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14,700 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Loading data
Step1: Add counts for each type into buildings
Step2: Normalize longitude and latitude
Step3: Analysis features
Step4: Cross Validation
Step5: According to this simplified ... | Python Code:
buildings = pd.read_csv("../data/buildings.csv")
events = pd.read_csv("../data/events.csv")
buildings.head(2)
events.head(2)
events['type'].value_counts() # types: 1: 311-calls, 2: crimes, 3: blight violations
Explanation: Loading data
End of explanation
def str_to_list(events_str):
events_list = eve... |
14,701 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python II Hausaufgaben
1.Baue eine Funktion mit dem Namen 'double', der die Zahl 5 vedoppelt
Step1: 2.Baue einen for-loop, der durch vordefinierte Zahlen-list geht, und mithilfe der eben kr... | Python Code:
def double(number):
result = number*2
return result
double(5)
Explanation: Python II Hausaufgaben
1.Baue eine Funktion mit dem Namen 'double', der die Zahl 5 vedoppelt:
End of explanation
lst = list(range(1,5))
for elem in lst:
print(double(elem))
Explanation: 2.Baue einen for-loop, der durch v... |
14,702 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Reading and manipulating datasets with Pandas
This notebook shows how to create Series and Dataframes with Pandas. Also, how to read CSV files and creaate pivot tables. The first part is bas... | Python Code:
import numpy as np
from __future__ import print_function
import pandas as pd
pd.__version__
Explanation: Reading and manipulating datasets with Pandas
This notebook shows how to create Series and Dataframes with Pandas. Also, how to read CSV files and creaate pivot tables. The first part is based on the c... |
14,703 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Readable Syntax - Quicksort in Python
Quicksort Pseudocode from Wikipedia
Here the Python implementation
Step1: Interactive and Batch possibilities - Munich temperatures
Step2: Python is n... | Python Code:
import random
# A python implementation of the Wikipedia quicksort algorithm
def my_quicksort(array):
if len(array) < 1:
return array
pivot = array[0] # select a pivot (first element of list)
rest = array[1:] # the array with the pivot
# removed
less = [x f... |
14,704 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Getting started with the computational analysis of games
Step1: Gambit version 16.0.0 is the current development version. You can get it from http
Step2: Inspecting a game
The game th... | Python Code:
import gambit
Explanation: <h1>Getting started with the computational analysis of games:</h1>
<h2>Playing "stripped down" poker</h2>
<i>Theodore L. Turocy</i><br/>
<i>University of East Anglia</i>
<br/><br/>
<h3>EC'16 Workshop
24 July 2016</h3>
End of explanation
gambit.__version__
Explanation: Gambit vers... |
14,705 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pynetics QuickStart
In this example we are going to build a very simple and useless algorithm to explore the possibilities of the pynetics library.
Our problem will be as follows. We'll goin... | Python Code:
from pynetics.ga_bin import BinaryIndividualSpawningPool
# Let's define the size of our individuals (the numer of 1's and 0's)
individual_size = 25
binary_individual_spawning_pool=BinaryIndividualSpawningPool(size=individual_size)
Explanation: Pynetics QuickStart
In this example we are going to build a ver... |
14,706 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
20 NEWS GROUPS
Antes de nada, hay que importar los paquetes necesarios.
Step1: Lectura de los datos
A continuación, se define una función para cargar los datos que se encuentran en las carp... | Python Code:
%pylab inline
from sklearn import datasets
Explanation: 20 NEWS GROUPS
Antes de nada, hay que importar los paquetes necesarios.
End of explanation
def loadDataset(directory):
dataset = datasets.load_files(directory)
print "Loaded %d documents" % len(dataset.data)
print "Loaded %d categorie... |
14,707 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
5 minutes to creating your first Machine Learning model
There's a number of services out there that make Machine Learning accessible to the masses by abstracting away the complexities of cre... | Python Code:
BIGML_USERNAME = '' # fill in your username between the quotes
BIGML_API_KEY = '' # fill in your API key
BIGML_AUTH = 'username=' + BIGML_USERNAME + ';api_key=' + BIGML_API_KEY # leave as it is
print "Authentication variables set!"
Explanation: 5 minutes to creating your first Machine Learning model
There'... |
14,708 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step2: Más Álgebra lineal con Python
Esta notebook fue creada originalmente como un blog post por Raúl E. López Briega en Matemáticas, análisis de datos y python. El contenido esta bajo la l... | Python Code:
# <!-- collapse=True -->
# importando modulos necesarios
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import scipy.sparse as sp
import scipy.sparse.linalg
import scipy.linalg as la
import sympy
# imprimir con notación matemática.
sympy.init_printing(use_latex='mathjax')
# <!-- coll... |
14,709 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Contest entry by Wouter Kimman
Strategy
Step1: First steps, reading in and exploring the data are the same as Brendon's steps
Step2: 1) Prediction from training set using all wells
Let's d... | Python Code:
from numpy.fft import rfft
from scipy import signal
import numpy as np
import matplotlib.pyplot as plt
import plotly.plotly as py
import pandas as pd
import timeit
from sqlalchemy.sql import text
from sklearn import tree
from sklearn import cross_validation
from sklearn.cross_validation import train_test_s... |
14,710 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Dual CRISPR Screen Analysis
Count Plots
Amanda Birmingham, CCBB, UCSD (abirmingham@ucsd.edu)
Instructions
To run this notebook reproducibly, follow these steps
Step1: Matplotlib Display
Ste... | Python Code:
g_timestamp = ""
g_dataset_name = "20160510_A549"
g_count_alg_name = "19mer_1mm_py"
g_fastq_counts_dir = '/Users/Birmingham/Repositories/ccbb_tickets/20160210_mali_crispr/data/interim/20160510_D00611_0278_BHK55CBCXX_A549'
g_fastq_counts_run_prefix = "19mer_1mm_py_20160615223822"
g_collapsed_counts_dir = "/... |
14,711 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Income dataset
https
Step1: Merged income, zipcode, and station id for final dataframe | Python Code:
income = pd.read_excel("../data/unique/ACS_14_5YR_B19013.xls")
income = income.loc[8:]
income.head()
income = income.drop(['Unnamed: 1', 'Unnamed: 2', 'Unnamed: 3'], axis=1)
income = income.rename(columns={'B19013: MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2014 INFLATION-ADJUSTED DOLLARS) - Univers... |
14,712 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Requirement
Step1: Sending a mail is, with the proper library, a piece of cake...
