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8,700 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lab 12
Step1: Like last week, we're going to use pyspark, a Python package that wraps Apache Spark and makes its functionality available in Python. We'll also use a few of the standard Pyth... | Python Code:
!pip install sseclient
Explanation: Lab 12: Spark Streaming
Introduction
In this lab, we're going to look at data streaming with Apache Spark. At the end of the lab, you should be able to:
Create a local StreamingContext object.
Use Spark to analyse the recent Wikipedia edits stream.
Getting started
Let's ... |
8,701 | 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', 'niwa', 'sandbox-2', 'landice')
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: NIWA
Source ID: SANDBOX-2
Topic: Landice
Sub-Topics: Glaciers, Ice.
Prop... |
8,702 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interact Exercise 4
Imports
Step2: Line with Gaussian noise
Write a function named random_line that creates x and y data for a line with y direction random noise that has a normal distribut... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
Explanation: Interact Exercise 4
Imports
End of explanation
def random_line(m, b, sigma, size=10):
Create a line y = m*x + b + N(0,sigm... |
8,703 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Federated learning algorithms
This tutorial introduces algorithms for federated learning in FedJAX. By completing this tutorial, we'll learn how to write clear and efficient algorithms that ... | Python Code:
# Uncomment these to install fedjax.
# !pip install fedjax
# !pip install --upgrade git+https://github.com/google/fedjax.git
import jax
import jax.numpy as jnp
import numpy as np
import fedjax
# We only use TensorFlow for datasets, so we restrict it to CPU only to avoid
# issues with certain ops not being ... |
8,704 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Creating epochs of equal length
This tutorial shows how to create equal length epochs and briefly demonstrates
an example of their use in connectivity analysis.
First, we import necessary mo... | Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.preprocessing import compute_proj_ecg
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
r... |
8,705 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Estimating Joint Tour Frequency
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes running ActivitySim in estimation mode to read ... | Python Code:
import os
import larch # !conda install larch -c conda-forge # for estimation
import pandas as pd
Explanation: Estimating Joint Tour Frequency
This notebook illustrates how to re-estimate a single model component for ActivitySim. This process
includes running ActivitySim in estimation mode to read house... |
8,706 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This kind of neural network is used in a ... | Python Code:
# Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-... |
8,707 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
# Table of Contents
<div class="toc" style="margin-top
Step1: A simple neural network in Numpy
So what is a neural network anyway. Let's start by looking at a picture.
A neural network cons... | Python Code:
# Imports
import numpy as np
import sys
sys.path.append('../') # This is where all the python files are!
from importlib import reload
import utils; reload(utils)
from utils import *
import keras_models; reload(keras_models)
from keras_models import *
from losses import crps_cost_function
from scipy.stat... |
8,708 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tranlation Matrix Tutorial
What is it ?
Suppose we are given a set of word pairs and their associated vector representaion ${x_{i},z_{i}}{i=1}^{n}$, where $x{i} \in R^{d_{1}}$ is the distibu... | Python Code:
import os
from gensim import utils
from gensim.models import translation_matrix
from gensim.models import KeyedVectors
Explanation: Tranlation Matrix Tutorial
What is it ?
Suppose we are given a set of word pairs and their associated vector representaion ${x_{i},z_{i}}{i=1}^{n}$, where $x{i} \in R^{d_{1}}$... |
8,709 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
First we import the table of tag-article mappings from our SQL database
(but read in as a .csv).
Step1: We only care about the content type "Article"
Step2: But we need to get the tag name... | Python Code:
df = pd.read_csv('atlas-taggings.csv')
df[2:5]
Explanation: First we import the table of tag-article mappings from our SQL database
(but read in as a .csv).
End of explanation
articles = df[df.tagged_type == 'Article']
Explanation: We only care about the content type "Article"
End of explanation
articles.t... |
8,710 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Données multidimensionnelles SQL - énoncé
Ce notebook propose l'utilisation de SQL avec SQLite pour manipuler les données depuis un notebook (avec le module sqlite3).
Step1: Représentation
... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import pyensae
from pyquickhelper.helpgen import NbImage
from jyquickhelper import add_notebook_menu
add_notebook_menu()
Explanation: Données multidimensionnelles SQL - énoncé
Ce notebook propose l'utilisation de SQL avec SQLite pou... |
8,711 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Seaice
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', 'cas', 'sandbox-2', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CAS
Source ID: SANDBOX-2
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics,... |
8,712 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Functions
Functions are blocks of code identified by a name, which can receive ""predetermined"" parameters or not ;).
In Python, functions
Step3: In the above example, we have caps ... | Python Code:
def caps(val):
caps returns double the value of the provided value
return val*2
a = caps("TEST ")
print(a)
print(caps.__doc__)
Explanation: Functions
Functions are blocks of code identified by a name, which can receive ""predetermined"" parameters or not ;).
In Python, functions:
return o... |
8,713 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Saving and Loading Models
In this notebook, I'll show you how to save and load models with PyTorch. This is important because you'll often want to load previously trained models to use in ma... | Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets, transforms
import helper
import fc_model
# Define a transform to normalize the data
t... |
8,714 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Learning
Assignment 2
Previously in 1_notmnist.ipynb, we created a pickle with formatted datasets for training, development and testing on the notMNIST dataset.
The goal of this assignm... | Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
Explanation: Deep Learning
Assignment 2
Previousl... |
8,715 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
(📗) Cookbook
Step1: ipcluster setup to run parallel code
You can find more details about this in our [ipyparallel tutorial].
