital elements.
Reference bodies
As a reminder, there is a one-to-one mapping... | Python Code:
import rebound
sim = rebound.Simulation()
sim.add(m=1., x=1., vz = 2.)
Explanation: Orbital Elements
Note: All angles for orbital elements are in radians
We can add particles to a simulation by specifying cartesian components:
End of explanation
sim.add(m=1., a=1.)
sim.status()
Explanation: Any components... |
2,966 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I have some data structured as below, trying to predict t from the features. | Problem:
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
data = load_data()
scaler = StandardScaler()
scaler.fit(data)
scaled = scaler.transform(data)
inversed = scaler.inverse_transform(scaled) |
2,967 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Q-Network implementation
This notebook shamelessly demands you to implement a DQN - an approximate q-learning algorithm with experience replay and target networks - and see if it works ... | Python Code:
#XVFB will be launched if you run on a server
import os
if type(os.environ.get("DISPLAY")) is not str or len(os.environ.get("DISPLAY")) == 0:
!bash ../xvfb start
os.environ['DISPLAY'] = ':1'
Explanation: Deep Q-Network implementation
This notebook shamelessly demands you to implement a DQN - an app... |
2,968 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Morph volumetric source estimate
This example demonstrates how to morph an individual subject's
Step1: Setup paths
Step2: Compute example data. For reference see
sphx_glr_auto_examples_inv... | Python Code:
# Author: Tommy Clausner <tommy.clausner@gmail.com>
#
# License: BSD (3-clause)
import os
import nibabel as nib
import mne
from mne.datasets import sample
from mne.minimum_norm import apply_inverse, read_inverse_operator
from nilearn.plotting import plot_glass_brain
print(__doc__)
Explanation: Morph volume... |
2,969 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial
In this tutorial we'll explore the two main pieces of functionality that
HSMMLearn provides
Step1: Decoding
For this part of the tutorial we'll use an HSMM with 3 internal states a... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Tutorial
In this tutorial we'll explore the two main pieces of functionality that
HSMMLearn provides:
Viterbi decoding: given a sequence of observations, find the sequence of
hidden states that maximizes the joint probabil... |
2,970 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MuJoCo tutorial with dm_control Python bindings
<p><small><small>Copyright 2021 The dm_control Authors.</small></p>
<p><small><small>Licensed under the Apache License, Version 2.0 (the "Lice... | Python Code:
#@title Run to install MuJoCo and `dm_control`
import distutils.util
import subprocess
if subprocess.run('nvidia-smi').returncode:
raise RuntimeError(
'Cannot communicate with GPU. '
'Make sure you are using a GPU Colab runtime. '
'Go to the Runtime menu and select Choose runtime type.'... |
2,971 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Functions
Making reusable blocks of code.
Starting point
Step1: What about for $a = 2$, $b = 8$, and $c = 1$?
Step3: Functions
Step5: Observe how this function works.
Step7: Summarize
St... | Python Code:
## Code here
import math
(-4 + math.sqrt(4**2 - 4*1*3))/(2*1)
Explanation: Functions
Making reusable blocks of code.
Starting point:
In this exercise, we're going to calculate one of the roots from the quadratic formula:
$r_{p} = \frac{-b + \sqrt{b^{2} - 4ac}}{2a}$
Determine $r_{p}$ for $a = 1$, $b=4$, and... |
2,972 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Cppyy Tutorial
(Modified from Enrico Guiraud's cppyy tutorial.)
This tutorial introduces the basic concepts for using cppyy, the automatic Python-C++ generator. To install cppyy on your syst... | Python Code:
import cppyy
Explanation: Cppyy Tutorial
(Modified from Enrico Guiraud's cppyy tutorial.)
This tutorial introduces the basic concepts for using cppyy, the automatic Python-C++ generator. To install cppyy on your system, simply run (this may take a while as it will pull in and compile a custom version of LL... |
2,973 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Boosting a decision stump
The goal of this notebook is to implement your own boosting module.
Brace yourselves! This is going to be a fun and challenging assignment.
Use SFrames to do some f... | Python Code:
import graphlab
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Boosting a decision stump
The goal of this notebook is to implement your own boosting module.
