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7,200 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
STA 208
Step3: The response variable is quality.
Step4: Exercise 2.1 (5 pts) Compare the leave-one-out risk with the empirical risk for linear regression, on this dataset.
Step5: Exercise... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import LeaveOneOut
from sklearn import linear_model, neighbors
%matplotlib inline
plt.style.use('ggplot')
# dataset path
data_dir = "."
sample_data = pd.read_csv(data_dir+"/hw1.csv", delimiter=',')
sample_da... |
7,201 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Dataframes ( Pandas ) and Plotting ( Matplotlib/Seaborn )
Written by Jin Cheong & Luke Chang
In this lab we are going to learn how to load and manipulate datasets in a dataframe format using... | Python Code:
# matplotlib inline is an example of 'cell magic' and
# enables plotting IN the notebook and not opening another window.
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
Explanation: Dataframes ( Pandas ) and Plotting ( Matplotlib/Seaborn )
W... |
7,202 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<img style='float
Step1: Skill 1
Step2: Skill 2
Step3: Skill 3
Step4: Normalized Taylor diagrams
The radius is model standard deviation error divided by observations deviation,
azimuth ... | Python Code:
import os
try:
import cPickle as pickle
except ImportError:
import pickle
run_name = '2015-08-17'
fname = os.path.join(run_name, 'config.pkl')
with open(fname, 'rb') as f:
config = pickle.load(f)
import numpy as np
from pandas import DataFrame, read_csv
from utilities import to_html, save_html,... |
7,203 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Microbiome machine learning analysis
Setup
Import the calour module
Step1: Regression
Loading the data
We will use the data from Qitta study 103 (https
Step2: Process the data
Get rid of t... | Python Code:
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from sklearn.model_selection import RepeatedStratifiedKFold
from calour.training import plot_scatter, plot_roc, plot_cm
import calour as ca
%matplotlib notebook
Explanation: Microbiome machine learning analysis
Setup
Import the calo... |
7,204 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
主题模型
王成军
wangchengjun@nju.edu.cn
计算传播网 http
Step1: Download data
http
Step2: Build the topic model
Step3: We can see the list of topics a document refers to
by using the model[doc] syntax... | Python Code:
%matplotlib inline
from __future__ import print_function
from wordcloud import WordCloud
from gensim import corpora, models, similarities, matutils
import matplotlib.pyplot as plt
import numpy as np
Explanation: 主题模型
王成军
wangchengjun@nju.edu.cn
计算传播网 http://computational-communication.com
2014年高考前夕，百度“基于海... |
7,205 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Multi-indexing
When dealing with Series data, it is often useful to index each element of the series with multiple labels and then select and aggregrate data based on these indices. For exam... | Python Code:
from thunder import Series
from numpy import arange, array
data = tsc.loadSeriesFromArray(arange(12))
data.first()
Explanation: Multi-indexing
When dealing with Series data, it is often useful to index each element of the series with multiple labels and then select and aggregrate data based on these indice... |
7,206 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Learning with TensorFlow
Credits
Step1: First reload the data we generated in 1_notmist.ipynb.
Step2: Reformat into a shape that's more adapted to the models we're going to train
Step... | Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
import cPickle as pickle
import numpy as np
import tensorflow as tf
Explanation: Deep Learning with TensorFlow
Credits: Forked from TensorFlow by Google
Setup
Refer to the setup instructions.
Exerci... |
7,207 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Sveučilište u Zagrebu
Fakultet elektrotehnike i računarstva
Strojno učenje 2018/2019
http
Step1: 1. Klasifikator stroja potpornih vektora (SVM)
(a)
Upoznajte se s razredom svm.SVC, koja u... | Python Code:
import numpy as np
import scipy as sp
import pandas as pd
import mlutils
import matplotlib.pyplot as plt
%pylab inline
Explanation: Sveučilište u Zagrebu
Fakultet elektrotehnike i računarstva
Strojno učenje 2018/2019
http://www.fer.unizg.hr/predmet/su
Laboratorijska vježba 3: Stroj potpornih vektora i al... |
7,208 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<center> Chukwuemeka Mba-Kalu </center> <center> Joseph Onwughalu </center>
<center> An Analysis of the Brazilian Economy between 2000 and 2012 </center>
<center> Final Project In Partial Fu... | Python Code:
# Inportant Packages
import pandas as pd
import matplotlib.pyplot as plt
import sys
import datetime as dt
print('Python version is:', sys.version)
print('Pandas version:', pd.__version__)
print('Date:', dt.date.today())
Explanation: <center> Chukwuemeka Mba-Kalu </center> <center> Joseph Onwughalu </center... |
7,209 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Test external lookup table (multi-dimensional np array) code for rule 18
Step1: External transduction of CA.get_spacetime() | Python Code:
A = 2
r = 1
table = lookup_table(18, 2, 1)
R = 2*r + 1
scan = tuple(np.arange(0,A)[::-1])
for a in product(scan, repeat = R):
print a, table[a]
x = [0,1,1,0]
print neighborhood(x, 1)
for item in neighborhood(x, 1):
print item
print max_rule(2,1)
print example.current_state()
example.evolve(1)
print... |
7,210 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Precipitation in the Meteorology component
Goal
Step1: Programmatically create a file holding the precipitation rate time series. This will mimic what I'll need to do in WMT, where I'll hav... | Python Code:
mps_to_mmph = 1000 * 3600
Explanation: Precipitation in the Meteorology component
Goal: In this example, I give the Meteorology component a time series of precipitation values and check whether it produces output when the model state is updated.