Step3: Get default (example) 12 taxon tree
The function... | Python Code:
## imports
import numpy as np
import ipyrad as ip
import ipyparallel as ipp
from ipyrad.analysis import baba
## print versions
print "ipyrad v.{}".format(ip.__version__)
print "ipyparallel v.{}".format(ipp.__version__)
print "numpy v.{}".format(np.__version__)
Explanation: (📗) Cookbook: ipyrad.anal... |
8,716 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Numpy Exercise 2
Imports
Step2: Factorial
Write a function that computes the factorial of small numbers using np.arange and np.cumprod.
Step4: Write a function that computes the factorial ... | Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
Explanation: Numpy Exercise 2
Imports
End of explanation
def np_fact(n):
Compute n! = n*(n-1)*...*1 using Numpy.
# YOUR CODE HERE
a = np.arange(1, n+1, 1) #Makes array from 1 to n+1
if n==0:
... |
8,717 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Seaice
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', 'cmcc', 'cmcc-cm2-hr4', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CMCC
Source ID: CMCC-CM2-HR4
Topic: Seaice
Sub-Topics: Dynamics, Thermod... |
8,718 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sheet To BigQuery
Import data from a sheet and move it to a BigQuery table.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use... | Python Code:
!pip install git+https://github.com/google/starthinker
Explanation: Sheet To BigQuery
Import data from a sheet and move it to a BigQuery table.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.... |
8,719 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TFX Components Walk-through
Learning Objectives
Develop a high level understanding of TFX pipeline components.
Learn how to use a TFX Interactive Context for prototype development of TFX pip... | Python Code:
import os
import time
from pprint import pprint
import absl
import tensorflow as tf
import tensorflow_data_validation as tfdv
import tensorflow_model_analysis as tfma
import tensorflow_transform as tft
import tfx
from tensorflow_metadata.proto.v0 import schema_pb2
from tfx.components import (
CsvExampl... |
8,720 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step by step code for abide_motion_wrapper.py
Step1: Read in the phenotypic behavioural data
This is the Phenotypic_V1_0b_preprocessed1.csv file. It's saved in the DATA folder.
You can fin... | Python Code:
import matplotlib.pylab as plt
%matplotlib inline
from matplotlib import ticker
from glob import glob
import numpy as np
import os
import pandas as pd
from scipy.stats import linregress, pearsonr, spearmanr
import nibabel as nib
import urllib
import seaborn as sns
sns.set_context('notebook', font_scale=2)
... |
8,721 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<div class="alert alert-info">**Hint**
Step1: You will use this data to train and evaluate your learning algorithm.
A "learning algorithm" which finds a polynomial of given degree that mini... | Python Code:
data = np.load("data/xy_data.npy")
# only show the first ten points, since there are a lot
data[:10]
Explanation: <div class="alert alert-info">**Hint**: Much of the material covered in this problem is introduced in the Geman et al. (1992) reading. If you are having trouble with the conceptual questions, t... |
8,722 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<table align="left">
<td>
<a href="https
Step1: Restart the kernel
After you install the additional packages, you need to restart the notebook kernel so it can find the packages.
Step... | Python Code:
import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = "--user"
if os.... |
8,723 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1. Data exploration
Exploração dos dados baseado em
Step1: Create a function to analysing
Step2: Feature Observation
We are see the 6 features in dataset
Step3: 2. Developing a model
In t... | Python Code:
from data import get_full_data, get_who_is
from matplotlib import pyplot as plt
from sklearn import linear_model
from predicting_who_is import accuracy_score, performance_metric
import pandas as pd
import numpy as np
from IPython.display import display # Allows the use of display() for DataFrames
# Import ... |
8,724 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Machine Learning Engineer Nanodegree
Model Evaluation & Validation
Project
Step1: Data Exploration
In this first section of this project, you will make a cursory investigation about the Bos... | Python Code:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
from sklearn.cross_validation import ShuffleSplit
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the Boston housing dataset
data = pd.rea... |
8,725 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PLEASE MAKE A COPY BEFORE CHANGING
Copyright 2021 Google LLC
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
... | Python Code:
# The Developer Key is used to retrieve a discovery document containing the
# non-public Full Circle Query v2 API. This is used to build the service used
# in the samples to make API requests. Please see the README for instructions
# on how to configure your Google Cloud Project for access to the Full Circ... |
8,726 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Spark RDD basic manipulation
RDD creation
We create a simple RDD by paralellizing a collection local to the driver (usually they would be created by fetching from external sources or reading... | Python Code:
# All numbers from 0 to 1000. Split in 4 partitions
numbers = sc.parallelize( range(0,1001), 4 )
print numbers.getNumPartitions()
print numbers.count()
print numbers.take(10)
Explanation: Spark RDD basic manipulation
RDD creation
We create a simple RDD by paralellizing a collection local to the driver (usu... |
8,727 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Chapter 9 – Up and running with TensorFlow
This notebook contains all the sample code and solutions to the exercices in chapter 9.
Setup
First, let's make sure this notebook works well in bo... | Python Code:
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
import tensorflow as tf
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
# to make this noteb... |
8,728 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Calculating Transit Timing Variations (TTV) with REBOUND
The following code finds the transit times in a two planet system. The transit times of the inner planet are not exactly periodic, du... | Python Code:
import rebound
import numpy as np
Explanation: Calculating Transit Timing Variations (TTV) with REBOUND
The following code finds the transit times in a two planet system. The transit times of the inner planet are not exactly periodic, due to planet-planet interactions.