Step2: ... but if we take it a little further, we can connect our doorbell project to the sending of mail!... | Python Code:
MAIL_SERVER = "mail.****.com"
FROM_ADDRESS = "noreply@****.com"
TO_ADDRESS = "my_friend@****.com"
Explanation: Requirement:
For sending mail you need an outgoing mail server (that, in the case of this script, also needs to allow unauthenticated outgoing communication). Fill out the required credentials in ... |
14,713 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tuning BM25 parameters
We tune BM25 parameters on a per-field basis including doc2query expansions and bigrammed text fields. These values are used later when optimizing more complex queries... | Python Code:
%load_ext autoreload
%autoreload 2
import importlib
import os
import sys
from copy import deepcopy
from elasticsearch import Elasticsearch
from skopt.plots import plot_objective
# project library
sys.path.insert(0, os.path.abspath('..'))
import qopt
importlib.reload(qopt)
from qopt.notebooks import evaluat... |
14,714 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
!!! D . R . A . F . T !!!
Luminance
The Luminance $L_v$ is the quantity defined by the formula
Step1: Note
Step2: Note
Step3: ASTM D1535-08$^{\epsilon 1}$ (2008) Method
Since 1943, the re... | Python Code:
import colour
colour.utilities.filter_warnings(True, False)
sorted(colour.LUMINANCE_METHODS.keys())
Explanation: !!! D . R . A . F . T !!!
Luminance
The Luminance $L_v$ is the quantity defined by the formula: <a name="back_reference_1"></a><a href="#reference_1">[1]</a>
$$
\begin{equation}
L_v=\cfrac{d\Phi... |
14,715 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<center><img src="src/ipyleaflet.svg" width="50%"></center>
Repository
Step1: Layers
Marker
Step2: Heatmap layer
Step3: Velocity
Step4: Controls
Step5: Clean | Python Code:
from ipyleaflet import Map, basemaps, basemap_to_tiles
center = (52.204793, 360.121558)
m = Map(
layers=(basemap_to_tiles(basemaps.NASAGIBS.ModisTerraTrueColorCR, "2018-11-12"), ),
center=center,
zoom=4
)
m
Explanation: <center><img src="src/ipyleaflet.svg" width="50%"></center>
Repository: htt... |
14,716 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
FMI Hirlam, MET Norway HARMONIE and NCEP GFS comparison demo
In this demo notebook we provide short comparison of using three different weather forecast models
Step1: Import datahub parsing... | Python Code:
%matplotlib notebook
import numpy as np
print ('numpy version is ', np.__version__)
import matplotlib.pyplot as plt
import mpl_toolkits.basemap
print ('mpl_toolkits.basemap version is ', mpl_toolkits.basemap.__version__)
from mpl_toolkits.basemap import Basemap
import warnings
import datetime
import dateut... |
14,717 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Features for Trajectory Recommendation
Features
Load Data
Compute POI Info
Construct Travelling Sequences
Compute Some Sequence Statistics
Compute Transition Probabilities
Basic Definitions
... | Python Code:
%matplotlib inline
import os
import re
import math
import random
import pickle
import pandas as pd
import numpy as np
import scipy.stats
#from numba import jit
from datetime import datetime
from joblib import Parallel, delayed
import matplotlib.pyplot as plt
nfeatures = 8 # number of features
EPS = 1e-12 #... |
14,718 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The use of watermark (above) is optional, and we use it to keep track of the changes while developing the tutorial material. (You can install this IPython extension via "pip install watermar... | Python Code:
import numpy as np
# Setting a random seed for reproducibility
rnd = np.random.RandomState(seed=123)
# Generating a random array
X = rnd.uniform(low=0.0, high=1.0, size=(3, 5)) # a 3 x 5 array
print(X)
Explanation: The use of watermark (above) is optional, and we use it to keep track of the changes while ... |
14,719 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
内容索引
相关性分析 --- cov函数、diagonal函数、trace函数、corrcoef函数
多项式拟合 --- polyfit函数、polyval函数、roots函数、polyder函数
计算净额成交量 --- sign函数、piecewise函数
模拟交易过程 --- vectorize函数、round函数
数据平滑 --- hanning函数
Step1: 1.... | Python Code:
%matplotlib inline
import numpy as np
from matplotlib.pyplot import plot
from matplotlib.pyplot import show
Explanation: 内容索引
相关性分析 --- cov函数、diagonal函数、trace函数、corrcoef函数
多项式拟合 --- polyfit函数、polyval函数、roots函数、polyder函数
计算净额成交量 --- sign函数、piecewise函数
模拟交易过程 --- vectorize函数、round函数
数据平滑 --- hanning函数
End of... |
14,720 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
An RNN model to generate sequences
RNN models can generate long sequences based on past data. This can be used to predict stock markets, temperatures, traffic or sales data based on past pat... | Python Code:
import math
import numpy as np
from matplotlib import pyplot as plt
import utils_prettystyle
import utils_batching
import utils_display
import tensorflow as tf
print("Tensorflow version: " + tf.__version__)
Explanation: An RNN model to generate sequences
RNN models can generate long sequences based on past... |
14,721 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lesson 1 & 2
Step1: Set up Structure for VGG 16
Step2: Remove Last Dense Layer (1000 ImageNet Classes) and add a Dense Layer for 2 Classes
Step3: Train Cats vs. Dogs model on dataset in b... | Python Code:
import tensorflow as tf
#path = 'data/dogscats/sample'
path = 'data/dogscats/'
import os
import json
from glob import glob
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import image as mpimg
%matplotlib inline
from tensorflow.contrib.keras.python.keras.models import Model, Sequent... |
14,722 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
San Francisco Crime Classification
Predict the category of crimes that occurred in the city by the bay
From 1934 to 1963, San Francisco was infamous for housing some of the world's most noto... | Python Code:
# Step 1 - importing classes we plan to use
import csv as csv
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
import seaborn as sns
# show plots inline
%matplotlib inline
#
# Preparing the data
#
data = pd.read_csv('../in... |
14,723 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compiled Sequential Importance Sampling
Compiled sequential importance sampling [1], or inference compilation, is a technique to amortize the computational cost of inference by learning a pr... | Python Code:
import torch
import torch.nn as nn
import torch.functional as F
import pyro
import pyro.distributions as dist
import pyro.infer
import pyro.optim
import os
smoke_test = ('CI' in os.environ)
n_steps = 2 if smoke_test else 2000
Explanation: Compiled Sequential Importance Sampling
Compiled sequential importan... |
14,724 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python + Astronomy
This course will be an introduction to Astropy, a maturing library for astronomy routines and tools in Python.
