Unnamed: 0 int64 0 16k | text_prompt stringlengths 110 62.1k | code_prompt stringlengths 37 152k |
|---|---|---|
10,200 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TensorFlow Tutorial #16
Reinforcement Learning (Q-Learning)
by Magnus Erik Hvass Pedersen
/ GitHub / Videos on YouTube
Introduction
This tutorial is about so-called Reinforcement Learning in... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import gym
import numpy as np
import math
# Use TensorFlow v.2 with this old v.1 code.
# E.g. placeholder variables and sessions have changed in TF2.
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
Explanation: TensorFlow Tutorial #16
Reinforcem... |
10,201 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Character Sequence to Sequence
In this notebook, we'll build a model that takes in a sequence of letters, and outputs a sorted version of that sequence. We'll do that using what we've learne... | Python Code:
import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
Explanation: Character Sequence to Sequence
In this notebook, we'll build a model that takes in a sequence of lette... |
10,202 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Advanced model training using hyperopt
In the Advanced Model Training tutorial we have already taken a look into hyperparameter optimasation using GridHyperparamOpt in the deepchem pacakge. ... | Python Code:
!pip install deepchem
!pip install hyperopt
Explanation: Advanced model training using hyperopt
In the Advanced Model Training tutorial we have already taken a look into hyperparameter optimasation using GridHyperparamOpt in the deepchem pacakge. In this tutorial, we will take a look into another hyperpara... |
10,203 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Econophysics
Names of group members
// put your names here!
Goals of this assignment
Witness what we call "emergent behavior"; large patterns manifesting from the simple interactions of tiny... | Python Code:
# Use Python to make a filled-in plot
# from the data that got reported out
Explanation: Econophysics
Names of group members
// put your names here!
Goals of this assignment
Witness what we call "emergent behavior"; large patterns manifesting from the simple interactions of tiny agents
Develop a graphica... |
10,204 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Instalación
Lo primero es instalar Python. Para ello, la mejor forma es bajarse Anaconda, está disponible en Windows, Linux y MacOS. <img src="http
Step1: Crear un entorno
Anaconda nos per... | Python Code:
#from IPython.display import HTML
#HTML('''<script>
#code_show=true;
#function code_toggle() {
# if (code_show){
# $('div.input').hide();
# } else {
# $('div.input').show();
# }#
# code_show = !code_show
#}
#$( ocument ).ready(code_toggle);
#</script>
#The raw code for this IPython notebook is by default... |
10,205 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
The number of unique values is huge. This makes me think in a direction where we could center basis functions at the centers of discovered clusters. Discover cluster centers via K-Means?
Ste... | Python Code:
train_X = train.values[:,:-1]
train_t = train.values[:,-1]
print train_X.shape
print train_t.shape
train.describe()
train.head()
train.tail()
Explanation: The number of unique values is huge. This makes me think in a direction where we could center basis functions at the centers of discovered clusters. Dis... |
10,206 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: 深度卷积生成对抗网络
<table class="tfo-notebook-buttons" align="left">
<td> <a target="_blank" href="https
Step2: 加载和准备数据集
您将使用 MNIST 数据集来训练生成器和判别... | 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... |
10,207 | 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
<table align="l... |
10,208 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Preprocessing
For local run, we cannot afford running with full data. We will sample the data randomly (using hash) to about 200~300 instances. It takes about 15 minutes.
Step1: Train
To ge... | Python Code:
import mltoolbox.image.classification as model
from google.datalab.ml import *
worker_dir = '/content/datalab/tmp/coast'
preprocessed_dir = worker_dir + '/coast300'
model_dir = worker_dir + '/model300'
train_set = BigQueryDataSet('SELECT image_url, label FROM coast.train WHERE rand() < 0.04')
model.preproc... |
10,209 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Comparing Data
You'll often want to compare data in your dataset, to see if you can discern trends or relationships.