First, let's import the REBOUND and n... |
8,729 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Weights and Biases (wandb) Demo
In deep learning, we perform a lot of model training especially for novel neural architectures. The problem is deep learning frameworks like PyTorch do not pr... | Python Code:
!pip install wandb
Explanation: Weights and Biases (wandb) Demo
In deep learning, we perform a lot of model training especially for novel neural architectures. The problem is deep learning frameworks like PyTorch do not provide sufficient tools to visualize input data, track the progress of our experiments... |
8,730 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Aprendizado Supervisionado
Testando modelos
Step1: Agora vamos analisar os modelos do KNN utilizando K=3 e K=10, mas calculando a acurácia na base de teste.
Step2: Apesar de ter dado valor... | Python Code:
#Imports necessários
from sklearn.datasets import load_iris
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
%matplotlib inline
data_iris = load_iris()
X = data_iris.data
y = data_iris.target
... |
8,731 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Recursion and Dictionaries
Dr. Chris Gwilliams
gwilliamsc@cardiff.ac.uk
Overview
Scripts in Python
Types
Methods and Functions
Flow control
Lists
Iteration
for loops
while loops
Now
Dicts
Tu... | Python Code:
empty_dict = {}
contact_dict = {
"name": "Homer",
"email": "homer@simpsons.com",
"phone": 999
}
print(contact_dict)
Explanation: Recursion and Dictionaries
Dr. Chris Gwilliams
gwilliamsc@cardiff.ac.uk
Overview
Scripts in Python
Types
Methods and Functions
Flow control
Lists
Iteration
for loops
... |
8,732 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Problem 1
Using the following dictionary
Step1: Problem 2
Write a function that finds the number of elements in a list (without using the built-in len function). Now, use %%timeit to compar... | Python Code:
my_dict = {
'a': 3,
'b': 2,
'c': 10,
'd': 7,
'e': 9,
'f' : 12,
'g' : 13
}
# print the keys with even values
print('keys with even values:')
for key, value in my_dict.items():
# modulo 2 == 0 implies the number is even
if value % 2 == 0:
print(key)
# pri... |
8,733 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hybrid coding scheme for diagonal Gaussians
```
Copyright 2022 Google LLC.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with... | Python Code:
import numpy as np
import scipy as sp
import scipy.stats
import matplotlib.pyplot as plt
from tqdm import tqdm
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
Explanation: Hybrid coding scheme for diagonal Gaussians
```
Copyright 2022 Google LLC.
Licensed under the Apache License, Version... |
8,734 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Analysis of Oscar-nominated Films
Step1: Descriptive Analysis
To better understand general trends in the data. This is a work in progress. last updated on
Step2: This can be more or less c... | Python Code:
import re
import numpy as np
import pandas as pd
import scipy.stats as stats
pd.set_option('display.float_format', lambda x: '%.3f' % x)
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(color_codes=True)
sb.set_palette("muted")
np.random.seed(sum(map(o... |
8,735 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Context
John Doe remarked in #AP1432 that there may be too much code in our application that isn't used at all. Before migrating the application to the new platform, we have to analyze which... | Python Code:
import pandas as pd
coverage = pd.read_csv("datasets/jacoco.csv")
coverage = coverage[['PACKAGE', 'CLASS', 'LINE_COVERED' ,'LINE_MISSED']]
coverage['LINES'] = coverage.LINE_COVERED + coverage.LINE_MISSED
coverage.head(1)
Explanation: Context
John Doe remarked in #AP1432 that there may be too much code in o... |
8,736 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
what we will learn ...
what is CNN
<hr/>
Convolution Operation
Relu layer
<hr/>
Pooling?
<hr/>
Flattening
<hr/>
Full Connection
1. What is CNN
----------------------------------------------... | Python Code:
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
Explanation: what we will learn ...
what is CNN
<hr/>
Convolution Operation
Relu layer
<hr/>
Pooling?
<hr/>
Flattening
<hr/>
Full Connec... |
8,737 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Defining and Run a Custom Analytical Model
Here you will be creating trivial analytical model following the API.
You can start by importing the necessary module components.
Step1: You also ... | Python Code:
# Module imports
from solarbextrapolation.map3dclasses import Map3D
from solarbextrapolation.analyticalmodels import AnalyticalModel
from solarbextrapolation.visualisation_functions import visualise
Explanation: Defining and Run a Custom Analytical Model
Here you will be creating trivial analytical model f... |
8,738 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Partitioner examples
This is a jupyter notebook with a few vignettes that present some of the Python partitioner package's functionality.
Note
Step1: Process the English Wiktionary to gener... | Python Code:
from partitioner import partitioner
from partitioner.methods import *
Explanation: Partitioner examples
This is a jupyter notebook with a few vignettes that present some of the Python partitioner package's functionality.
Note: Cleaning of text and determination of clauses occurs in the partitionText metho... |
8,739 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TD 7
Step1: La programmation dynamique est une façon de résoudre de manière similaire une classe de problèmes d'optimisation qui vérifie la même propriété. On suppose qu'il est possible de... | Python Code:
import pyensae
from jyquickhelper import add_notebook_menu
add_notebook_menu()
Explanation: TD 7 : Programmation dynamique et plus court chemin
End of explanation
import pyensae
pyensae.download_data("matrix_distance_7398.zip", website = "xd")
Explanation: La programmation dynamique est une façon de résou... |
8,740 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Now that I've streamlined the MCMC process, I am going to submit multiple chains simultaneously. This notebook will make multiple, similar config files, for broad comparison.