Astropy started as a combination of various common Python li... | Python Code:
# First, make sure this works:
import astropy
# If this doesn't work, raise your hand!
Explanation: Python + Astronomy
This course will be an introduction to Astropy, a maturing library for astronomy routines and tools in Python.
Astropy started as a combination of various common Python libraries (Pyfits, ... |
14,725 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Encoder-Decoder Analysis
Model Architecture
Step1: Perplexity on Each Dataset
Step2: Loss vs. Epoch
Step3: Perplexity vs. Epoch
Step4: Generations
Step5: BLEU Analysis
Step6: N-pairs B... | Python Code:
report_file = '/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing23_200_512_04drb/encdec_noing23_200_512_04drb.json'
log_file = '/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing23_200_512_04drb/encdec_noing23_200_512_04drb_logs.json'
import json
import matp... |
14,726 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
类元编程是指在运行时创建或定制类的技艺，在 Python 中，类是一等对象，因此任何时候都可以使用函数新建类，无需使用 class 关键字。类装饰器也是函数，不公审查，修改甚至可以把被装饰类替换成其它类。最后，元类是类元编程最高级的工具，使用元类可以创建具有某种特质的全新类种，例如我们见过的抽象基类
类工厂函数
标准库的一个类工厂函数 -- collections.namedt... | Python Code:
class Dog:
def __init__(self, name, weight, owner):
self.name = name
self.weight = weight
self.owner = owner
rex = Dog('Rex', 30, 'Bob')
rex
Explanation: 类元编程是指在运行时创建或定制类的技艺，在 Python 中，类是一等对象，因此任何时候都可以使用函数新建类，无需使用 class 关键字。类装饰器也是函数，不公审查，修改甚至可以把被装饰类替换成其它类。最后，元类是类元编程最高级... |
14,727 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Loschmidt Plots
Plots for the recirq.otoc.loschmidt.tilted_sqare_lattice algorithmic benchmark. See the analysis-walkthrough.ipynb notebook for more detail into the functions used to create ... | Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
# Set up reasonable defaults for figure fonts
import matplotlib
matplotlib.rcParams.update(**{
'axes.titlesize': 14,
'axes.labelsize': 14,
'xtick.labelsize': 12,
'ytick.labelsize': 12,
'legend.fontsize': 12,
'legend.title_fonts... |
14,728 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compute source power using DICS beamformer
Compute a Dynamic Imaging of Coherent Sources (DICS)
Step1: Reading the raw data and creating epochs
Step2: We are interested in the beta band. ... | Python Code:
# Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
# Roman Goj <roman.goj@gmail.com>
# Denis Engemann <denis.engemann@gmail.com>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import mne
from mne.datasets import... |
14,729 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Summary tutorial
Step1: To check that everything is operating as expected we can check that the imports succeed.
Step2: Basic Example
Let's say that we have the following function and we w... | Python Code:
!pip install --upgrade git+https://github.com/google/learned_optimization.git oryx tensorflow==2.8.0rc0 numpy
Explanation: Summary tutorial: Getting metrics out of your models
The goal of the learned_optimization.summary module is to seamlessly allow researchers to annotate and extract data from within a j... |
14,730 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Reusable Embeddings
Learning Objectives
1. Learn how to use a pre-trained TF Hub text modules to generate sentence vectors
1. Learn how to incorporate a pre-trained TF-Hub module into a Kera... | Python Code:
import os
import pandas as pd
from google.cloud import bigquery
Explanation: Reusable Embeddings
Learning Objectives
1. Learn how to use a pre-trained TF Hub text modules to generate sentence vectors
1. Learn how to incorporate a pre-trained TF-Hub module into a Keras model
1. Learn how to deploy and use a... |
14,731 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Absorbing Random Walk Centrality
A short introduction by example
The absorbing-centrality module contains an implementation for a greedy algorithm to compute the k-central nodes in a graph ... | Python Code:
graph = nx.karate_club_graph() # load the graph
node_positions = nx.spring_layout(graph) # fix the node positions
make_graph_plot(graph, node_positions, node_size = 0,
node_color = "white", with_labels = True)
Explanation: Absorbing Random Walk Centrality
A short introduction by example... |
14,732 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Receptive Field Estimation and Prediction
This example reproduces figures from Lalor et al.'s mTRF toolbox in
MATLAB
Step1: Load the data from the publication
First we will load the data c... | Python Code:
# Authors: Chris Holdgraf <choldgraf@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# Nicolas Barascud <nicolas.barascud@ens.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
from os.path import join
import mne
from mne.dec... |
14,733 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Datasets
機器學習資料集/ 範例三
Step1: (二)資料集介紹
digits = datasets.load_digits() 將一個dict型別資料存入digits，我們可以用下面程式碼來觀察裏面資料
Step2: | 顯示 | 說明 |
| -- | -- |
| ('target_names', (3L,))| 共有三種鳶尾花 setosa, versic... | Python Code:
#這行是在ipython notebook的介面裏專用，如果在其他介面則可以拿掉
%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
from sklearn.decomposition import PCA
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first t... |
14,734 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
evaluate predictions
| Python Code::
mean_absolute_error(y_test, predictions)
|
14,735 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Initialization
Welcome to the first assignment of "Improving Deep Neural Networks".