Define a helpful constant:
End of explanation
import numpy as... |
7,211 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lower Star Filtrations
Step1: Overview of 0D Persistence for Point Clouds
Step2: Piecewise Linear Lower Star Filtrations
First, we define a lower star time series filtration function. The... | Python Code:
%matplotlib notebook
import numpy as np
from scipy import ndimage
from ripser import ripser
from persim import plot_diagrams as plot_dgms
import matplotlib.pyplot as plt
from scipy import sparse
import time
import PIL
from mpl_toolkits.mplot3d import Axes3D
import sys
import ipywidgets as widgets
from IPy... |
7,212 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TLS handshake overview
This is the standard, modern TLS 1.2 handshake
Step1: (C) ---> (S) ClientHello
Step2: (C) <--- (S) ServerHello
Step3: (C) <--- (S) Certificate
Step4: (C) <--- (S) ... | Python Code:
# We're going to parse several successive records from the passive listening of a standard TLS handshake
from scapy.all import *
load_layer('tls')
Explanation: TLS handshake overview
This is the standard, modern TLS 1.2 handshake:
<img src="images/handshake_tls12.png" alt="Handshake TLS 1.2" width="400"/>
... |
7,213 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Predicting Life
Step1: <h2>What is a Random Forest? </h2>
As with all the important questions in life, this is best deferred to the Wikipedia page. A random forest is an ensemble of decisio... | Python Code:
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import normalize
import random
test=pd.read_csv("test.csv")
test.head()
mData=pd.read_csv("train.csv")
mData.head()
mData = mData.drop(["Passenger... |
7,214 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Additional forces
REBOUND is a gravitational N-body integrator. But you can also use it to integrate systems with additional, non-gravitational forces.
This tutorial gives you a very quick o... | Python Code:
import rebound
rebound.reset()
rebound.integrator = "whfast"
rebound.add(m=1.)
rebound.add(m=1e-6,a=1.)
rebound.move_to_com() # Moves to the center of momentum frame
Explanation: Additional forces
REBOUND is a gravitational N-body integrator. But you can also use it to integrate systems with additional, n... |
7,215 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: DDSP Processor Demo
This notebook provides an introduction to the signal Processor() object. The main object type in the DDSP library, it is the base class used for Sy... | Python Code:
# Copyright 2021 Google LLC. All Rights Reserved.
#
# 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 applic... |
7,216 | Given the following text description, write Python code to implement the functionality described.
Description:
Program to implement Simpson 's 3/8 rule
Given function to be integrated ; Function to perform calculations ; Calculates value till integral limit ; driver function
| Python Code:
def func(x ) :
return(float(1 ) /(1 + x * x ) )
def calculate(lower_limit , upper_limit , interval_limit ) :
interval_size =(float(upper_limit - lower_limit ) / interval_limit )
sum = func(lower_limit ) + func(upper_limit ) ;
for i in range(1 , interval_limit ) :
if(i % 3 == 0 ) :
sum =... |
7,217 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
My playing with the Kaggle titanic challenge.
I got lots of the ideas for this first Kaggle advanture from here
Step1: Let's see where they got on
Step2: OK, so clearly there were more peo... | Python Code:
import pandas as pd
from pandas import Series, DataFrame
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set_style("whitegrid")
train_df = pd.read_csv("train.csv",dtype={"Age":np.float64},)
train_df.head()
# find how many ages
train_df['Age'].count()
# how m... |
7,218 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
K-Means
Goal
Unsupervised learning algorithms look for structure in unlabelled data. One of the common objective is to find clusters. Clusters are groups of data that are similar according t... | Python Code:
import random
import time
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from fct import normalize_min_max, plot_2d, plot_clusters
Explanation: K-Means
Goal
Unsupervised learning algorithms look for structure in unlabelled data. One of the common objective is to find clusters. Clust... |
7,219 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Control Ops Tutorial
In this tutorial we show how to use control flow operators in Caffe2 and give some details about their underlying implementations.
Conditional Execution Using NetBuilder... | Python Code:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from caffe2.python import workspace
from caffe2.python.core import Plan, to_execution_step, Net
from caffe2.python.net_builder import ops, NetBuilder
Explanat... |
7,220 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Signal denoising using RNNs in PyTorch
In this post, I'll use PyTorch to create a simple Recurrent Neural Network (RNN) for denoising a signal. I started learning RNNs using PyTorch. However... | Python Code:
import numpy as np
import math, random
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(0)
Explanation: Signal denoising using RNNs in PyTorch
In this post, I'll use PyTorch to create a simple Recurrent Neural Network (RNN) for denoising a signal. I started learning RNNs using PyTorch. How... |
7,221 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Training at scale with the Vertex AI Training Service
Learning Objectives
Step1: Change the following cell as necessary
Step2: Confirm below that the bucket is regional and its region equa... | Python Code:
import os
from google.cloud import bigquery
Explanation: Training at scale with the Vertex AI Training Service
Learning Objectives:
1. Learn how to organize your training code into a Python package
1. Train your model using cloud infrastructure via Google Cloud Vertex AI Training Service
1. (optional... |
7,222 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Independent Analysis - Srinivas (handle
Step1: we've now dropped the last of the discrete numerical inexplicable data, and removed children from the mix
Extracting the samples we are intere... | Python Code:
# Standard
import pandas as pd
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
# Dimensionality reduction and Clustering
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.cluster import MeanShift, estimate_bandwidth
from sklearn import manifold, dat... |
7,223 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Notebook is a revised version of notebook from Amy Wu
E2E ML on GCP
Step1: Restart the kernel
After you install the additional packages, you need to restart the notebook kernel so it can fi... | Python Code:
import os
# The Vertex AI Workbench Notebook product has specific requirements
IS_WORKBENCH_NOTEBOOK = os.getenv("DL_ANACONDA_HOME")
IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(
"/opt/deeplearning/metadata/env_version"
)
# Vertex AI Notebook requires dependencies to be installed with '--user'
U... |
7,224 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Interact Exercise 3
Imports
Step2: Using interact for animation with data
A soliton is a constant velocity wave that maintains its shape as it propagates. They arise from non-linear wave eq... | Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
Explanation: Interact Exercise 3
Imports
End of explanation
def soliton(x, t, c, a):
Return phi(x, t) for a soliton wave with cons... |
7,225 | Given the following text description, write Python code to implement the functionality described.