Univariate Data
Univariate data is data that consist of only one variable... | Python Code:
%matplotlib inline
import pandas as pd
from matplotlib import pyplot as plt
df = pd.DataFrame({'Name': ['Dan', 'Joann', 'Pedro', 'Rosie', 'Ethan', 'Vicky', 'Frederic', 'Jimmie', 'Rhonda', 'Giovanni', 'Francesca', 'Rajab', 'Naiyana', 'Kian', 'Jenny'],
'Grade':[50,50,46,95,50,5,57,42,26,72... |
10,210 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
FPE Interface Board Bring-up Procedure
Abstract
Step1: Test Start Date
Step2: Test Conductor Information
Please write down your personal information for accountability purposes
Step3: Uni... | Python Code:
import random
test_check = {}
Explanation: FPE Interface Board Bring-up Procedure
Abstract: This iPython Notebook contains instructions for the FPE Interface Board PCB Bring-up test flow. This procedure can be used for the Interface Boards, versions 6.2 and 7.0. Simliar iPython Notebooks will be created ... |
10,211 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python Basics with Numpy (optional assignment)
Welcome to your first assignment. This exercise gives you a brief introduction to Python. Even if you've used Python before, this will help fam... | Python Code:
### START CODE HERE ### (≈ 1 line of code)
test = 'Hello World'
### END CODE HERE ###
print ("test: " + test)
Explanation: Python Basics with Numpy (optional assignment)
Welcome to your first assignment. This exercise gives you a brief introduction to Python. Even if you've used Python before, this will he... |
10,212 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: MLE fit for three component binding - simulated data
In this notebook we will see how well we can reproduce Kd of a non-fluorescent ligand from simulated experimental data with a maxi... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
from scipy import optimize
import seaborn as sns
%pylab inline
#Competitive binding function
#This function and its assumptions are defined in greater detail in this notebook:
## modelling-CompetitiveBinding-ThreeComponentBinding.ipynb
def three_component... |
10,213 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Welcome to Time Series!
Forecasting is perhaps the most common application of machine learning in the real world. Businesses forecast product demand, governments forecast economic and popula... | Python Code:
#$HIDE_INPUT$
import pandas as pd
df = pd.read_csv(
"../input/ts-course-data/book_sales.csv",
index_col='Date',
parse_dates=['Date'],
).drop('Paperback', axis=1)
df.head()
Explanation: Welcome to Time Series!
Forecasting is perhaps the most common application of machine learning in the real wor... |
10,214 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: RASP Diabetes Rates
Diabetes rates from CHIS surveys for 2015, 2016 and 2017, segmented by race, age, sex and poverty status
Step3: Poverty, Age and Race
Step4: Compare to CHIS
Here... | Python Code:
import seaborn as sns
import metapack as mp
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display
from publicdata.chis import *
%matplotlib inline
sns.set_context('notebook')
idx = pd.IndexSlice # Convenience redefinition.
#pkg = mp.jupyter.open_packag... |
10,215 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Data Input
To do any computation, you need to have data. Getting the data in the framework of a workflow is therefore the first step of every analysis. Nipype provides many different modules... | Python Code:
from nipype import DataGrabber, Node
# Create DataGrabber node
dg = Node(DataGrabber(infields=['subject_id', 'task_id'],
outfields=['anat', 'func']),
name='datagrabber')
# Location of the dataset folder
dg.inputs.base_directory = '/data/ds102'
# Necessary default parameters
... |
10,216 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Generative Adversarial Network
In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten d... | Python Code:
%matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
Explanation: Generative Adversarial Network
In this notebook, we'll be building a gen... |
10,217 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Import directives
Step1: Ordered dictionaries
See https | Python Code:
import collections
Explanation: Import directives
End of explanation
d = collections.OrderedDict()
d["2"] = 2
d["3"] = 3
d["1"] = 1
print(d)
print(type(d.keys()))
print(list(d.keys()))
print(type(d.values()))
print(list(d.values()))
for k, v in d.items():
print(k, v)
Explanation: Ordered dictionaries
S... |
10,218 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h2> 4ppm </h2>
Enough retcor groups, and fewer peak insertion problems than 4.5 or 5ppm.