Training your neural network requires specifying an initial value of the weights. A well chosen initializ... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
import sklearn
import sklearn.datasets
from init_utils import sigmoid, relu, compute_loss, forward_propagation, backward_propagation
from init_utils import update_parameters, predict, load_dataset, plot_decision_boundary, predict_dec
%matplotlib inline
plt... |
14,736 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tuples and Lists
Tuples
A Python Tuple is an immutable
sequence of fixed sized. They are created using round brackets () with commas to separate the elements.
Step1: The elements of a tupl... | Python Code:
('x', 'y', 'z')
Explanation: Tuples and Lists
Tuples
A Python Tuple is an immutable
sequence of fixed sized. They are created using round brackets () with commas to separate the elements.
End of explanation
(1, 'b', 2.5)
Explanation: The elements of a tuple need not have the same type.
End of explanation
... |
14,737 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
# Overall Summary
Step1: Monthly stats
Step2: Daily stats -- weekdays
Step3: There are weekly pattern in booking time, high from Monday to Fri, low in the Friday and weekend.
Monthly stat... | Python Code:
daily_stats[['count_click', 'count_booking_train', 'count_booking_test']].sum()/1000
print 'booking ratio for train set: ', daily_stats.count_booking_train.sum() * 1.0 \
/ (daily_stats.count_click.sum() + daily_stats.count_booking_train.sum())
print 'daily booking in train set: ', daily_stats.count_booki... |
14,738 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exercise Answer Key
Step1: Helper Functions
Step2: Exercise 1
Step3: b. $1 Bets
By running 1000 simulations, find the mean and standard deviation of the payout if instead you bet $1 at a ... | Python Code:
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import math
import cvxpy
Explanation: Exercise Answer Key: Position Concentration Risk
Lecture Link
This exercise notebook refers to this lecture. Please use the lecture for explanations and sample code.
http... |
14,739 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exercise 5.1
Step1: Optimization
Maximum likelihood optimization is a statistical method for finding the best fitting set of parameters for a model. A likelihood function can be made up of ... | Python Code:
import scipy.optimize as so
import numpy
import toyplot
Explanation: Exercise 5.1: Likelihood model optimization
This exercise uses the Python programming language. We will make use of the statistical libraries scipy and numpy to generate data under a parametric model (a model that takes one or more variab... |
14,740 | Given the following text description, write Python code to implement the functionality described.
Description:
Check whether product of digits at even places is divisible by sum of digits at odd place of a number
Below function checks whether product of digits at even places is divisible by sum of digits at odd places ... | Python Code:
def productSumDivisible(n ) :
sum = 0
product = 1
num = str(n )
for i in range(len(num ) ) :
if(i % 2 != 0 ) :
product = product * int(num[i ] )
else :
sum = sum + int(num[i ] )
if(product % sum == 0 ) :
return True
return False
if __name__== "__main __":
n = 1234 ... |
14,741 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Five programming problems every Software Engineer should be able to solve in less than 1 hour
http
Step1: Problem 2
Write a function that combines two lists by alternatingly taking elements... | Python Code:
a = [1,2,3,4,5,6,7]
def adding(x):
total = 0
for i in x:
total += i
print(total)
total = 0
i = 0
while i < len(x):
total += x[i]
i += 1
print(total)
return
adding(a)
b = [1,2,3,4,5,6,7]
def recur(lst,counter,total):
if counter == le... |
14,742 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Convolutional Networks
So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected net... | Python Code:
# As usual, a bit of setup
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.cnn import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient
from cs231n.layers... |
14,743 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Working with CTF data
Step1: To reduce memory consumption and running time, some of the steps are
precomputed. To run everything from scratch change use_precomputed to
False. With use_preco... | Python Code:
# Authors: Mainak Jas <mainak.jas@telecom-paristech.fr>
# Eric Larson <larson.eric.d@gmail.com>
# Jaakko Leppakangas <jaeilepp@student.jyu.fi>
#
# License: BSD-3-Clause
import os.path as op
import pandas as pd
import numpy as np
import mne
from mne import combine_evoked
from mne.minimum_n... |
14,744 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
split into train test sets
| Python Code::
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
|
14,745 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Porkchops with poliastro
Porkchops are also known as mission design curves since they show different parameters used to design the ballistic trajectories for the targetting problem such us
S... | Python Code:
import astropy.units as u
from poliastro.plotting.porkchop import porkchop
from poliastro.bodies import Earth, Mars
from poliastro.util import time_range
launch_span = time_range("2005-04-30", end="2005-10-07")
arrival_span = time_range("2005-11-16", end="2006-12-21")
Explanation: Porkchops with poliastro
... |
14,746 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
최적 정규화
알파 최적화 문제
오차-분산 트레이드오프
모형 최적화 뒤의 편향 오차(bias)와 오차 분산(variance)는 다음과 같은 트레이드-오프(trade-off) 관계를 가진다. 즉, 어느 하나가 작아지면 다른 하나는 커지게 된다.