Brace yourselves! This is going to be a fun and challenging assignment.
Use SFrames to do some feature engineering.
Modify the decisi... |
2,974 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Creating a document similarity microservice for the Reuters-21578 dataset.
First download the Reuters-21578 dataset in JSON format into the local folder
Step1: Create a gensim LSI document ... | Python Code:
import json
import codecs
import os
docs = []
for filename in os.listdir("reuters-21578-json/data/full"):
f = open("reuters-21578-json/data/full/"+filename)
js = json.load(f)
for j in js:
if 'topics' in j and 'body' in j:
d = {}
d["id"] = j['id']
d["... |
2,975 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Convolutional Autoencoder
Sticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data.
Step1: Network Archit... | Python Code:
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
Explanati... |
2,976 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: Text classification with TensorFlow Lite Model Maker
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="http... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dist... |
2,977 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pandas 数据读写
API
读取 | 写入
--- | ---
read_csv | to_csv
read_excel | to_excel
read_hdf | to_hdf
read_sql | to_sql
read_json | to_json
read_html | to_html
read_stata | to_stata
read_clipboard | ... | Python Code:
import numpy as np
import pandas as pd
csvframe=pd.read_csv('myCSV_01.csv')
csvframe
# 也可以通过read_table来读写数据
pd.read_table('myCSV_01.csv',sep=',')
Explanation: Pandas 数据读写
API
读取 | 写入
--- | ---
read_csv | to_csv
read_excel | to_excel
read_hdf | to_hdf
read_sql | to_sql
read_json | to_json
read_html | to_ht... |
2,978 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Mining
First thing to do is to find the structure of the file. We can find a description in the "dblp.dtd" file and on the website
Step1: Observation of the data
Load the author-> publ... | Python Code:
from importlib import reload
import xml_parser
reload(xml_parser)
from xml_parser import Xml_parser
#Xml_parser = Xml_parser().collect_data("../pmi_data")
Explanation: Data Mining
First thing to do is to find the structure of the file. We can find a description in the "dblp.dtd" file and on the website:
Th... |
2,979 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Landice
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cams', 'sandbox-3', 'landice')
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: CAMS
Source ID: SANDBOX-3
Topic: Landice
Sub-Topics: Glaciers, Ice.
Prop... |
2,980 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Text classification with preprocessed text
Step2: <a id="download"></a>
Download the IMDB dataset
The IMDB movie reviews dataset comes packaged in tfds. It has alread... | 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... |
2,981 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Diagram bifurkacyjny dla równania logistycznego $x \to a x (1-x)$
Równanie logistyczne jest niezwykle prostym równaniem iteracyjnym wykazującym zaskakująco złożone zachowanie. Jego własności... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pycuda.gpuarray as gpuarray
from pycuda.curandom import rand as curand
from pycuda.compiler import SourceModule
import pycuda.driver as cuda
try:
ctx.pop()
ctx.detach()
except:
print ("No CTX!")
cuda.init()
device = cu... |
2,982 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Comparing Corpus Visualizers on Yellowbrick
Step1: UMAP vs T-SNE
Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualisation s... | Python Code:
##### Import all the necessary Libraries
from yellowbrick.text import TSNEVisualizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from yellowbrick.text import UMAPVisualizer
from yellowbrick.datasets import load_hobbies
Explanation:... |
2,983 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Running faster your code
[Discrete signal energy](https
Step2: Now, using Numpy's array multiplication and sum
Step4: Another example to see that vectorization not only involves pur... | Python Code:
import numpy as np
def non_vectorized_dot_product(x, y):
Return the sum of x[i] * y[j] for all pairs of indices i, j.