Description:
Remove all nodes which don 't lie in any path with sum>= k
A utility function to create a new Binary Tree node with given data ; print the tree in LVR ( Inorder traversal ) way . ; Main function which truncate... | Python Code:
class newNode :
def __init__(self , data ) :
self . data = data
self . left = self . right = None
def Print(root ) :
if(root != None ) :
Print(root . left )
print(root . data , end = "▁ ")
Print(root . right )
def pruneUtil(root , k , Sum ) :
if(root == None ) :
return N... |
7,226 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Głęboka sieć neuronowa w aspektowej analizie wydźwięku
Tomek Korbak
24 maja 2016
Problem
Wytrenować klasyfikator, który dostając na wejściu zdanie języka polskiego, zwróci jego wydźwięk, to ... | Python Code:
import json
from itertools import chain
from pprint import pprint
from time import time
import os
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from gensim.models import Word2Vec
from gensim.corpora.dictionary import Dictionary
os.environ['... |
7,227 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Recursive least squares
Recursive least squares is an expanding window version of ordinary least squares. In addition to availability of regression coefficients computed recursively, the rec... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from pandas_datareader.data import DataReader
np.set_printoptions(suppress=True)
Explanation: Recursive least squares
Recursive least squares is an expanding window version of ordinary lea... |
7,228 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
Introduction
There are multiple reasons for analyzing a version control system like your Git repository. See for example Adam Tornhill's book "Your Code as a Crime Scene" or his... | Python Code:
import git
GIT_REPO_PATH = r'../../spring-petclinic/'
repo = git.Repo(GIT_REPO_PATH)
git_bin = repo.git
git_bin
Explanation: Introduction
Introduction
There are multiple reasons for analyzing a version control system like your Git repository. See for example Adam Tornhill's book "Your Code as a Crime Scen... |
7,229 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Session 0
Step1: Now press 'a' or 'b' to create new cells. You can also use the toolbar to create new cells. You can also use the arrow keys to move up and down.
<a name="kernel"></a>
Ker... | Python Code:
4*2
Explanation: Session 0: Preliminaries with Python/Notebook
<p class="lead">
Parag K. Mital<br />
<a href="https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info">Creative Applications of Deep Learning w/ Tensorflow</a><br />
<a href="https://www.kadenze.com/partners... |
7,230 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example
Step1: Say $f(t) = t * exp(- t)$, and $F(s)$ is the Laplace transform of $f(t)$. Let us first evaluate this transform using sympy.
Step2: Suppose we are confronted with a dataset $... | Python Code:
from symfit import (
variables, parameters, Model, Fit, exp, laplace_transform, symbols,
MatrixSymbol, sqrt, Inverse, CallableModel
)
import numpy as np
import matplotlib.pyplot as plt
Explanation: Example: Matrix Equations using Tikhonov Regularization
This is an example of the use of matrix expressio... |
7,231 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Character level language model - Dinosaurus land
Welcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special t... | Python Code:
import numpy as np
from utils import *
import random
from random import shuffle
Explanation: Character level language model - Dinosaurus land
Welcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special task. Leading biology res... |
7,232 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tensorflow Image Recognition Tutorial
This tutorial shows how we can use MLDB's TensorFlow integration to do image recognition. TensorFlow is Google's open source deep learning library.
We... | Python Code:
from pymldb import Connection
mldb = Connection()
Explanation: Tensorflow Image Recognition Tutorial
This tutorial shows how we can use MLDB's TensorFlow integration to do image recognition. TensorFlow is Google's open source deep learning library.
We will load the Inception-v3 model to generate descript... |
7,233 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2018 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: オブジェクト検出
<table class="tfo-notebook-buttons" align="left">
<td><a target=... | Python Code:
# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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 re... |
7,234 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deploying a Model and Predicting with Cloud Machine Learning Engine
This notebook is the final step in a series of notebooks for doing machine learning on cloud. The previous notebook, demon... | Python Code:
import google.datalab as datalab
import google.datalab.ml as ml
import mltoolbox.regression.dnn as regression
import os
import requests
import time
Explanation: Deploying a Model and Predicting with Cloud Machine Learning Engine
This notebook is the final step in a series of notebooks for doing machine lea... |
7,235 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plot bike-share data with Matplotlib
Step1: Question 1
Step2: Question 2
Step3: Question 3
Step4: Question 4 | Python Code:
from pandas import DataFrame, Series
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
weather = pd.read_table('daily_weather.tsv')
usage = pd.read_table('usage_2012.tsv')
stations = pd.read_table('stations.tsv')
newseasons = {'Summer': 'Spring', 'Spring': 'Winter', ... |
7,236 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MP2 theory for a closed-shell reference
In this notebook we will use wicked to generate equations for the MP2 method using an orbital-invariant formalism
Step1: Here we define the operator ... | Python Code:
import wicked as w
w.reset_space()
w.add_space("o", "fermion", "occupied", ["i", "j", "k", "l", "m", "n"])
w.add_space("v", "fermion", "unoccupied", ["a", "b", "c", "d", "e", "f"])
Explanation: MP2 theory for a closed-shell reference
In this notebook we will use wicked to generate equations for the MP2 met... |
7,237 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Feature
Step1: Config
Automatically discover the paths to various data folders and compose the project structure.
Step2: Identifier for storing these features on disk and referring to them... | Python Code:
from pygoose import *
import os
import warnings
import gensim
from fuzzywuzzy import fuzz
from nltk import word_tokenize
from nltk.corpus import stopwords
from scipy.stats import skew, kurtosis
from scipy.spatial.distance import cosine, cityblock, jaccard, canberra, euclidean, minkowski, braycurtis
Explana... |
7,238 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Build a histogram with percentages correct for each category
Step1: Stats of text length for correct and incorrect | Python Code:
df_test = df[(df["is_test"] == True)]
df_test["prediction"] = predictions
#print df_test.head()
# Compare the percent correct to the results from earlier to make sure things are lined up right
print "Calculated accuracy:", sum(df_test["label"] == df_test["prediction"]) / float(len(df_test))
print "Model ac... |
7,239 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
All About
Step1: Graphic Interpretation
The graphic above illustrates the pattern of follow-ups in the CMMI data set for each of the 1,640 unique patients. Using your cursor, you can hover ... | Python Code:
from IPython.core.display import display, HTML;from string import Template;
HTML('<script src="//d3js.org/d3.v3.min.js" charset="utf-8"></script>')
css_text2 = '''
#main { float: left; width: 750px;}#sidebar { float: right; width: 100px;}#sequence { width: 600px; height: 70px;}#legend { padding: 10p... |
7,240 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
```
Copyright 2020 The IREE Authors
Licensed under the Apache License v2.0 with LLVM Exceptions.