* 이 방식 잘 안 쓴다. 안 쓰는 이유는 분산과 바이어스를 또 얼만큼 맞춰야 하는지 기준을 정해야... | Python Code:
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import cross_val_score
n_samples = 1000
np.random.seed(0)
X = np.sort(np.random.rand(n_samples))
y = np.cos(1.5 * np.pi * X) + np.random... |
14,747 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
BottomUpParceLiNGAM
Import and settings
In this example, we need to import numpy, pandas, and graphviz in addition to lingam.
Step1: Test data
First, we generate a causal structure with 7 v... | Python Code:
import numpy as np
import pandas as pd
import graphviz
import lingam
from lingam.utils import print_causal_directions, print_dagc, make_dot
import warnings
warnings.filterwarnings('ignore')
print([np.__version__, pd.__version__, graphviz.__version__, lingam.__version__])
np.set_printoptions(precision=3, su... |
14,748 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
BASIC CONCEPTS TO REMEMBER
Step1: USING EXPRESSIONS AS INDICES
Step2: List slices
Step3: List comprehensions
Step4: modular operator
Step5: String operations
Step6: string indexes and ... | Python Code:
x = [5, 10, 15, 20, 25, 30]
x[3]
[2, 4, 6, 8, 10][4]
#Index Beyond list #this is suppose to go wrong
x[90]
type(x)
type(x[0])
#type of list values can be diffetent from the list.
len([10])
#empty list is an starting point
len([])
len([])
max(x), sum(x)
sorted([x])
#bring is it to you in order
range(0,10)
l... |
14,749 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Titanic Survival with DNN
Predicting survival on the Titanic using an artificial neural network in Keras
Supervised Learning. Binary classification
This project is based on a dataset contain... | Python Code:
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import helper
import keras
helper.info_gpu()
helper.reproducible(seed=0) # Setup reproducible results from run to run using Keras
%matplotlib inline
Explanation: Titanic Survival with DNN
Predicting survi... |
14,750 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Facies classification using Machine Learning- Majority voting
Contest entry by Priyanka Raghavan and Steve Hall
This notebook demonstrates how to train a machine learning algorithm to predic... | Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
from pandas import set_option
set_option("display.max_rows", 10)
pd.options.mode.chained_assignment =... |
14,751 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
An Introduction to K-Means Clustering
by Scott Hendrickson & Fiona Pigott
K-Means is for learning unknown categories
K-means is a machine learning technique for learning unknown categories--... | Python Code:
# Import some python libraries that we'll need
import matplotlib.pyplot as plt
import random
import math
import sys
%matplotlib inline
def make_data(n_points, n_clusters=2, dim=2, sigma=1):
x = [[] for i in range(dim)]
for i in range(n_clusters):
for d in range(dim):
x[d].extend... |
14,752 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Primitive generators
This notebook contains tests for tohu's primitive generators.
Step1: Constant
Constant simply returns the same, constant value every time.
Step2: Boolean
Boolean retur... | Python Code:
import tohu
from tohu.v5.primitive_generators import *
from tohu.v5.utils import print_generated_sequence
print(f'Tohu version: {tohu.__version__}')
Explanation: Primitive generators
This notebook contains tests for tohu's primitive generators.
End of explanation
g = Constant('quux')
print_generated_sequen... |
14,753 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Predict Shakespeare with Cloud TPUs and Keras
Overview
This example uses tf.keras to build a language model and train it on a Cloud TPU. This language model predicts t... | 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... |
14,754 | 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 ... |
14,755 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Field correlations in Atom-Cavity systems
A reproduction and verification of Rebic et al. PRA 69, 035804 (2004)
Step1: The states will be $\big|m\big\rangle \otimes \big|n\big\rangle$ where... | Python Code:
from qutip import *
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
print(qutip.__version__)
import sys
print(sys.version)
# Note, it works fine to truncate at 4 (as in the paper)
# QuTiP can do much larger space just fine so feel free to increase this.
N=4
taus=np.linspace(0,10,500)
... |
14,756 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step2: Imports
Step3: tf.data.Dataset
Step4: Let's have a look at the data
Step5: Keras model
If you are not sure what cross-entropy, dropout, softmax or batch-normalizati... | Python Code:
BATCH_SIZE = 128
EPOCHS = 10
training_images_file = 'gs://mnist-public/train-images-idx3-ubyte'
training_labels_file = 'gs://mnist-public/train-labels-idx1-ubyte'
validation_images_file = 'gs://mnist-public/t10k-images-idx3-ubyte'
validation_labels_file = 'gs://mnist-public/t10k-labels-idx1-ubyte'
Expl... |
14,757 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exercises
Step1: Data
Step2: Exercise 1
Step3: b. Standard Deviation
Determine standard deviation of the sample.
Step4: c. Standard Error
Using the standard deviation and sample_size, de... | Python Code:
def generate_autocorrelated_data(theta, mu, sigma, N):
X = np.zeros((N, 1))
for t in range(1, N):
X[t] = theta * X[t-1] + np.random.normal(mu, sigma)
return X
def newey_west_SE(data):
ind = range(0, len(data))
ind = sm.add_constant(ind)
model = regression.linear_model.OLS(da... |
14,758 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Variables
In computer programming, a variable is a storage location and an associated symbolic name (an identifier) which contains some known or unknown quantity or information, a value.