Step1: <h2> Import the dataframe and remove any features that are all zero </h2>
Step2: <h2> Get m... | Python Code:
import time
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.cross_validation import cross_val_score
#fr... |
10,219 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Merge overlapping striplogs
Imagine we have a Striplog with overlapping Interval. We would like to be able to merge the Intervals in this Striplog, while following some rules about pr... | Python Code:
from striplog import Striplog
csv = Top,Base,Comp Time,Comp Depth
100,200,2,a
110,120,1,b
150,325,3,c
210,225,1,d
300,400,2,e
350,375,3,f
s = Striplog.from_csv(text=csv)
Explanation: Merge overlapping striplogs
Imagine we have a Striplog with overlapping Interval. We would like to be able to merge the Inte... |
10,220 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Python's super()
TODO
* https
Step1: Sans super()
Avant l'existance de la fonction super(), we would have hardwired the call with A.bonjour(self).
...
Step2: Le même exemple avec un argume... | Python Code:
help(super)
Explanation: Python's super()
TODO
* https://docs.python.org/3/library/functions.html#super
* https://rhettinger.wordpress.com/2011/05/26/super-considered-super/
* https://stackoverflow.com/questions/904036/chain-calling-parent-constructors-in-python
* https://stackoverflow.com/questions/239930... |
10,221 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
GPy
GPy is a framework for Gaussian process based applications. It is design for speed and reliability. The main three pillars of its functionality are made of
Ease of use
Reproduceability
... | Python Code:
import GPy, numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
Explanation: GPy
GPy is a framework for Gaussian process based applications. It is design for speed and reliability. The main three pillars of its functionality are made of
Ease of use
Reproduceability
Scalability
In this tuto... |
10,222 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
EventVestor
Step1: Let's go over the columns
Step2: Now suppose we want a DataFrame of the Blaze Data Object above, want to filter it further down to the announcements only, and we only wa... | Python Code:
# import the dataset
from quantopian.interactive.data.eventvestor import issue_equity
# or if you want to import the free dataset, use:
# from quantopian.interactive.data.eventvestor import issue_equity_free
# import data operations
from odo import odo
# import other libraries we will use
import pandas as ... |
10,223 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Background on projectors and projections
This tutorial provides background information on projectors and Signal Space
Projection (SSP), and covers loading and saving projectors, adding and r... | Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa
from scipy.linalg import svd
import mne
def setup_3d_axes():
ax = plt.axes(projection='3d')
ax.view_init(azim=-105, elev=20)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('... |
10,224 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Explicit Solutions
This notebook will show how to fit explicit solutions to summary measures.
It will first fit the Rescorla-Wagner model to the trial-by-trial response rates. It will then f... | Python Code:
# System packages
import sys
# Storing and manipulating data...
import pandas as pd
import numpy as np
from scipy.optimize import curve_fit, minimize
from scipy.stats import norm, pearsonr
# Plotting...
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
plt.rc('text', usetex=True)
# T... |
10,225 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2020 Google LLC.
Licensed under the Apache License, Version 2.0
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with ... | Python Code:
#@title Run this cell after making your choices.
allow_data_collection = True #@param {type: "boolean"}
include_in_dataset = True #@param {type: "boolean"}
if allow_data_collection:
if include_in_dataset:
print('Usage data may be collected and released in a public dataset.')