This ma... | Python Code:
import yaml
import copy
from os import path
import numpy as np
orig_cfg_fname = '/home/users/swmclau2//Git/pearce/bin/mcmc/nh_gg_sham_hsab_mcmc_config.yaml'
with open(orig_cfg_fname, 'r') as yamlfile:
orig_cfg = yaml.load(yamlfile)
orig_cfg
#this will enable easier string formatting
sbatch_template = #... |
8,741 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Preparing Scraped Data for Prediction
This notebook describes the process in which the raw films.csv and nominations.csv files are "wrangled" into a workable format for our classifier(s). At... | Python Code:
import re
import pandas as pd
import numpy as np
pd.set_option('display.float_format', lambda x: '%.3f' % x)
nominations = pd.read_csv('../data/nominations.csv')
# clean out some obvious mistakes...
nominations = nominations[~nominations['film'].isin(['2001: A Space Odyssey', 'Oliver!', 'Closely Observed T... |
8,742 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I have two tensors of dimension like 1000 * 1. I want to check how many of the elements are not equal in the two tensors. I think I should be able to do this in few lines like Numpy... | Problem:
import numpy as np
import pandas as pd
import torch
A, B = load_data()
cnt_not_equal = int(len(A)) - int((A == B).sum()) |
8,743 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
deepchem
Step1: Let's see what dataset looks like
Step2: One of the missions of deepchem is to form a synapse between the chemical and the algorithmic worlds
Step3: Now that we're oriente... | Python Code:
%load_ext autoreload
%autoreload 2
%pdb off
# set DISPLAY = True when running tutorial
DISPLAY = False
# set PARALLELIZE to true if you want to use ipyparallel
PARALLELIZE = False
import warnings
warnings.filterwarnings('ignore')
dataset_file= "../datasets/pdbbind_core_df.pkl.gz"
from deepchem.utils.save i... |
8,744 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Planning Algorithms
Do you remember on lesson 2 and 3 we discussed algorithms that basically solve MDPs? That is, find a policy given a exact representation of the environment. In this secti... | Python Code:
import numpy as np
import pandas as pd
import tempfile
import pprint
import json
import sys
import gym
from gym import wrappers
from subprocess import check_output
from IPython.display import HTML
Explanation: Planning Algorithms
Do you remember on lesson 2 and 3 we discussed algorithms that basically solv... |
8,745 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Naive Bayes Classifiers
Naive Bayes classifiers are a family of classifiers that are quite similar to the linear models discussed previously. However, they tend to be even faster in training... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Naive Bayes Classifiers
Naive Bayes classifiers are a family of classifiers that are quite similar to the linear models discussed previously. However, they tend to be even faster in training. The price paid for this efficien... |
8,746 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Session 3
Step2: Then we're going to try this with the MNIST dataset, which I've included a simple interface for in the libs module.
Step3: Let's take a look at what this returns
St... | Python Code:
# imports
%matplotlib inline
# %pylab osx
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
# Some additional libraries which we'll use just
# to produce some visualizations of our training
from libs.utils import montag... |
8,747 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compute source power spectral density (PSD) of VectorView and OPM data
Here we compute the resting state from raw for data recorded using
a Neuromag VectorView system and a custom OPM system... | Python Code:
# Authors: Denis Engemann <denis.engemann@gmail.com>
# Luke Bloy <luke.bloy@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
from mne.filter import next_fast_len
import mne
print(__doc__)
data_path = mne.datasets.opm.data_path()
subject =... |
8,748 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: ノートブックで TensorBoard を使用する
<table class="tfo-notebook-buttons" align="left">
<td> <a target="_blank" href="https
Step2: TensorFlow、dateti... | 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... |
8,749 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
It appears the two states are equivalent, which means this is a single state. This is the spacetime equivalent of a fair coin, so this is the desired result. Which makes me feel better about... | Python Code:
state_overlay_diagram(field, random_states.get_causal_field(), t_max = 50, x_max = 50)
for state in random_states.causal_states():
print state.plc_configs()
for state in random_states.causal_states():
print state.morph()
t_trans = random_states.all_transitions(zipped = False)[1]
print np.unique(t_t... |
8,750 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Progress report
Neural Networks
Sara Jones
Abstract
This project will take hand written digits 0 to 9 and recognize them through a computer-learning program. The neural network will require ... | Python Code:
import numpy as np
import math
import random
import string
from scipy import optimize
%matplotlib inline
import matplotlib.pyplot as plt
from IPython.html.widgets import interact
from sklearn.datasets import load_digits
digits = load_digits()
print(digits.data.shape)
Explanation: Progress report
Neural Net... |
8,751 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introdução
Esse notebook traz as analises pedidas na disciplina Biologia Evolutiva - Bio507
Neste tutorial vamos utilizar a a linguagem Python 2.7 e os pacotes numpy, pandas, dendropy e matp... | Python Code:
import numpy as np
import pandas as pd
import dendropy as dp
import matplotlib as mpl
Explanation: Introdução
Esse notebook traz as analises pedidas na disciplina Biologia Evolutiva - Bio507
Neste tutorial vamos utilizar a a linguagem Python 2.7 e os pacotes numpy, pandas, dendropy e matplotlib
Leitura de... |
8,752 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example code that shows how to plot the function value and a decision
boundary for a simple logistic regression model using python
Author
Step1: Create the domain for the plot
Step2: Make ... | Python Code:
%matplotlib inline
import numpy as np
from scipy.special import expit
import matplotlib.pyplot as plt
# define our hypothesis (vectorized!)
def f(x):
return expit(np.matrix([0, 1, -.5,.5])*x);
Explanation: Example code that shows how to plot the function value and a decision
boundary for a simple logist... |
8,753 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interact Exercise 4
Imports
Step2: Line with Gaussian noise
Write a function named random_line that creates x and y data for a line with y direction random noise that has a normal distribut... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
Explanation: Interact Exercise 4
Imports
End of explanation
def random_line(m, b, sigma, size=10):
Create a line y = m*x + b + N(0,sigm... |
8,754 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
My first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementatio... | Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Explanation: My first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the... |
8,755 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
def sghmc(Y, X, stogradU, M, eps, m, theta, C, V)
Step1: Correct coefficients
Step2: Our code - SGHMC
Step3: Our code - Gradient descent
Step5: Cliburn's code | Python Code:
# Load data
X = np.concatenate((np.ones((pima.shape[0],1)),pima[:,0:8]), axis=1)
Y = pima[:,8]
Xs = (X - np.mean(X, axis=0))/np.concatenate((np.ones(1),np.std(X[:,1:], axis=0)))
n, p = X.shape
M = np.identity(p)
### HMC version
def logistic(x):
return 1/(1+np.exp(-x))
def U(theta, Y, X):
return - (... |
8,756 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about emb... | Python Code:
import time
import numpy as np
import tensorflow as tf
import utils
Explanation: Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural lang... |
8,757 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
If you have not already read it, you may want to start with the first tutorial
Step1: Here we will again load some pre-generated data meant to represent well-sampled, precise radial velocit... | Python Code:
import astropy.coordinates as coord
import astropy.table as at
from astropy.time import Time
import astropy.units as u
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
import corner
import pymc3 as pm
import pymc3_ext as pmx
import exoplanet as xo
import arviz as az
import thejoker as ... |
8,758 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
* Visualizing of genetic similarity with Lightning + GraphX *
Setup lightning
Step1: Load structure similarity data
Public data from http
Step2: Show the network (unlabeled)
Step3: Show t... | Python Code:
%libraryDependencies += "org.viz.lightning" %% "lightning-scala" % "0.1.6"
%update
import org.viz.lightning._
import org.apache.spark.graphx._
val lgn = Lightning(host="https://lightning-spark-summit.herokuapp.com" )
lgn.enableNotebook()
Explanation: * Visualizing of genetic similarity with Lightning + Gra... |
8,759 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Wk1.0
Warm-up
Step1: 3. Extend your program to n objects. How many different combinations do I have for 5 objects? How about 15? What is the max number of objects I could calculate for if I... | Python Code:
count = 1
for elem in range(1, 3 + 1):
count *= elem
print(count)
Explanation: Wk1.0
Warm-up: I got 32767 problems and overflow is one of them.
1. Swap the values of two variables, a and b without using a temporary variable.
2. Suppose I had six different sodas. In how many different combinations ... |
8,760 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<div align="right">Python 3.6</div>
Testing The Google Maps Subclass
This notebook was created to test objects associated with extracting information into a Dataframe using the Google Maps A... | Python Code:
# general libraries
import pandas as pd
## required for Google Maps API code
import os
## for larger data and/or make many requests in one day - get Google API key and use these lines:
# os.environ["GOOGLE_API_KEY"] = "YOUR_GOOGLE_API_Key"
## for better security (PROD environments) - install key to server ... |
8,761 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Self-Driving Car Engineer Nanodegree
Deep Learning
Project
Step1: Step 1
Step2: Include an exploratory visualization of the dataset
Visualize the German Traffic Signs Dataset using the pic... | Python Code:
# Load pickled data
import pickle
from keras.datasets import cifar10
from sklearn.model_selection import train_test_split
# TODO: Fill this in based on where you saved the training and testing data
#training_file = "traffic-signs-data/train.p"
#validation_file = "traffic-signs-data/valid.p"
#testing_file =... |
8,762 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pandas Examples
http
Step1: Let's read data from the BRFSS
Step2: If we group by sex, we get a DataFrameGroupBy object.
Step3: If we select a particular column from the GroupBy, we get a ... | Python Code:
from __future__ import print_function, division
%matplotlib inline
import numpy as np
import nsfg
import first
import analytic
import thinkstats2
import seaborn
Explanation: Pandas Examples
http://thinkstats2.com
Copyright 2017 Allen B. Downey
MIT License: https://opensource.org/licenses/MIT
End of explana... |
8,763 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step3: Notation
$Y$ generic random variable
$U$ latent random variable
$V$ residual random variable
$X$ predictor
Parameters
$\eta$ and $\nu$ generic parameters
$\mu=E[Y]$ mean parameter
$\g... | Python Code:
model =
data {
int<lower=0> N; //nr subjects
real<lower=0> k;
real<lower=0> t;
}generated quantities{
real<lower=0> y;
y=gamma_rng(k,1/t);
}
smGammaGen = pystan.StanModel(model_code=model)
model =
data {
int<lower=0> N; //nr subjects
real<lower=0> y[N];
}parameters{
real<l... |
8,764 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Bayesian interpretation of medical tests
This notebooks explores several problems related to interpreting the results of medical tests.