C
c... | Python Code:
'''
variable assignments
this is a variable assignment
'''
x = 1.0
my_variable = 12
print type(x)
print type(my_variable)
Explanation: Variables
In computer programming, a variable is a storage location and an associated symbolic name (an identifier) which contains some known or unknown quantity or inform... |
14,759 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Batch Normalization – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a convolutional neural network with 20 convolutional l... | Python Code:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
Explanation: Batch Normalization – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll crea... |
14,760 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Text-to-Video retrieval with S3D MIL-NCE
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https
Step3: 导入 TF-Hub 模型
本教程演示了如何使用 TensorFlow Hub 中的 S3D MIL-NCE ... | Python Code:
!pip install -q opencv-python
import os
import tensorflow.compat.v2 as tf
import tensorflow_hub as hub
import numpy as np
import cv2
from IPython import display
import math
Explanation: Text-to-Video retrieval with S3D MIL-NCE
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href=... |
14,761 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Handling Utilities
tiff file directory to tiff stack conversion
A utility script that can be executed from the command line to convert tif files in a directory into a tif stack
Step1: ... | Python Code:
%%bash
build_tiff_stack.py --help
Explanation: Data Handling Utilities
tiff file directory to tiff stack conversion
A utility script that can be executed from the command line to convert tif files in a directory into a tif stack:
End of explanation
%%bash
extract_channels_from_raw.py --help
Explanation: Th... |
14,762 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Generate data needed for visualization in Tableau
Start with the standard imports we have used for every notebook in this class.
Step1: Each of the datasheets downloaded from ELSI had downl... | Python Code:
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('display.notebook_repr_html', True)
import seaborn as ... |
14,763 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Question 2
Step1: Question 3.1
Step2: Question 3.2 Does the most popular of all the 'lil' has the more followers?
Step3: Question 4 (first part)
Step4: Question 5 Picking up artists
Step... | Python Code:
#Question 2 awnser.
for artist in artists:
print(artist['name'], artist['popularity'])
if len(artist['genres']) == 0:
print("no genres listed")
else:
genres = ", ".join(artist['genres'])
print("Genres list: ", genres)
Explanation: Question 2: What genres are most represe... |
14,764 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
← Back to Index
Basic Feature Extraction
Somehow, we must extract the characteristics of our audio signal that are most relevant to the problem we are trying to solve. For example, if w... | Python Code:
kick_signals = [
librosa.load(p)[0] for p in Path().glob('audio/drum_samples/train/kick_*.mp3')
]
snare_signals = [
librosa.load(p)[0] for p in Path().glob('audio/drum_samples/train/snare_*.mp3')
]
len(kick_signals)
len(snare_signals)
Explanation: ← Back to Index
Basic Feature Extraction
Someh... |
14,765 | Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
I'm using tensorflow 2.10.0. | Problem:
import tensorflow as tf
x = tf.Variable(0)
x.assign(114514) |
14,766 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compare evoked responses for different conditions
In this example, an Epochs object for visual and auditory responses is created.
Both conditions are then accessed by their respective names ... | Python Code:
# Authors: Denis Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
from mne.viz import plot_evoked_topo
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
Explanation:... |
14,767 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Importing the CSV files
This CSV file is available on Irans dataset in World bank](https
Step1: As I wanted the emission types be my coloumns and the years be the rows, I used transpose() f... | Python Code:
# Importing Iran`s dataset
IRAN_SOURCE_FILE = 'iran_emission_dataset.csv'
iran_csv = pd.read_csv(IRAN_SOURCE_FILE)
iran_csv.head(5)
# Importing Turkey`s dataset
TURKEY_SOURCE_FILE = 'turkey_emission_dataset.csv'
turkey_csv = pd.read_csv(TURKEY_SOURCE_FILE)
turkey_csv.head(5)
Explanation: Importing the CSV... |
14,768 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
operator* multiplies the same reference
Step1: Changing l[1] actually references a different object
Integers are immutable.
Step2: Setting l[2] to the same number does not create a new obj... | Python Code:
l = [[]] * 3
l[0] is l[1], l[0] is l[2]
l[0].append("abc")
l
l = [1] * 3
print(l)
l[0] is l[1], l[0] is l[2]
Explanation: operator* multiplies the same reference
End of explanation
l[1] = 2
print(l)
l[0] is l[1], l[0] is l[2], l[1] is l[2]
Explanation: Changing l[1] actually references a different object
I... |
14,769 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
An introduction to NumPy
NumPy provides an efficient representation of multidimensional datasets like vectors and matricies, and tools for linear algebra and general matrix manipulations - e... | Python Code:
import numpy as np
Explanation: An introduction to NumPy
NumPy provides an efficient representation of multidimensional datasets like vectors and matricies, and tools for linear algebra and general matrix manipulations - essential building blocks of virtually all technical computing
Typically NumPy is impo... |
14,770 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Vertex SDK
Step1: Install the latest GA version of google-cloud-storage library as well.
Step2: Restart the kernel
Once you've installed the additional packages, you need to restart the no... | Python Code:
import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
Explanation: Vertex SDK: Custom training tabular regression model for batch prediction with explainabi... |
14,771 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Calculating Seasonal Averages from Timeseries of Monthly Means
Author
Step1: Some calendar information so we can support any netCDF calendar.
Step4: A few calendar functions to determine t... | Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import xarray as xr
from netCDF4 import num2date
import matplotlib.pyplot as plt
print("numpy version : ", np.__version__)
print("pandas version : ", pd.__version__)
print("xarray version : ", xr.__version__)
Explanation: Calculating Seasonal Ave... |
14,772 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Batch Normalization
One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to c... | Python Code:
# As usual, a bit of setup
from __future__ import print_function
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
fro... |
14,773 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The EM algorithm for Hawkes processes
Here we explore the optimisation algorithm for parameter estimation given in
Mohler et al. "Randomized Controlled Field Trials of Predictive Policing". ... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
Explanation: The EM algorithm for Hawkes processes
Here we explore the optimisation algorithm for parameter estimation given in
Mohler et al. "Randomized Controlled Field Trials of Predictive Policing". Journal of the American Statistica... |
14,774 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<a href="https
Step1: Neural style transfer
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https
Step2: Download images and choose a style image and ... | 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... |
14,775 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Introduction" data-toc-modified-id="Introduction-1">Introduction</a></span><... | Python Code:
id_ = 'N1467344745'
Explanation: <h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Introduction" data-toc-modified-id="Introduction-1">Introduction</a></span><ul class="toc-item"><li><span><a href="#Setup" data-toc-modified-id="Setup-1.1">Setu... |
14,776 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
PySAL Change Log Statistics
This notebook generates the summary statistics for a package.