else:
print('Usag... |
10,226 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Explainable deep-learning -- Visualizing deep neural networks
Author
Step1: Function to load the model for generating predictions
Step2: Function to generate predictions
Step3: Function t... | Python Code:
import sys
import argparse
import numpy as np
import requests
import matplotlib
matplotlib.use('Agg')
import os
import time
import json
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import tqdm
from io import BytesIO
from PIL import Image
from keras.preprocessing import image
... |
10,227 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Fine-tuning a Pretrained Network for Style Recognition
In this example, we'll explore a common approach that is particularly useful in real-world applications
Step1: 1. Setup and dataset do... | Python Code:
caffe_root = '../' # this file should be run from {caffe_root}/examples (otherwise change this line)
import sys
sys.path.insert(0, caffe_root + 'python')
import caffe
caffe.set_device(0)
caffe.set_mode_gpu()
import numpy as np
from pylab import *
%matplotlib inline
import tempfile
# Helper function for de... |
10,228 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Calling Functions from Functions
Step2: Interating over a collection
Make the 12 x 12 times table
Step3: Iterating over a list of strings
Shows how to iterate over strings and that you can... | Python Code:
def add_together(one, two):
one = one + two
return one
def mutiply_and_add(one, two):
one = add_together(one, two)
return one * one
temparary_value = mutiply_and_add(2, 3)
print(temparary_value)
print(mutiply_and_add(2, 3))
Explanation: Calling Functions from Functions
End of explanation
nu... |
10,229 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Step1: Quantum Double-slit Experiment
Step2: Now define the double_slit function and make it interactive | Python Code:
%pylab inline
import numpy as np
import matplotlib.pyplot as plot
from scipy.integrate import trapz,cumtrapz
from IPython.html.widgets import interact, interactive
def distribute1D(x,prob,N):
takes any distribution which is directly proportional
to the number of particles, and returns data that is... |
10,230 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Table of Contents
<p><div class="lev1 toc-item"><a href="#Cookbook-for-cantera_tools-module" data-toc-modified-id="Cookbook-for-cantera_tools-module-1"><span class="toc-item-num">1 &nbs... | Python Code:
import cantera_tools as ctt
import numpy as np
from scipy import integrate
import cantera as ct
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Cookbook-for-cantera_tools-module" data-toc-modified-id="Cookbook-for... |
10,231 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Image Gradients
In this notebook we'll introduce the TinyImageNet dataset and a deep CNN that has been pretrained on this dataset. You will use this pretrained model to compute gradients wit... | Python Code:
# As usual, a bit of setup
import time, os, json
import numpy as np
import skimage.io
import matplotlib.pyplot as plt
from cs231n.classifiers.pretrained_cnn import PretrainedCNN
from cs231n.data_utils import load_tiny_imagenet
from cs231n.image_utils import blur_image, deprocess_image
%matplotlib inline
pl... |
10,232 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regression Week 3
Step1: Next we're going to write a polynomial function that takes an SArray and a maximal degree and returns an SFrame with columns containing the SArray to all the powers... | Python Code:
import graphlab
Explanation: Regression Week 3: Assessing Fit (polynomial regression)
In this notebook you will compare different regression models in order to assess which model fits best. We will be using polynomial regression as a means to examine this topic. In particular you will:
* Write a function t... |
10,233 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Insights from medical posts
In this notebook, I try to find characteristics of medical posts.
What is the ratio of post from professionals vs. those from general public?
What are the char... | Python Code:
# Set up paths/ os
import os
import sys
this_path=os.getcwd()
os.chdir("../data")
sys.path.insert(0, this_path)
# Load datasets
import pandas as pd
df = pd.read_csv("MedHelp-posts.csv",index_col=0)
df.head(2)
df_users = pd.read_csv("MedHelp-users.csv",index_col=0)
df_users.head(2)
# 1 classify users as pr... |
10,234 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Deep LSTM RNNs
Step1: Dataset
Step2: Check the data real quick
Step3: Preparing the data for training
Step4: Long short-term memory (LSTM) RNNs
An LSTM block has mechanisms to enable "me... | Python Code:
from __future__ import print_function
import mxnet as mx
from mxnet import nd, autograd
import numpy as np
from collections import defaultdict
mx.random.seed(1)
# ctx = mx.gpu(0)
ctx = mx.cpu(0)
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
f... |
10,235 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ATM 623
Step1: Contents
Emission temperature and lapse rates
Solar Radiation
Terrestrial Radiation and absorption spectra
<a id='section1'></a>
1. Emission temperature and lapse rates
Plane... | Python Code:
# Ensure compatibility with Python 2 and 3
from __future__ import print_function, division
Explanation: ATM 623: Climate Modeling
Brian E. J. Rose, University at Albany
Lecture 6: A Brief Review of Radiation
Warning: content out of date and not maintained
You really should be looking at The Climate Labora... |
10,236 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Google Hashcode 2022
Google Hashcode is a team programming competition to solve a complex engineering problem.