Example:
>>> my_dot_product(np.arange(20), np.arange(20))
result = 0
for i in range(len(x)):
result += x[i] * y[i]
return result
signal = ... |
2,984 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
$\newcommand{\xv}{\mathbf{x}}
\newcommand{\Xv}{\mathbf{X}}
\newcommand{\piv}{\mathbf{\pi}}
\newcommand{\yv}{\mathbf{y}}
\newcommand{\Yv}{\mathbf{Y}}
\newcommand{\zv}{\mathbf{z}}
\newcommand{... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
xs = np.linspace(-5,10,1000)
mu = 5.5
plt.plot(xs, 1/np.sqrt((xs-mu)**2))
plt.ylim(0,20)
plt.plot([mu, mu], [0, 20], 'r--',lw=2)
plt.xlabel('$x$')
plt.ylabel('$p(x)$');
Explanation: $\newcommand{\xv}{\mathbf{x}}
\newcommand{\Xv}{\mathbf{... |
2,985 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
6.86x - Introduction to ML Packages (Part 2)
This tutorial is designed to provide a short introduction to deep learning with PyTorch.
You can start studying this tutorial as you work through... | Python Code:
# Start by importing torch
import torch
Explanation: 6.86x - Introduction to ML Packages (Part 2)
This tutorial is designed to provide a short introduction to deep learning with PyTorch.
You can start studying this tutorial as you work through unit 3 of the course.
For more resources, check out the PyTorch... |
2,986 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python --> IPython --> (IPython) Notebook
What, Why ?
Python = language interpreter
IPython = enhanced interaction, shortcuts, ..
IPython notebook = html frontend + protocol
Jupyter notebo... | Python Code:
## Let's have a short look at the IPython web site:
from IPython.display import display, Image, HTML
HTML('<iframe src=http://ipython.org width=1000 height=400> </iframe>')
Explanation: Python --> IPython --> (IPython) Notebook
What, Why ?
Python = language interpreter
IPython = enhanced interaction, shor... |
2,987 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Goal-Based Data Collection
A practical approach to robot reinforcement learning is to first collect a large batch of real or simulated robot interaction data,
using some data collection pol... | Python Code:
from rl_coach.agents.td3_exp_agent import TD3GoalBasedAgentParameters
from rl_coach.architectures.embedder_parameters import InputEmbedderParameters
from rl_coach.architectures.layers import Dense, Conv2d, BatchnormActivationDropout, Flatten
from rl_coach.base_parameters import EmbedderScheme
from rl_coach... |
2,988 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Obtain the SELU parameters for arbitrary fixed points
Author
Step2: Function to obtain the parameters for the SELU with arbitrary fixed point (mean variance)
Step4: Adjust the SELU functio... | Python Code:
import numpy as np
from scipy.special import erf,erfc
from sympy import Symbol, solve, nsolve
Explanation: Obtain the SELU parameters for arbitrary fixed points
Author: Guenter Klambauer, 2017
tested under Python 3.5
End of explanation
def getSeluParameters(fixedpointMean=0,fixedpointVar=1):
Finding t... |
2,989 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Notebook Title
Notebook description.
Question 1
Question 1 text.
Step1: Question 2
Question 2 text. | Python Code:
print("Student 1 answers question 1.")
print("Student 2 answers question 1.")
print("Student 3 answers question 1.")
Explanation: Notebook Title
Notebook description.
Question 1
Question 1 text.
End of explanation
print("Student 1 answers question 2.")
print("Student 3 answers question 2.")
print("Student ... |
2,990 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step 1
Step1: So we have a general idea of what things look like. Let's convert the readings to numpy arrays.
Step2: And for our purposes we have focused and non-focused. Let's pool all th... | Python Code:
import json
import pandas as pd
import tensorflow as tf
import numpy as np
df = pd.read_csv("kaggle_data/eeg-data.csv")
df.head()
df.loc[df.label=='math1']
Explanation: Step 1: Pull in Data, preprocess it.
Here I'm using example data from the BioSENSE research group at UC Berekely, collected using a Neuros... |
2,991 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I have the following torch tensor: | Problem:
import numpy as np
import pandas as pd
import torch
t, idx = load_data()
assert type(t) == torch.Tensor
assert type(idx) == np.ndarray
idx = 1 - idx
idxs = torch.from_numpy(idx).long().unsqueeze(1)
# or torch.from_numpy(idxs).long().view(-1,1)
result = t.gather(1, idxs).squeeze(1) |
2,992 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
In this notebook, we duplicate the neural network created in the TensorFlow example in keras. I found the model in keras to be conceptually cleaner.