It assumes you are running this under the tools directory at the toplevel of the package
Change the... | Python Code:
package_name = 'spint'
release_date = '2020-09-08'
start_date = '2019-07-22'
Explanation: PySAL Change Log Statistics
This notebook generates the summary statistics for a package.
It assumes you are running this under the tools directory at the toplevel of the package
Change the values only in the next ce... |
14,777 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Lecture
Step1: When to use python? -- 50xp, Status
Step3: Python as a calculator -- 100xp, Status
Step5: Lecture
Step7: 2. Calculations with variables
Remember how you calculated the... | Python Code:
# working with print function
print(5 / 8)
# Add another print function on new line
print(7 + 10)
Explanation: Lecture : Hello Python!
[RQ-1] : Which of the following statements is correct?
Ans: The Ipython Shell is typically used to work with Python interactively.
[RQ-2] : Which file extension is used for... |
14,778 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Object Model
bqplot is based on Grammar of Graphics paradigm. The Object Model in bqplot gives the user the full flexibility to build custom plots. This means the API is verbose but fully cu... | Python Code:
from bqplot import (LinearScale, Axis, Figure, OrdinalScale,
LinearScale, Bars, Lines, Scatter)
# first, let's create two vectors x and y to plot using a Lines mark
import numpy as np
x = np.linspace(-10, 10, 100)
y = np.sin(x)
# 1. Create the scales
xs = LinearScale()
ys = LinearScale... |
14,779 | 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
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 researchers are creating new b... |
14,780 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pandas
Step1: Datafreymin əsasına yerləşəcək verilənlər mənbə üzrə üzrə daxili və xarici formalara bölünür
Step2: Növbəti email_list_lst_cln dəyşəninə isə sütun adlarından ibarət siyahı tə... | Python Code:
import pandas as pd
Explanation: Pandas: DataFreym yaratmağın müxtəlif üsulları
İlk öncə pandas-da verilənlərin 2D əndazəli forma (sadə dil ilə "cədvəl") daxilində saxlanma vasitəsi və forması olan Dataframe yaratmaqdan başlayaq. Bu dərs tam olaraq müxtəlif mənbələrdən və formalarda əldə edilmiş məlumatı D... |
14,781 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Speci... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'sandbox-3', 'ocnbgchem')
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: NERC
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topics: Tracers.
Prop... |
14,782 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1>Table of Contents<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Intro" data-toc-modified-id="Intro-1"><span class="toc-item-num">1 &nbs... | Python Code:
# Basic libraries import
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib
from matplotlib import animation
from PIL import Image, ImageDraw
import os
import sys
import itertools
import collections
from math import cos, sin, pi
# Plotting
%matplo... |
14,783 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Learning avalanche problems by meteorological factors
Step1: Split into test and traininng data to run a prediction
We use the avalanche forecasts from Nordvestlandet including the forecast... | Python Code:
import pandas as pd
import numpy as np
import json
import graphviz
import matplotlib.pyplot as plt
from sklearn import tree
from sklearn.preprocessing import LabelEncoder
from pprint import pprint
pd.set_option("display.max_rows",6)
%matplotlib inline
Explanation: Learning avalanche problems by meteorologi... |
14,784 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction to numerical simulations
Step1: Next, we will need parameters for the simulation. These are known as initial condititons. For a 2 body gravitation problem, we'll need to know t... | Python Code:
#Physical Constants (SI units)
G=6.67e-11 #Universal Gravitational constant in m^3 per kg per s^2
AU=1.5e11 #Astronomical Unit in meters = Distance between sun and earth
daysec=24.0*60*60 #seconds in a day
Explanation: Introduction to numerical simulations: The 2 Body Problem
Many problems in statistical p... |
14,785 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Beta Hedging
By Evgenia "Jenny" Nitishinskaya and Delaney Granizo-Mackenzie with example algorithms by David Edwards
Part of the Quantopian Lecture Series
Step1: Now we can perform the regr... | Python Code:
# Import libraries
import numpy as np
from statsmodels import regression
import statsmodels.api as sm
import matplotlib.pyplot as plt
import math
# Get data for the specified period and stocks
start = '2014-01-01'
end = '2015-01-01'
asset = get_pricing('TSLA', fields='price', start_date=start, end_date=end... |
14,786 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Load the data from our JSON file.
The data is stored as a dictionary of dictionaries in the json file. We store it that way beacause it's easy to add data to the existing master data file. A... | Python Code:
with open('../pipeline/data/ProcessedDay90ApartmentData.json') as g:
my_dict2 = json.load(g)
dframe2 = DataFrame(my_dict2)
dframe2 = dframe2.T
dframe2 = dframe2[['content', 'laundry', 'price', 'dog', 'bed',
'bath', 'feet', 'long', 'parking', 'lat', 'smoking', 'getphotos',
'cat', 'hasmap', 'wheelchai... |
14,787 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Creating Ukulele Chord Diagrams in SVG with Python
With the Python modul uchord you can create ukulele chord diagrams in SVG format.