See https
Step1: 2. Import TensorFlow and Other Dependencies
Step2: 3. Load the MNIST Datas... | Python Code:
%%capture
!python -m pip install iree-compiler iree-runtime iree-tools-tf -f https://github.com/google/iree/releases
# Import IREE's TensorFlow Compiler and Runtime.
import iree.compiler.tf
import iree.runtime
Explanation: ```
Copyright 2020 The IREE Authors
Licensed under the Apache License v2.0 with LLVM... |
7,241 | Given the following text description, write Python code to implement the functionality described.
Description:
Find two non
Function to check if two non - intersecting subarrays with equal sum exists or not ; Sort the given array ; Traverse the array ; Check for duplicate elements ; If no duplicate element is present i... | Python Code:
def findSubarrays(arr , N ) :
arr . sort() ;
i = 0 ;
for i in range(N - 1 ) :
if(arr[i ] == arr[i + 1 ] ) :
print("YES ") ;
return ;
print("NO ") ;
if __name__== ' __main __' :
arr =[4 , 3 , 0 , 1 , 2 , 0 ] ;
N = len(arr ) ;
findSubarrays(arr , N ) ;
|
7,242 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Some remarks on sorting
You can either use the sorted() Python built-in function which will produce a sorted version of anything you can iterate over. Lists have a .sort() method which sorts... | Python Code:
l1=[3,1,4,6,7]
l2=sorted(l1)
print(l1,l2) #l2 is a different list from l1
l1.sort() #now l1 is sorted in-place
print(l1)
Explanation: Some remarks on sorting
You can either use the sorted() Python built-in function which will produce a sorted version of anything you can iterate over. Lists have a .sort() m... |
7,243 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Parallelize image filters with dask
This notebook will show how to parallize CPU-intensive workload using dask array. A simple uniform filter (equivalent to a mean filter) from scipy.ndimage... | Python Code:
%pylab inline
from scipy.ndimage import uniform_filter
import dask.array as da
def mean(img):
"ndimage.uniform_filter with `size=51`"
return uniform_filter(img, size=51)
Explanation: Parallelize image filters with dask
This notebook will show how to parallize CPU-intensive workload using dask array... |
7,244 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<center>
<h1> ILI285 - Computación Científica I / INF285 - Computación Científica </h1>
<h2> Interpolation
Step1: <div id='intro' />
Introduction
Previously in our jupyter notebook... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import scipy as sp
from scipy import interpolate
import ipywidgets as widgets
import matplotlib as mpl
mpl.rcParams['font.size'] = 14
mpl.rcParams['axes.labelsize'] = 20
mpl.rcParams['xtick.labelsize'] = 14
mpl.rcParams['ytick.labelsize'... |
7,245 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook uses the March Madness dataset provided by Kaggel.com. Pleas use kaggle.com to access that data.
I put the flat data into a SQLite database on my local, for the notebook expla... | Python Code:
# imports
import sqlite3 as sql
from sklearn import datasets
from sklearn import metrics
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: This notebook uses the March Madness dataset provided by Kaggel.com. Pleas use kaggle.com to... |
7,246 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial 11
Step1: 1. New parameters in additional_net_params
There are a few unique additions for the grid envs to additional_net_params to be aware of.
grid_array
grid_array passes infor... | Python Code:
from flow.core.params import NetParams
from flow.scenarios.grid import SimpleGridScenario
from flow.core.params import TrafficLightParams
from flow.core.params import SumoParams, EnvParams, InitialConfig, NetParams, \
InFlows, SumoCarFollowingParams
from flow.core.params import VehicleParams
import num... |
7,247 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plotting the full vector-valued MNE solution
The source space that is used for the inverse computation defines a set of
dipoles, distributed across the cortex. When visualizing a source esti... | Python Code:
# Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
#
# License: BSD-3-Clause
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path / 'subjects'
smoothing_steps =... |
7,248 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Simple CNN
We are going to define a simple Convolutional Network and we are going to train it from scrath on the dataset. The results of this model is going to be our benchmark
We are going ... | Python Code:
%autosave 0
IMAGE_SIZE = (360,404) # The dimensions to which all images found will be resized.
BATCH_SIZE = 32
NUMBER_EPOCHS = 8
TENSORBOARD_DIRECTORY = "../logs/simple_model/tensorboard"
TRAIN_DIRECTORY = "../data/train/"
VALID_DIRECTORY = "../data/valid/"
TEST_DIRECTORY = "../data/test/"
NUMBER_TRAIN_SAM... |
7,249 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep Learning
Assignment 5
The goal of this assignment is to train a Word2Vec skip-gram model over Text8 data.
Step2: Download the data from the source website if necessary.