Step1: Set up the parameters for the mo... | Python Code:
from __future__ import print_function
from __future__ import division
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import ... |
2,993 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example 4 - stripy gradients on the sphere
SSRFPACK is a Fortran 77 software package that constructs a smooth interpolatory or approximating surface to data values associated with arbitraril... | Python Code:
import stripy as stripy
mesh = stripy.spherical_meshes.icosahedral_mesh(refinement_levels=4, include_face_points=True)
print(mesh.npoints)
Explanation: Example 4 - stripy gradients on the sphere
SSRFPACK is a Fortran 77 software package that constructs a smooth interpolatory or approximating surface to dat... |
2,994 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook explores the effect of using optimization routine and data extrapolation to zero for S(Q)
We are going to load a data pattern and background Pattern of $Mg_2SiO_4$. The data is... | Python Code:
%matplotlib inline
import os
import sys
import matplotlib.pyplot as plt
sys.path.insert(1, os.path.join(os.getcwd(), '../../'))
from glassure.core.calc import calculate_fr, calculate_sq, optimize_sq, calculate_gr
from glassure.core.utility import extrapolate_to_zero_poly, convert_density_to_atoms_per_cubic... |
2,995 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
image processing
Average Hash
이미지를 비교 가능한 해시 값으로 나타낸 것
해시 함수 MD5, SHA256 등을 이용해 데이터 값을 간단한 해시 값으로 변환할 수 있음
이미지가 비슷한지 등을 검출할 때는 해시함수를 사용하면 안됨. 해상도 크기 조정, 색조 보정, 압축 형식 변경 등으로 해시값이 달라짐
Step2: ... | Python Code:
from PIL import Image
import numpy as np
def average_hash(fname, size = 16):
img = Image.open(fname)
img = img.convert('L') # 1을 지정하면 이진화, RGB, RGBA, CMYK 등의 모드도 지원
img = img.resize((size, size), Image.ANTIALIAS)
pixel_data = img.getdata()
pixels = np.array(pixel_data)
pixels = pixe... |
2,996 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
This notebook shows how to plot and analyze a phase diagram.
Written using
Step1: Generating the phase diagram
To generate a phase diagram, we obtain entries from the Materials... | Python Code:
from pymatgen.ext.matproj import MPRester
from pymatgen.analysis.phase_diagram import PhaseDiagram, PDPlotter
%matplotlib inline
Explanation: Introduction
This notebook shows how to plot and analyze a phase diagram.
Written using:
- pymatgen==2021.2.8
End of explanation
#This initializes the REST adaptor. ... |
2,997 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Decoding in time-frequency space data using the Common Spatial Pattern (CSP)
The time-frequency decomposition is estimated by iterating over raw data that
has been band-passed at different f... | Python Code:
# Authors: Laura Gwilliams <laura.gwilliams@nyu.edu>
# Jean-Remi King <jeanremi.king@gmail.com>
# Alex Barachant <alexandre.barachant@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from ... |
2,998 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
DKRZ CMIP6 submission form for ESGF data publication
General Information (to be completed based on official CMIP6 references)
Data to be submitted for ESGF data publication must follow the r... | Python Code:
from dkrz_forms import form_widgets
form_widgets.show_status('form-submission')
Explanation: DKRZ CMIP6 submission form for ESGF data publication
General Information (to be completed based on official CMIP6 references)
Data to be submitted for ESGF data publication must follow the rules outlined in the CM... |
2,999 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
01 SEP 2017
Step1: Finally starting to understand this problem. So ResourceExhaustedError isn't system memory (or at least not only) but graphics memory. The card (obviously) cannot handle ... | Python Code:
%matplotlib inline
import importlib
import os, sys; sys.path.insert(1, os.path.join('../utils'))
import utils2; importlib.reload(utils2)
from utils2 import *
from scipy.optimize import fmin_l_bfgs_b
from scipy.misc import imsave
from keras import metrics
from vgg16_avg import VGG16_Avg
from bcolz_array_ite... |
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