In this notebook we are showing how Mathematical Optimization methods as Mixed ... | Python Code:
import os
if not os.path.isdir('input_data'):
os.system('git clone https://github.com/ampl/amplpy.git')
os.chdir('amplpy/notebooks/hashcode')
if not os.path.isdir('ampl_input'):
os.mkdir('ampl_input')
Explanation: Google Hashcode 2022
Google Hashcode is a team programming competition to solve a c... |
10,237 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
在 python 中，下划线命名规则往往令人相当疑惑：单下划线、双下划线、双下划线还分前后……那它们的作用与使用场景到底有何区别呢？
1、单下划线（_）
通常情况下，单下划线（_）会在以下3种场景中使用：
1.1 在解释器中：
在这种情况下，“_”代表交互式解释器会话中上一条执行的语句的结果。这种用法首先被标准CPython解释器采用，然后其他类型的解释器也先后采用。
Step... | Python Code:
8 * 9
_ + 8
Explanation: 在 python 中，下划线命名规则往往令人相当疑惑：单下划线、双下划线、双下划线还分前后……那它们的作用与使用场景到底有何区别呢？
1、单下划线（_）
通常情况下，单下划线（_）会在以下3种场景中使用：
1.1 在解释器中：
在这种情况下，“_”代表交互式解释器会话中上一条执行的语句的结果。这种用法首先被标准CPython解释器采用，然后其他类型的解释器也先后采用。
End of explanation
for _ in range(1, 11):
print(_, end='、 ')
Explanation: 1.2 作为一个名称：
这与上面一点... |
10,238 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
OpenStreetMap的OSM文件对象数据分类捡取器
by openthings@163.com, 2016-03-21.
功能：
输出三个单行存储的json文件，可在Spark中使用，通过spark的sc.read.json()直接读入处理。
本工具将osm文件按照tag快速分类，直接转为node/way/relation三个json文件，并按行存储。
说明：
S... | Python Code:
import os
import time
import json
from pprint import *
import lxml
from lxml import etree
import xmltodict, sys, gc
from pymongo import MongoClient
gc.enable() #Enable Garbadge Collection
# 将指定tag的对象提取，写入json文件。
def process_element(elem):
elem_data = etree.tostring(elem)
elem_dict = xmltodict.pars... |
10,239 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Learning Scikit-learn
Step1: Import the digits dataset (http
Step2: Let's show how the digits look like...
Step3: Now, let's define a function that will plot a scatter with the two-dimens... | Python Code:
%pylab inline
import IPython
import sklearn as sk
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
print 'IPython version:', IPython.__version__
print 'numpy version:', np.__version__
print 'scikit-learn version:', sk.__version__
print 'matplotlib version:', matplotlib.__version__
Expla... |
10,240 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Redcard Exploratory Data Analysis
This dataset is taken from a fantastic paper that looks to see how analytical choices made by different data science teams on the same dataset in an attempt... | Python Code:
%matplotlib inline
%config InlineBackend.figure_format='retina'
from __future__ import absolute_import, division, print_function
import matplotlib as mpl
from matplotlib import pyplot as plt
from matplotlib.pyplot import GridSpec
import seaborn as sns
import numpy as np
import pandas as pd
import os, sys
f... |
10,241 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
N.B., Cannot use 32-bit programmable interrupt timer (PIT) to trigger periodic DMA due to hardware bug.