Step4: Read th... | Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
%matplotlib inline
from __future__ import print_function
import collections
import math
import numpy as np
import os
import random
import tensorflow as tf
import zipfile
from matplotlib import pylab... |
7,250 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
While nan == nan is always False, in many cases people want to treat them as equal, and this is enshrined in pandas.DataFrame.equals: | Problem:
import pandas as pd
import numpy as np
np.random.seed(10)
df = pd.DataFrame(np.random.randint(0, 20, (10, 10)).astype(float), columns=["c%d"%d for d in range(10)])
df.where(np.random.randint(0,2, df.shape).astype(bool), np.nan, inplace=True)
def g(df):
cols = (df.columns[df.iloc[0,:].fillna('Nan') != df.il... |
7,251 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Algorithms Exercise 2
Imports
Step2: Peak finding
Write a function find_peaks that finds and returns the indices of the local maxima in a sequence. Your function should
Step3: Here is a st... | Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
Explanation: Algorithms Exercise 2
Imports
End of explanation
def find_peaks(a):
Find the indices of the local maxima in a sequence.
peaks = []
for i in range(len(a)):
if i==0:
i... |
7,252 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<center>
<h1> ILI286 - Computación Científica II </h1>
<h2> Valores y Vectores Propios </h2>
<h2> <a href="#acknowledgements"> [S]cientific [C]omputing [T]eam </a> </h2>
<h2... | Python Code:
import numpy as np
from scipy import linalg
from matplotlib import pyplot as plt
%matplotlib inline
Explanation: <center>
<h1> ILI286 - Computación Científica II </h1>
<h2> Valores y Vectores Propios </h2>
<h2> <a href="#acknowledgements"> [S]cientific [C]omputing [T]eam </a> </h2>
<h2> Ve... |
7,253 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h3>Current School Panda</h3>
Working with directory school data
Creative Commons in all schools
This script uses a csv file from Creative Commons New Zealand and csv file from Ministry of E... | Python Code:
crcom = pd.read_csv('/home/wcmckee/Downloads/List of CC schools - Sheet1.csv', skiprows=5, index_col=0, usecols=[0,1,2])
Explanation: <h3>Current School Panda</h3>
Working with directory school data
Creative Commons in all schools
This script uses a csv file from Creative Commons New Zealand and csv file f... |
7,254 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Toplevel
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specif... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'pcmdi', 'sandbox-1', 'toplevel')
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: PCMDI
Source ID: SANDBOX-1
Sub-Topics: Radiative Forcings.
Properties... |
7,255 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a id='beginning'></a> <!--\label{beginning}-->
* Outline
* Glossary
* 4. The Visibility space
* Previous
Step1: Import section specific modules
Step2: 4.5.2 $uv$ coverage
Step3: Fro... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
Explanation: <a id='beginning'></a> <!--\label{beginning}-->
* Outline
* Glossary
* 4. The Visibility space
* Previous: 4.5.1 UV Coverage: UV tracks
... |
7,256 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Evaluating an Exam Using Ply
This notebook shows how we can use the package ply
to implement a scanner. Our goal is to implement a program that can be used to evaluate the results of an exa... | Python Code:
data = '''Class: Algorithms and Complexity
Group: TIT09AID
MaxPoints = 60
Exercise: 1. 2. 3. 4. 5. 6.
Jim Smith: 9 12 10 6 6 0
John Slow: 4 4 2 0 - -
Susi Sorglos: 9 12 12 9 9 6
'''
Explanation: Evaluating an Ex... |
7,257 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Препроцессинг фич
Step1: Обучение моделей
Step2: XGBoost
Step3: LightGBM
Step4: Vowpal Wabbit
Step5: Lasso
Step6: Submission
Step7: XGBoost
Step8: LightGBM
Step9: Lasso
Step10: Ens... | Python Code:
def align_to_lb_score(df):
# https://www.kaggle.com/c/sberbank-russian-housing-market/discussion/32717
df = df.copy()
trainsub = df[df.timestamp < '2015-01-01']
trainsub = trainsub[trainsub.product_type=="Investment"]
ind_1m = trainsub[trainsub.price_doc <= 1000000].index
ind_2m = t... |
7,258 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Now create the DrawControl and add it to the Map using add_control. We also register a handler for draw events. This will fire when a drawn path is created, edited or deleted (there are the ... | Python Code:
dc = DrawControl(marker={'shapeOptions': {'color': '#0000FF'}},
rectangle={'shapeOptions': {'color': '#0000FF'}},
circle={'shapeOptions': {'color': '#0000FF'}},
circlemarker={},
)
def handle_draw(target, action, geo_json):
print(action... |
7,259 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ACM Digital Library bibliometric analysis of legacy software
An incomplete bibliographical inquiry into what the ACM Digital Library has to say about legacy.
Basically two research questions... | Python Code:
import pandas as pd
import networkx as nx
import community
import itertools
import matplotlib.pyplot as plt
import numpy as np
import re
%matplotlib inline
Explanation: ACM Digital Library bibliometric analysis of legacy software
An incomplete bibliographical inquiry into what the ACM Digital Library has t... |
7,260 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Integration Exercise 2
Imports
Step1: Indefinite integrals
Here is a table of definite integrals. Many of these integrals has a number of parameters $a$, $b$, etc.
Find five of these integr... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy import integrate
Explanation: Integration Exercise 2
Imports
End of explanation
def integrand(x, a):
return 1.0/(x**2 + a**2)
def integral_approx(a):
# Use the args keyword argument to feed extra ... |
7,261 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Explicit 1D Benchmarks
This file demonstrates how to generate, plot, and output data for 1d benchmarks
Choose from
Step1: Generate the data with noise
Step2: Plot inline and save image
Ste... | Python Code:
from pypge.benchmarks import explicit
import numpy as np
# visualization libraries
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# plot the visuals in ipython
%matplotlib inline
Explanation: Explicit 1D Benchmarks
This file demonstrates how to generate, plot, and output data for 1... |
7,262 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Find the best $\alpha$ for $p = 7, N_y = 256, N_z = 2048$
Step1: Find the best $\alpha$ for $p = 15, N_y = 128, N_z = 1024$
Step2: Find the best $\alpha$ for $p = 31, N_y = 64, N_z = 512$ | Python Code:
alphs = list(np.linspace(0,pi/2, 16, endpoint=False))
Re=2000;
N = 7
Nl = 257
Nz = 2049
yms = []; y1s = []; y10s = []; zms = []
for alph in alphs:
yl=mesh(alph, Nl)
ym, y1, y10, zm, cm = wall_units(yl,Nz, N,Re)
yms.append(ym)
y1s.append(y1)
y10s.append(y10)
zms.append(zm)
alpha = 0.... |
7,263 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Machine Learning Engineer Nanodegree
Introduction and Foundations
Project 0
Step1: From a sample of the RMS Titanic data, we can see the various features present for each passenger on the s... | Python Code:
import numpy as np
import pandas as pd
# RMS Titanic data visualization code
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# Load the dataset
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)
# Print the first few entries of the RMS Ti... |
7,264 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>lcacoffee</h1>
script that displays coffees sold by hour at lca2015.