Copyright 2016 Allen Downey
MIT License
Step3: Medica... | Python Code:
from __future__ import print_function, division
from thinkbayes2 import Pmf, Suite
from fractions import Fraction
Explanation: Bayesian interpretation of medical tests
This notebooks explores several problems related to interpreting the results of medical tests.
Copyright 2016 Allen Downey
MIT License: htt... |
8,765 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Backpropagation Tutorial
(C) 2019 by Damir Cavar
Version
Step1: For plots of curves and functions we will use pyplot from matplotlib. We will import it here
Step2: Non-linearity Function a... | Python Code:
import numpy as np
Explanation: Backpropagation Tutorial
(C) 2019 by Damir Cavar
Version: 0.1, November 2019
Download: This and various other Jupyter notebooks are available from my GitHub repo.
Introduction
For more details on Backpropagation and its use in Neural Networks see Rumelhart, Hinton, and Willi... |
8,766 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compute LCMV beamformer on evoked data
Compute LCMV beamformer solutions on evoked dataset for three different choices
of source orientation and stores the solutions in stc files for visuali... | Python Code:
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import numpy as np
import mne
from mne.datasets import sample
from mne.beamformer import lcmv
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample... |
8,767 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<small><i>This notebook was prepared by Donne Martin. Source and license info is on GitHub.</i></small>
Challenge Notebook
Problem
Step1: Unit Test | Python Code:
%run ../bst/bst.py
%load ../bst/bst.py
def in_order_traversal(node, visit_func):
# TODO: Implement me
pass
def pre_order_traversal(node, visit_func):
# TODO: Implement me
pass
def post_order_traversal(node, visit_func):
# TODO: Implement me
pass
Explanation: <small><i>This notebook ... |
8,768 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Running simple example through EC2
start downloading linking_EC2
Tutorial for running liknking_EC2 see
Step1: start the actual cluster
Step2: login to main node and run
Step3: Terimante t... | Python Code:
%%bash
. ~/.bashrc
pip install --upgrade git+https://git@github.com/JonasWallin/linkingEC2
from linkingEC2 import LinkingHandler
from ConfigParser import ConfigParser
config = ConfigParser()
starfigconfig_folder = "/Users/jonaswallin/.starcluster/"
config.read(starfigconfig_folder + "config")
acess_key_id... |
8,769 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Micromagnetic standard problem 3
Author
Step1: Firstly, we import all necessary modules.
Step2: The following two functions are used for initialising the system's magnetisation [1].
Step3:... | Python Code:
!rm -rf standard_problem3/ # Delete old result files (if any).
Explanation: Micromagnetic standard problem 3
Author: Marijan Beg, Ryan Pepper
Date: 11 May 2016
Problem specification
This problem is to calculate the single domain limit of a cubic magnetic particle. This is the size $L$ of equal energy for ... |
8,770 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
If not yet available some libraries and their python bindings have to be installed
Step1: Create a meshed screen with a central hole
The screen is rectangular (215*150 mm) with a 4mm centr... | Python Code:
import numpy as np
from scipy import constants
import pygmsh
from MeshedFields import *
Explanation: If not yet available some libraries and their python bindings have to be installed :<br>
- gmsh (best installed globally through package management system)
- python3 -m pip install pygmsh --user
- VTK (best... |
8,771 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Language Translation
In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset o... | Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
Explanation: Language Translation
In this project, you’re going ... |
8,772 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Binning
Following script is used to bin the data and check stats of participants
Step1: Reading scan json files and extracting scan parameters
Step2: Convention
Step3: Group Stats
Th... | Python Code:
import pandas as pd
import numpy as np
import json
import string
df = pd.read_csv('/home1/varunk/data/ABIDE1/RawDataBIDs/composite_phenotypic_file.csv') # , index_col='SUB_ID'
df = df.sort_values(['SUB_ID'])
df
Explanation: Data Binning
Following script is used to bin the data and check stats of participan... |
8,773 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Computer Vision to find chess squares in a screenshot
Link to Github source code
The goal is to build a Reddit bot that listens on /r/chess for posts with an image in it (perhaps checking al... | Python Code:
import tensorflow as tf
import numpy as np
np.set_printoptions(suppress=True)
sess = tf.InteractiveSession()
Explanation: Computer Vision to find chess squares in a screenshot
Link to Github source code
The goal is to build a Reddit bot that listens on /r/chess for posts with an image in it (perhaps checki... |
8,774 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Variational Autoencoder
This scripts contains module for implementing variational autoencoder, the module contains
Step2: mnist_loader
Step3: Test mnist data
Step4: We are generating synt... | Python Code:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import time
from tensorflow.python.client import timeline
%matplotlib inline
Explanation: Variational Autoencoder
This scri... |
8,775 | 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)
====================================... | 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 ... |
8,776 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Question_3-1-3_Multiclass_Ridge
Janet Matsen
Code notes
Step1: Prepare MNIST training data
Step2: Dev | Python Code:
import numpy as np
import matplotlib as mpl
%matplotlib inline
import pandas as pd
import seaborn as sns
from mnist import MNIST # public package for making arrays out of MINST data.
import sys
sys.path.append('../code/')
from ridge_regression import RidgeBinary
from hyperparameter_explorer import Hyperpa... |
8,777 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
What is machine learning?