See here.
The solution shown below uses the 16-bit programmable delay block (PDB).
Dis... | Python Code:
import pandas as pd
def get_pdb_divide_params(frequency, F_BUS=int(48e6)):
mult_factor = np.array([1, 10, 20, 40])
prescaler = np.arange(8)
clock_divide = (pd.DataFrame([[i, m, p, m * (1 << p)]
for i, m in enumerate(mult_factor)
... |
10,242 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Quick Guide
Toytree is a Python tree plotting library designed for use inside
jupyter notebooks. In fact, this entire tutorial was created using notebooks, and assumes that you are followin... | Python Code:
import toytree # a tree plotting library
import toyplot # a general plotting library
import numpy as np # numerical library
print(toytree.__version__)
print(toyplot.__version__)
print(np.__version__)
Explanation: Quick Guide
Toytree is a Python tree plotting library designed for use inside
j... |
10,243 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<font color='teal'> Introduction to Neural Networks and Pytorch </font>
Notebook version
Step1: <font color='teal'> 1. Introduction and purpose of this Notebook </font>
<font color='teal'> ... | Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
size = 18
params = {'legend.fontsize': 'Large',
'axes.labelsize': size,
'axes.titlesize': size,
'xtick.labelsize': size*0.75,
'ytick.labelsize': size*0.75}
plt.rcParams.update(params)
Explanation: ... |
10,244 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Backpropagation
Step1: Variables & Terminology
$W_{i}$ - weights of the $i$th layer
$B_{i}$ - biases of the $i$th layer
$L_{a}^{i}$ - activation (Inner product of weights and inputs of prev... | Python Code:
from IPython.display import Image
Image("mlp.png", height=200, width=600)
Explanation: Backpropagation
End of explanation
from IPython.display import YouTubeVideo
YouTubeVideo("LOc_y67AzCA")
import numpy as np
from utils import backprop_decision_boundary, backprop_make_classification, backprop_make_moons
f... |
10,245 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Target Function
Lets create a target 1-D function with multiple local maxima to test and visualize how the BayesianOptimization package works. The target function we will try to maximize is ... | Python Code:
def target(x):
return np.exp(-(x - 2)**2) + np.exp(-(x - 6)**2/10) + 1/ (x**2 + 1)
x = np.linspace(-2, 10, 1000)
y = target(x)
plt.plot(x, y)
Explanation: Target Function
Lets create a target 1-D function with multiple local maxima to test and visualize how the BayesianOptimization package works. The t... |
10,246 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
verify pyEMU Influence class
Step1: instaniate pyemu object and drop prior info. Then reorder the jacobian and save as binary. This is needed because the pest utilities require strict ord... | Python Code:
%matplotlib inline
import os
import shutil
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pyemu
Explanation: verify pyEMU Influence class
End of explanation
pst = pyemu.Pst("freyberg.pst")
pst.pestpp_options = {}
inf = pyemu.Influence(jco="freyberg.jcb",pst=pst,verbose=False)... |
10,247 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Kernel Approximations for Large-Scale Non-Linear Learning
Predictions in a kernel-SVM are made using the formular
$$
\hat{y} = \alpha_1 y_1 k(\mathbf{x^{(1)}}, \mathbf{x}) + ... + \alpha_n y... | Python Code:
from helpers import Timer
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
digits = load_digits()
X, y = digits.data / 16., digits.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
Explanation: Kernel Approximations for Large-Scal... |
10,248 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Plotting workbook
Plotting workbook to accompany the paper Carbon Capture and Storage Energy Systems vs. Dispatchable Renewables for Climate Mitigation
Step1: Eq. 6
Define EROI-CCS function... | Python Code:
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib.font_manager as font_manager
plt.style.use('seaborn-darkgrid')
%matplotlib inline
prop = font_manager.FontProperties('Segoe UI')
from sympy import *
init_printing()
Explanation: Plotting workbook
Plotting workbook to accompany the ... |
10,249 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Decorators
Decorators can be thought of as functions which modify the functionality of another function. They help to make your code shorter and more "Pythonic".