Currently it opens a .json file and converts it into a python dict.
It's missing monday data.
sale by hour is
Step1:... | Python Code:
import json
import os
import pandas
import getpass
theuser = getpass.getuser()
Explanation: <h1>lcacoffee</h1>
script that displays coffees sold by hour at lca2015.
Currently it opens a .json file and converts it into a python dict.
It's missing monday data.
sale by hour is: key - the hour (24hr). Value i... |
7,265 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Prerequisites
This notebook contains examples which are expected to be run with exactly 4 MPI processes; not because they wouldn't work otherwise, but simply because it's what their descript... | Python Code:
import ipyparallel as ipp
c = ipp.Client(profile='mpi')
Explanation: Prerequisites
This notebook contains examples which are expected to be run with exactly 4 MPI processes; not because they wouldn't work otherwise, but simply because it's what their description assumes. For this, you need to:
Install an M... |
7,266 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a id="top"></a>
Cloud Statistics
<hr>
Notebook Summary
This notebook explores Landsat 7 and Landsat 8 Data Cubes and reports cloud statistics
for selected regions within a cube. This is va... | Python Code:
# Enable importing of utilities.
import sys
import os
sys.path.append(os.environ.get('NOTEBOOK_ROOT'))
import numpy as np
import xarray as xr
import pandas as pd
import matplotlib.pyplot as plt
# Load Data Cube Configuration
import datacube
import utils.data_cube_utilities.data_access_api as dc_api
api =... |
7,267 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1st Order ODE
Let's solve
Step1: Try smaller timestep
Step2: Our numeric result is more accurate when we use a smaller timestep, but it's still not perfect
2nd Order ODE
Let's solve
Step3:... | Python Code:
y_0 = 1
t_0 = 0
t_f = 10
def dy_dt(y):
return .5*y
def analytic_solution_1st_order(t):
return np.exp(.5*t)
dt = .5
t_array = np.arange(t_0, t_f, dt)
y_array = np.empty_like(t_array)
y_array[0] = y_0
for i in range(len(y_array)-1):
y_array[i+1] = y_array[i] + (dt * dy_dt(y_array[i]))
plt.pl... |
7,268 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pattern Mining - Association Rule Mining
A frequent pattern is a substructure that appears frequently in a dataset. Finding the frequent patterns of a dataset is a essential step in data min... | Python Code:
import graphlab as gl
from graphlab import aggregate as agg
from visualization_helper_functions import *
Explanation: Pattern Mining - Association Rule Mining
A frequent pattern is a substructure that appears frequently in a dataset. Finding the frequent patterns of a dataset is a essential step in data mi... |
7,269 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Convolutional Neural Networks with 17flowers
A simple deep learning example on how to start classifying images with your own data.
This notebook is expected to be executed after 17flowers_da... | Python Code:
from __future__ import division, print_function
%matplotlib inline
path = "data/17flowers/"
import os, json
from glob import glob
import numpy as np
np.set_printoptions(precision=4, linewidth=100)
from matplotlib import pyplot as plt
# check that ~/.keras/keras.json is set for Theano and includes "image_da... |
7,270 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tarea 2
Step1: Realizar y verificar la descomposición SVD
Step2: Para alguna imagen de su elección, elegir distintos valores de aproximación a la imagen original
Step3: Ejercicio 2
Step4:... | Python Code:
from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
#url = sys.argv[1]
url = 'pikachu.png'
img = Image.open(url)
imggray = img.convert('LA')
Explanation: Tarea 2: Álgebra Lineal y Descomposición SVD
Teoría de Algebra Lineal y Optimización
1. ¿Por qué una matriz equivale a una transform... |
7,271 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Get the text we want to process
Step1: Let's take a smaller chunk from the text
Step2: Tokenizing text
Tokens are meaningful chunks of text
Step3: Stop words
Stop words are words that you... | Python Code:
with open('book.txt', 'r') as file:
text = file.readlines()
Explanation: Get the text we want to process
End of explanation
# using a list comprehension to simplify iterating over the the text structure
snippet = " ".join(block.strip() for block in text[175:200])
snippet
# alternative with for-loop
oth... |
7,272 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Finite Time of Integration (fti)
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your installation or ... | Python Code:
!pip install -I "phoebe>=2.0,<2.1"
Explanation: Finite Time of Integration (fti)
Setup
Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
%matp... |
7,273 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Electron Plasma Waves
Created by Rui Calado and Jorge Vieira, 2018
In this notebook, we are going to study the dispersion relation for electron plasma waves.
Theory
Electron plasma waves are... | Python Code:
import em1ds as zpic
#v_the = 0.001
v_the = 0.02
#v_the = 0.20
electrons = zpic.Species( "electrons", -1.0, ppc = 64, uth=[v_the,v_the,v_the])
sim = zpic.Simulation( nx = 500, box = 50.0, dt = 0.0999/2, species = electrons )
sim.filter_set("sharp", ck = 0.99)
#sim.filter_set("gaussian", ck = 50.0)
Explanat... |
7,274 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Ch 2. 자료의 정리
변수와 자료
도수분포표
1. 변수 ( variable, feature )
양적변수 ( quantitative variable, real value )
수치로 나타낼 수 있는 변수
- 이산변수 ( discrete )
- 정숫값을 취한 수 있는 변수
- ex. 자녀수, 자동차판매대수 등
- 연속변수 ( c... | Python Code:
import pandas as pd
import numpy as np
np.random.seed(0)
data = np.random.randint(50, 100, size=(8, 5))
data[0][0] = 12
data
np.sort(data.flatten())
Explanation: Ch 2. 자료의 정리
변수와 자료
도수분포표
1. 변수 ( variable, feature )
양적변수 ( quantitative variable, real value )
수치로 나타낼 수 있는 변수
- 이산변수 ( discrete )
- 정숫값을 취... |
7,275 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pre-processing and training LDA
The purpose of this tutorial is to show you how to pre-process text data, and how to train the LDA model on that data. This tutorial will not explain you the ... | Python Code:
# Read data.
import os
from smart_open import smart_open
# Folder containing all NIPS papers.
data_dir = 'nipstxt/'
# Folders containin individual NIPS papers.
yrs = ['00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']
dirs = ['nips' + yr for yr in yrs]
# Read all texts into a lis... |
7,276 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<img src="../Pierian-Data-Logo.PNG">
<br>
<strong><center>Copyright 2019. Created by Jose Marcial Portilla.</center></strong>
CIFAR Code Along with CNN
The <a href='https
Step1: Load the CI... | Python Code:
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from torchvision.utils import make_grid
import numpy as np
import pandas as pd
import seaborn as sn # for heatmaps
from sklearn.metrics import confusion_m... |
7,277 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<center><h1>CNN 多通道情感分析</h1></center>
一个有三个通道，分别是word embedding，POS 标签 embedding, 词的情感极性强度embedding
Step1: POS当作一个通道。
Tag word 的方法： http
Step2: 情感极性当作一个通道。
读取情感强度文件，构建字典
Step3: 构建情感极性强度通道... | Python Code:
import keras
from os.path import join
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout,Activation, Lambda,Input
from keras.layers import Embedding
from keras.layers import Convolution1D
from keras.datasets import imdb
from keras import b... |
7,278 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
GPS tracks
http
Step1: TODO
Step2: Convert the data to Web Mercator
Step3: Contextily helper function
Step4: Add background tiles to plot
Step5: Save selected departments into a GeoJSON... | Python Code:
import pandas as pd
import geopandas as gpd
Explanation: GPS tracks
http://geopandas.org/gallery/plotting_basemap_background.html#adding-a-background-map-to-plots
https://ocefpaf.github.io/python4oceanographers/blog/2015/08/03/fiona_gpx/
End of explanation
df = gpd.read_file("communes-20181110.shp")
!head ... |
7,279 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Visualization 1
Step1: Scatter plots
Learn how to use Matplotlib's plt.scatter function to make a 2d scatter plot.
Generate random data using np.random.randn.
Style the markers (color, size... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
Explanation: Visualization 1: Matplotlib Basics Exercises
End of explanation
?plt.scatter()
from matplotlib import markers
markers.MarkerStyle.markers.keys()
x = np.random.rand(100)
y = np.random.rand(100)
plt.scatter(x, y, label = 'The ... |
7,280 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<!--BOOK_INFORMATION-->
<a href="https
Step1: Generating the training data
The first step is to generate some training data. For this, we will use NumPy's random
number generator. As discus... | Python Code:
import numpy as np
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
Explanation: <!--BOOK_INFORMATION-->
<a href="https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv" target="_blank"><img align="left" src="data/cover.jpg" style="width: 76... |
7,281 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Gravitational Redshift (rv_grav)
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or ... | Python Code:
!pip install -I "phoebe>=2.1,<2.2"
Explanation: Gravitational Redshift (rv_grav)
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
%matp... |
7,282 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Numpy Exercise 4
Imports
Step1: Complete graph Laplacian
In discrete mathematics a Graph is a set of vertices or nodes that are connected to each other by edges or lines. If those edges don... | Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
Explanation: Numpy Exercise 4
Imports
End of explanation
import networkx as nx
K_5=nx.complete_graph(5)
nx.draw(K_5)
Explanation: Complete graph Laplacian
In discrete mathematics a Graph is a set of vertices or node... |
7,283 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example 5.4
Step1: The Data
We'll start off by exploring our dataset to see what attributes we have and how the class of the tumor is represented
Before we proceed, ensure to include header... | Python Code:
import pandas as pd # we use this library to import a CSV of cancer tumor data
import numpy as np # we use this library to help us represent traditional Python arrays/lists as matrices/tensors with linear algebra operations
from sknn.mlp import Classifier, Layer # we use this library for the actual neural ... |
7,284 | 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 ... |
7,285 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href='http
Step1: Files
2. Create a file in the current working directory called contacts.txt by running the cell below
Step2: 3. Open the file and use .read() to save the contents of t... | Python Code:
abbr = 'NLP'
full_text = 'Natural Language Processing'
# Enter your code here:
print(f'{abbr} stands for {full_text}')
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Python Text Basics Assessment - Solutions
Welcome to your assessment! Complete the tasks descr... |
7,286 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Genetic Algorithm Workshop
In this workshop we will code up a genetic algorithm for a simple mathematical optimization problem.
Genetic Algorithm is a
* Meta-heuristic
* Inspired by N... | Python Code:
%matplotlib inline
# All the imports
from __future__ import print_function, division
from math import *
import random
import sys
import matplotlib.pyplot as plt
# TODO 1: Enter your unity ID here
__author__ = "<unity-id>"
class O:
Basic Class which
- Helps dynamic updates
- Pretty... |
7,287 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
NumPy를 활용한 선형대수 입문
선형대수(linear algebra)는 데이터 분석에 필요한 각종 계산을 위한 기본적인 학문이다.
데이터 분석을 하기 위해서는 실제로 수많은 숫자의 계산이 필요하다. 하나의 데이터 레코드(record)가 수십개에서 수천개의 숫자로 이루어져 있을 수도 있고 수십개에서 수백만개의 이러한 데이터 레코드를 조합... | Python Code:
x = np.array([1, 2, 3, 4])
x
x = np.array([[1], [2], [3], [4]])
x
Explanation: NumPy를 활용한 선형대수 입문
선형대수(linear algebra)는 데이터 분석에 필요한 각종 계산을 위한 기본적인 학문이다.
데이터 분석을 하기 위해서는 실제로 수많은 숫자의 계산이 필요하다. 하나의 데이터 레코드(record)가 수십개에서 수천개의 숫자로 이루어져 있을 수도 있고 수십개에서 수백만개의 이러한 데이터 레코드를 조합하여 계산하는 과정이 필요할 수 있다.