One definition
Step1: What are the features?
- TV
Step2: Linear regression
Pros
Step3: Splitting X and y into training and testing sets
Step4: Linear regression... | Python Code:
import pandas as pd
# read CSV file directly from a URL and save the results
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
# display the first 5 rows
data.head()
data.shape
Explanation: What is machine learning?
One definition: "Machine learning is the semi-automat... |
8,778 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using folium.colormap
A few examples of how to use folium.colormap in choropleths.
Let's load a GeoJSON file, and try to choropleth it.
Step2: Self-defined
You can build a choropleth in usi... | Python Code:
import json
import pandas as pd
us_states = os.path.join('data', 'us-states.json')
US_Unemployment_Oct2012 = os.path.join('data', 'US_Unemployment_Oct2012.csv')
geo_json_data = json.load(open(us_states))
unemployment = pd.read_csv(US_Unemployment_Oct2012)
unemployment_dict = unemployment.set_index('State')... |
8,779 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: 変分推論を使用した一般化線形混合効果モデルの適合
<table class="tfo-notebook-buttons" align="left">
<t... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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... |
8,780 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
BigQuery command-line tool
The BigQuery command-line tool is installed as part of the Cloud SDK and can be used to interact with BigQuery. When you use CLI commands in a notebook, the comman... | Python Code:
!bq help
Explanation: BigQuery command-line tool
The BigQuery command-line tool is installed as part of the Cloud SDK and can be used to interact with BigQuery. When you use CLI commands in a notebook, the command must be prepended with a !.
View available commands
To view the available commands for the Bi... |
8,781 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Read SST, Mask and Calculate global mean
In this notebook, we will carry out the following basic operations
* have a quick visualization of spatial data
* use mask array to mask out land
* c... | Python Code:
%matplotlib inline
import numpy as np
from netCDF4 import Dataset # http://unidata.github.io/netcdf4-python/
import matplotlib.pyplot as plt # to generate plots
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 15, 9
Explanation: Read SST, Mask and Calculate global mean
I... |
8,782 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Improving the Text Classifier
Goals
Try Random Forest on our Sentiment Data
Try Support Vector Machines on our Sentiment Data
Introduction to Hyperparameters
Introduction
There are three way... | Python Code:
import pandas as pd
import numpy as np
from sklearn.model_selection import cross_val_score
df = pd.read_csv('../scikit/tweets.csv')
target = df['is_there_an_emotion_directed_at_a_brand_or_product']
text = df['tweet_text']
# We need to remove the empty rows from the text before we pass into CountVectorizer
... |
8,783 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Measuring a Multiport Device with a 2-Port Network Analyzer
Introduction
In microwave measurements, one commonly needs to measure a n-port device with a m-port network analyzer ($m<n$ of cou... | Python Code:
import skrf as rf
from itertools import combinations
%matplotlib inline
from pylab import *
rf.stylely()
Explanation: Measuring a Multiport Device with a 2-Port Network Analyzer
Introduction
In microwave measurements, one commonly needs to measure a n-port device with a m-port network analyzer ($m<n$ of c... |
8,784 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 The TensorFlow Authors.
Step1: Keras でのプルーニングの例
<table class="tfo-notebook-buttons" align="left">
<td> <a target="_blank" href="https
Step2: プルーニングを使用せずに、MNIST のモデルをトレ... | 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... |
8,785 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
You have already seen that when you change the input value to a function, you often get a different output. For instance, consider an add_five() function that just adds five to... | Python Code:
print(2 > 3)
Explanation: Introduction
You have already seen that when you change the input value to a function, you often get a different output. For instance, consider an add_five() function that just adds five to any number and returns the result. Then add_five(7) will return an output of 12 (=7+5), a... |
8,786 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture 7
Step1: Anytime you see a statement that starts with import, you'll recognize that the programmer is pulling in some sort of external functionality not previously available to Pyth... | Python Code:
import random
Explanation: Lecture 7: Vectorized Programming
CSCI 1360E: Foundations for Informatics and Analytics
Overview and Objectives
We've covered loops and lists, and how to use them to perform some basic arithmetic calculations. In this lecture, we'll see how we can use an external library to make ... |
8,787 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Single trial linear regression analysis with the LIMO dataset
Here we explore the structure of the data contained in the
LIMO dataset.
This example replicates and extends some of the main an... | Python Code:
# Authors: Jose C. Garcia Alanis <alanis.jcg@gmail.com>
#
# License: BSD-3-Clause
import numpy as np
import matplotlib.pyplot as plt
from mne.datasets.limo import load_data
from mne.stats import linear_regression
from mne.viz import plot_events, plot_compare_evokeds
from mne import combine_evoked
print(__d... |
8,788 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook is part of the clifford documentation
Step1: This creates an algebra with the required signature and imports the basis blades into the current workspace. We can check our metr... | Python Code:
from clifford.g3c import *
blades
Explanation: This notebook is part of the clifford documentation: https://clifford.readthedocs.io/.
Example 1 Interpolating Conformal Objects
In this example we will look at a few of the tools provided by the clifford package for (4,1) conformal geometric algebra (CGA) and... |
8,789 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Hyperparameter Optimization xgboost
What the options there're for tuning?
* GridSearch
* RandomizedSearch
All right!