To properly explain decorat... | Python Code:
def func():
return 1
func()
Explanation: Decorators
Decorators can be thought of as functions which modify the functionality of another function. They help to make your code shorter and more "Pythonic".
To properly explain decorators we will slowly build up from functions. Make sure to restart the Pyt... |
10,250 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
TensorFlow Dataset API
Learning Objectives
1. Learn how use tf.data to read data from memory
1. Learn how to use tf.data in a training loop
1. Learn how use tf.data to read data from disk
1.... | Python Code:
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.0 || pip install tensorflow==2.0
import json
import math
import os
from pprint import pprint
import numpy as np
import tensorflow as tf
print(tf.version.VERSION)
Explanation: TensorFlow Dataset API
Learning Objectives
1... |
10,251 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Generative Adversarial Network
In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten d... | Python Code:
%matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
Explanation: Generative Adversarial Network
In this notebook, we'll be building a gen... |
10,252 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Linear models for regression
y_pred = x_test[0] * coef_[0] + ... + x_test[n_features-1] * coef_[n_features-1] + intercept_
Step1: Linear Regression
Step2: Ridge Regression (L2 penalty)
Ste... | Python Code:
from sklearn.datasets import make_regression
from sklearn.cross_validation import train_test_split
X, y, true_coefficient = make_regression(n_samples=80, n_features=30, n_informative=10, noise=100, coef=True, random_state=5)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=5)
print(X_... |
10,253 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Radio Frequency Interference mitigation using deep convolutional neural networks
This example demonstrates how tf_unet is trained on the 'Bleien Galactic Survey data'.
To create the training... | Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import glob
plt.rcParams['image.cmap'] = 'gist_earth'
Explanation: Radio Frequency Interference mitigation using deep convolutional neural networks
This example demonstrates how tf_unet is trained on the 'Bleien Galacti... |
10,254 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Le but de cet exemple est de calculer, pour chaque décile de revenu, la part de leur consommation que les ménages accordent à chaque catégorie de bien. Les catégories suivent le niveau le pl... | Python Code:
from __future__ import division
import pandas
import seaborn
Explanation: Le but de cet exemple est de calculer, pour chaque décile de revenu, la part de leur consommation que les ménages accordent à chaque catégorie de bien. Les catégories suivent le niveau le plus agrégé de la nomenclature COICOP.
Import... |
10,255 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Update TOC trends analysis
Tore has previously written code to calculate Mann-Kendall (M-K) trend statistics and Sen's slope estimates for data series in RESA2. According to my notes from a ... | Python Code:
# Read data and results from the Excel macro
in_xlsx = (r'C:\Data\James_Work\Staff\Heleen_d_W\ICP_Waters\TOC_Trends_Analysis_2015'
r'\Data\mk_sen_test_data.xlsx')
raw_df = pd.read_excel(in_xlsx, sheetname='input')
res_df = pd.read_excel(in_xlsx, sheetname='results')
raw_df
res_df
Explanation: Up... |
10,256 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Gather summary statistics from network outputs
This example script displays the use of emu to
estimate normal distribution parameters from the output of each convolutional layer in a given p... | Python Code:
import sys
import os
import numpy as np
from collections import OrderedDict
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Gather summary statistics from network outputs
This example script displays the use of emu to
estimate normal distribution parameters from the output of each convoluti... |
10,257 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Copyright 2019 The TensorFlow Authors.
Step1: Text classification with TensorFlow Lite Model Maker
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="http... | Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# dist... |
10,258 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Implementing logistic regression from scratch
The goal of this notebook is to implement your own logistic regression classifier. We will
Step1: Load review dataset
For this assignment, we w... | Python Code:
# Run some setup code for this notebook.
import sys
import os
sys.path.append('..')
import graphlab
Explanation: Implementing logistic regression from scratch
The goal of this notebook is to implement your own logistic regression classifier. We will:
Extract features from Amazon product reviews.