선형대수를 사용하는 첫번째 ... |
7,288 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This notebook only contains executable code cells for the examples mentioned in
https
Step1: The problem
Step2: The solution
Step3: Using pad_shard_unpad()
Step4: Computing metrics in ev... | Python Code:
!pip install -q chex einops
# tfds.split_for_jax_process() was added in 4.5.1
!pip install -q tensorflow_datasets -U
# flax.jax_utils.pad_shard_unpad() is only available at HEAD
!pip install -q git+https://github.com/google/flax
import collections
import chex
import einops
import jax
import jax.numpy as jn... |
7,289 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Analysis of classification results
Objective
Step1: Load original model
Step2: Load sample classification results
The implemented classification method does not return a single best-fit mo... | Python Code:
from IPython.core.display import HTML
css_file = 'pynoddy.css'
HTML(open(css_file, "r").read())
import sys, os
import matplotlib.pyplot as plt
# adjust some settings for matplotlib
from matplotlib import rcParams
# print rcParams
rcParams['font.size'] = 15
# determine path of repository to set paths corret... |
7,290 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Computing the top hashtags (JSON)
So you have tweets in a JSON file, and you'd like to get a list of the hashtags, from the most frequently occurring hashtags on down.
There are many, many d... | Python Code:
!cat 50tweets.json | jq -cr '[.entities.hashtags][0][].text'
!cat tweets4hashtags.json | jq -cr '[.entities.hashtags][0][].text' > allhashtags.txt
Explanation: Computing the top hashtags (JSON)
So you have tweets in a JSON file, and you'd like to get a list of the hashtags, from the most frequently occurri... |
7,291 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Stability map with MEGNO and WHFast
In this tutorial, we'll create a stability map of a two planet system using the chaos indicator MEGNO (Mean Exponential Growth of Nearby Orbits) and the s... | Python Code:
def simulation(par):
a, e = par # unpack parameters
rebound.reset()
rebound.integrator = "whfast-nocor"
rebound.dt = 5.
rebound.add(m=1.) # Star
rebound.add(m=0.000954, a=5.204, anom=0.600, omega=0.257, e=0.048)
rebound.add(m=0.000285, a=a, anom=0.871, omega=1.616, e=e)
reb... |
7,292 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
First off, I'm no mathmatician. I admit that. Yet I still need to understand how ScyPy's sparse matrices work arithmetically in order to switch from a dense NumPy matrix to a SciPy ... | Problem:
import numpy as np
from scipy import sparse
V = sparse.random(10, 10, density = 0.05, format = 'dok', random_state = 42)
x = 99
V._update(zip(V.keys(), np.array(list(V.values())) + x)) |
7,293 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
I was inspired by @twiecki and his great post about Bayesian neural networks. But I thought that that way of creating BNNs is not obvious and easy for people. That's why I decided to make Ge... | Python Code:
%env THEANO_FLAGS=device=cuda0
import matplotlib.pyplot as plt
%matplotlib inline
import gelato
import theano
import theano.tensor as tt
theano.config.warn_float64 = 'warn'
import numpy as np
import lasagne
import pymc3 as pm
Explanation: I was inspired by @twiecki and his great post about Bayesian neural ... |
7,294 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Title
Step1: Create Data
Step2: Select Based On The Result Of A Select | Python Code:
# Ignore
%load_ext sql
%sql sqlite://
%config SqlMagic.feedback = False
Explanation: Title: Nested Select
Slug: nested_select
Summary: Nested Select Based On Conditions in SQL.
Date: 2017-01-16 12:00
Category: SQL
Tags: Basics
Authors: Chris Albon
Note: This tutorial was written using Catherine Devlin... |
7,295 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
Was trying to generate a pivot table with multiple "values" columns. I know I can use aggfunc to aggregate values the way I want to, but what if I don't want to max or min both colu... | Problem:
import pandas as pd
import numpy as np
np.random.seed(1)
df = pd.DataFrame({
'A' : ['one', 'one', 'two', 'three'] * 6,
'B' : ['A', 'B', 'C'] * 8,
'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 4,
'D' : np.random.randn(24),
'E' : np.random.randn(24)
})
def g... |
7,296 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
1.1 Getting started
Prerequisites
Installation
This tutorial requires signac, so make sure to install the package before starting.
The easiest way to do so is using conda
Step1: We start by... | Python Code:
import signac
assert signac.__version__ >= '0.8.0'
Explanation: 1.1 Getting started
Prerequisites
Installation
This tutorial requires signac, so make sure to install the package before starting.
The easiest way to do so is using conda:
$ conda config --add channels conda-forge
$ conda install signac
or pip... |
7,297 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Model Tuning
Step1: basic Usage
A couple things are needed by the tuner
Step2: prepare the model
Step3: start tuning
Step4: view the best hyper-parameter set
Step5: understading hyper-s... | Python Code:
import matchzoo as mz
train_raw = mz.datasets.toy.load_data('train')
dev_raw = mz.datasets.toy.load_data('dev')
test_raw = mz.datasets.toy.load_data('test')
Explanation: Model Tuning
End of explanation
preprocessor = mz.models.DenseBaseline.get_default_preprocessor()
train = preprocessor.fit_transform(trai... |
7,298 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 2</font>
Download
Step1: Dicionários
Step2: Criando dicionários aninhados | Python Code:
# Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
Explanation: <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 2</font>
Download: http://github.com/dsacademybr
End of explanation
# Iss... |
7,299 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Example 3
Step1: Visualize initial state
Step2: Run simulation and visualize new state
Step3: Queries about LAMMPS simulation
Step4: Working with LAMMPS Variables
Step5: Accessing Atom ... | Python Code:
from lammps import IPyLammps
L = IPyLammps()
# 2d circle of particles inside a box with LJ walls
import math
b = 0
x = 50
y = 20
d = 20
# careful not to slam into wall too hard
v = 0.3
w = 0.08
L.units("lj")
L.dimension(2)
L.atom_style("bond")
L.boundary("f f p")
L.lattice("hex", 0.85)
L.r... |
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