Xgboost has about 20 params
Step1: Modeling
Step2: Tuning hyperparmeter... | Python Code:
import pandas as pd
import xgboost as xgb
import numpy as np
import seaborn as sns
from hyperopt import hp
from hyperopt import hp, fmin, tpe, STATUS_OK, Trials
%matplotlib inline
train = pd.read_csv('bike.csv')
train['datetime'] = pd.to_datetime( train['datetime'] )
train['day'] = train['datetime'].map(la... |
8,790 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: can we predict which learner will be best on a dataset from dataset properties?
Step2: can we predict the best score achievable on a dataset from dataset properties? | Python Code:
# get best classifier for each dataset
from tqdm import tqdm
best_method = dict()
for i,(dataset, group_data) in enumerate(tqdm(data.groupby('dataset'))):
best_method[dataset] = group_data['classifier'][np.argmax(group_data['accuracy'])]
# print(best_method)
# make new dataset combining metafeatures a... |
8,791 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Desription
Simulation of data-aided frequency synchronization
QPSK symbols are sampled, pulse shaped and transmitted
Uniformly distributed frequency distortion is added, before frequency is... | Python Code:
# importing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# showing figures inline
%matplotlib inline
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=True)
matplotlib.rc('figure', figsize=(28, 8) )
Explanation: Desription
Simulation of data-aid... |
8,792 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Examples
Step1: Try It Yourself
Go to the section 4.4. Numeric Types in the Python 3 documentation at https
Step2: Variables can be reassigned.
Step3: The ability to reassign variable val... | Python Code:
# The interpreter can be used as a calculator, and can also echo or concatenate strings.
3 + 3
3 * 3
3 ** 3
3 / 2 # classic division - output is a floating point number
# Use quotes around strings
'dogs'
# + operator can be used to concatenate strings
'dogs' + "cats"
print('Hello World!')
Explanation: Exam... |
8,793 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
# Defensive programming (2)
We have seen the basic idea that we can insert
assert statments into code, to check that the
results are what we expect, but how can we test
software... | Python Code:
def test_range_overlap():
assert range_overlap([(-3.0, 5.0), (0.0, 4.5), (-1.5, 2.0)]) == (0.0, 2.0)
assert range_overlap([ (2.0, 3.0), (2.0, 4.0) ]) == (2.0, 3.0)
assert range_overlap([ (0.0, 1.0), (0.0, 2.0), (-1.0, 1.0) ]) == (0.0, 1.0)
Explanation: # Defensive programming (2)
We have see... |
8,794 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Planning
Chapters 10-11
This notebook serves as supporting material for topics covered in Chapter 10 - Classical Planning and Chapter 11 - Planning and Acting in the Real World from the book... | Python Code:
from planning import *
from notebook import psource
Explanation: Planning
Chapters 10-11
This notebook serves as supporting material for topics covered in Chapter 10 - Classical Planning and Chapter 11 - Planning and Acting in the Real World from the book Artificial Intelligence: A Modern Approach.
This n... |
8,795 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Matching Market
This simple model consists of a buyer, a supplier, and a market.
The buyer represents a group of customers whose willingness to pay for a single unit of the good is captured... | Python Code:
import random as rnd
class Supplier():
def __init__(self):
self.wta = []
# the supplier has n quantities that they can sell
# they may be willing to sell this quantity anywhere from a lower price of l
# to a higher price of u
def set_quantity(self,n,l,u):
for i in r... |
8,796 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Comparing Encoder-Decoders Analysis
Model Architecture
Step1: Perplexity on Each Dataset
Step2: Loss vs. Epoch
Step3: Perplexity vs. Epoch
Step4: Generations
Step5: BLEU Analysis
Step6:... | Python Code:
report_files = ["/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing6_200_512_04drb/encdec_noing6_200_512_04drb.json", "/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing6_bow_200_512_04drb/encdec_noing6_bow_200_512_04drb.json"]
log_files = ["/Users/bking/Idea... |
8,797 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
6.2 - Using a pre-trained model with Keras
In this tutorial we will load the model we trained in the previous section, along with the training data and mapping dictionaries, and use it to ge... | Python Code:
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.callbacks import ModelCheckpoint
from keras.utils import np_utils
import sys
import re
import pickle
Explanation: 6.2 - Using a pre-trained model w... |
8,798 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
5.2 - Using convnets with small datasets
This notebook contains the code sample found in Chapter 5, Section 2 of Deep Learning with Python. Note that the original text features far more cont... | Python Code:
import os, shutil
# The path to the directory where the original
# dataset was uncompressed
original_dataset_dir = '/Users/fchollet/Downloads/kaggle_original_data'
# The directory where we will
# store our smaller dataset
base_dir = '/Users/fchollet/Downloads/cats_and_dogs_small'
os.mkdir(base_dir)
# Direc... |
8,799 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Twitter
Step3: So above we use the LIKE statement in conjunction with the % sign. The LIKE operator is going to match a string while the % matches any string of 0 or greater length. ... | Python Code:
# BE SURE TO RUN THIS CELL BEFORE ANY OF THE OTHER CELLS
import psycopg2
import pandas as pd
from skbio.diversity.alpha import shannon
# query database
statement =
SELECT *
FROM twitter.job
WHERE description LIKE '%New York City%';
try:
connect_str = "dbname='twitter' user='dsa_ro_user' host='dbase.d... |
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