Step1: <img src="pic/c.svg" align="left"><br><br><br><br... | Python Code:
import uchord
uchord.write_chord('c.svg','C','0003')
Explanation: Creating Ukulele Chord Diagrams in SVG with Python
With the Python modul uchord you can create ukulele chord diagrams in SVG format.
End of explanation
pip install uchord
Explanation: <img src="pic/c.svg" align="left"><br><br><br><br><br>
If... |
14,788 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The largest prime (so far)
A new record for the largest prime has been found lately. Let explore this number and know more about it and how to deal with it using some Python, Numpy and final... | Python Code:
import numpy as np
import math
from datetime import datetime
%load_ext Cython
Explanation: The largest prime (so far)
A new record for the largest prime has been found lately. Let explore this number and know more about it and how to deal with it using some Python, Numpy and finally using we will take a lo... |
14,789 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
On this notebook the best models and input parameters will be searched for. The problem at hand is predicting the price of any stock symbol 28 days ahead, assuming one model for all the symb... | Python Code:
# Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
%matplotlib inline
%pylab inline
pylab.rcParams['figure.figsize'] ... |
14,790 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Named Entity Recognition
In any text document, there are particular terms that represent specific entities that are more informative and have a unique context. These entities are know... | Python Code:
text = Three more countries have joined an “international grand committee” of parliaments, adding to calls for
Facebook’s boss, Mark Zuckerberg, to give evidence on misinformation to the coalition. Brazil, Latvia and Singapore
bring the total to eight different parliaments across the world, with plans to... |
14,791 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Using Interrupts and asyncio for Buttons and Switches
This notebook provides a simple example for using asyncio I/O to interact asynchronously with multiple input devices. A task is created ... | Python Code:
from pynq import PL
from pynq.overlays.base import BaseOverlay
base = BaseOverlay("base.bit")
Explanation: Using Interrupts and asyncio for Buttons and Switches
This notebook provides a simple example for using asyncio I/O to interact asynchronously with multiple input devices. A task is created for each i... |
14,792 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<img src="images/JHI_STRAP_Web.png" style="width
Step1: <a id="load"></a>
Load results
We load data from the multiplexed run that was performed on the JHI cluster, as described in README.md... | Python Code:
%pylab inline
import os
import pickle
import warnings; warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import scipy
import seaborn as sns; sns.set_context('notebook')
import tools
Explanation: <img src="images/JHI_STRAP_Web.png" style="width: 150px; float: right;">
Supplementary In... |
14,793 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Features as a Representation of Time Series for Classification
version 0.1
By AA Miller (Northwestern CIERA/Adler Planetarium)
10 June 2019
This lecture is about machine learning...
But hone... | Python Code:
def lc_plot(t, m, m_unc, period=0.0):
if period == 0.0:
fig, ax = plt.subplots()
ax.errorbar(t, m, m_unc,
fmt='o', color='MediumAquaMarine',
mec="0.2",mew=0.5)
ax.set_xlabel('HJD (d)')
ax.set_ylabel(r'$V_\mathrm{ASAS}\;(\mathrm{ma... |
14,794 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Augmentation
Este notebook ha sido creado para el uso de Data augmentation sobre nuestro conjunto de caras. Con el objetivo de aumentar y ofrecer un conjunto más variado de imagenes.
Ca... | Python Code:
from sklearn.datasets import fetch_lfw_people
# Importamos mediante una de las dos alternativas
# 1ª alternativa devuelve las imagenes en RGB pero con sus
# respectivos tres valores
faces = fetch_lfw_people(color = True)
positive_patches = faces.images
positive_patches.shape
Explanation: Data Augmentation
... |
14,795 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
UCI Datasets
Step1: The standard datasets are taken from the UCI Machine Learning Repository. For each dataset, header rows are added manually.
Binary
Ionosphere radar data, where 'good' is... | Python Code:
import os
import pandas as pd
import numpy as np
from mclearn.tools import fetch_data, download_data
%load_ext autoreload
%autoreload 2
uci_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/'
Explanation: UCI Datasets
End of explanation
url = uci_url + 'ionosphere/ionosphere.data'
dest = 'da... |
14,796 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Problem-set-Jupyter-Pyplot-and-Numpy
Write a note about the data set
Fisher's Iris Data Set is a well known data set that has become a common test case in machine learning. Each row in the d... | Python Code:
import numpy as np
# Load in data from csv file.
sepal_length, sepal_width, petal_length, petal_width = np.genfromtxt('../data/IRIS.csv', delimiter=',', usecols=(0,1,2,3), unpack=True, dtype=float)
iris_class = np.genfromtxt('../data/IRIS.csv', delimiter=',', usecols=(4), unpack=True, dtype=str)
# Loaded t... |
14,797 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Tutorial Part 3
Step1: Now, let's use MoleculeNet to load the Tox21 dataset. We need to make sure to process the data in a way that graph convolutional networks can use For that, we make su... | Python Code:
import deepchem as dc
from deepchem.models.tensorgraph.models.graph_models import GraphConvModel
Explanation: Tutorial Part 3: Introduction to Graph Convolutions
In the previous sections of the tutorial, we learned about Dataset and Model objects. We learned how to load some data into DeepChem from files o... |
14,798 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Laplace approximation ( Quadratic approximation)
In this notebook we will approximate posterior of beta-bernouli model for coin toss problem using laplace approximation method
Step1: Laplac... | Python Code:
try:
from probml_utils import latexify, savefig
except:
%pip install git+https://github.com/probml/probml-utils.git
from probml_utils import latexify, savefig
import jax
import jax.numpy as jnp
from jax import lax
try:
from tensorflow_probability.substrates import jax as tfp
except ModuleNo... |
14,799 | 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 6</font>
Download
Step1: Leitura de dados | 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 6</font>
Download: http://github.com/dsacademybr
End of explanation
impor... |
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