Convert an... |
10,259 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Brainstorm auditory tutorial dataset
Here we compute the evoked from raw for the auditory Brainstorm
tutorial dataset. For comparison, see [1]_ and
Step1: To reduce memory consumption and r... | 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... |
10,260 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Simulation of a Noddy history and analysis of its voxel topology
Example of how the module can be used to run Noddy simulations and analyse the output.
Step1: Compute the model
The simplest... | Python Code:
from IPython.core.display import HTML
css_file = 'pynoddy.css'
HTML(open(css_file, "r").read())
# Basic settings
import sys, os
import subprocess
# Now import pynoddy
import pynoddy
%matplotlib inline
# determine path of repository to set paths corretly below
repo_path = os.path.realpath('../..')
Explanati... |
10,261 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1> Scaling up ML using Cloud ML Engine </h1>
In this notebook, we take a previously developed TensorFlow model to predict taxifare rides and package it up so that it can be run in Cloud ML... | Python Code:
import os
PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID
REGION = 'us-central1' # Choose an available region for Cloud MLE from https://cloud.google.com/ml-engine/docs/regions.
BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME. Use a regional bucket in the region you selec... |
10,262 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Discrete probability distributions
Rigorous definitions of discrete probability laws and discrete random variables are provided in part 00. By reading this part or from your own education, y... | Python Code:
N = 6
xk = np.arange(1,N+1)
fig, ax = plt.subplots(1, 1)
ax.plot(xk, sps.randint.pmf(xk, xk[0], 1+xk[-1]), 'ro', ms=12, mec='r')
ax.vlines(xk, 0, sps.randint.pmf(xk, xk[0], 1+xk[-1]), colors='r', lw=4)
plt.show()
Explanation: Discrete probability distributions
Rigorous definitions of discrete probability l... |
10,263 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
bqplot
This notebook is meant to guide you through the first stages of using the bqplot visualization library. bqplot is a Grammar of Graphics based interactive visualization library for the... | Python Code:
# Let's begin by importing some libraries we'll need
import numpy as np
# And creating some random data
size = 100
np.random.seed(0)
x_data = np.arange(size)
y_data = np.cumsum(np.random.randn(size) * 100.0)
Explanation: bqplot
This notebook is meant to guide you through the first stages of using the bqplo... |
10,264 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Gaussian Processes in Shogun
By Heiko Strathmann - <a href="mailto
Step1: Some Formal Background (Skip if you just want code examples)
This notebook is about Bayesian regression models with... | Python Code:
%matplotlib inline
# import all shogun classes
from modshogun import *
import random
import numpy as np
import matplotlib.pyplot as plt
from math import exp
Explanation: Gaussian Processes in Shogun
By Heiko Strathmann - <a href="mailto:heiko.strathmann@gmail.com">heiko.strathmann@gmail.com</a> - <a href="... |
10,265 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Mining Ulysses
Step1: A better method in Python 3 is -
Step2: These are the words that appear more than 200 times and I have excluded the really common words (greater than 944 times) | Python Code:
s
clean_s = removeDelimiter(s," ",[".",",",";","_","-",":","!","?","\"",")","("])
wordlist = clean_s.split()
dictionary = {}
for word in wordlist:
if word in dictionary:
tmp = dictionary[word]
dictionary[word]=tmp+1
else:
dictionary[word]=1
import operator
sorted_dict = sorted(dictionary.items(), k... |
10,266 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Scratch dans un notebook
Il existe une version javascript de Scratch
Step1: Si le résultat est vide, cela signifie que les fichiers ont déjà été copiés. On veut obtenir ceci
Step4: On ex... | Python Code:
import code_beatrix.jsscripts.snap
%load_ext code_beatrix
import os, glob
js_path = os.path.dirname(code_beatrix.jsscripts.snap.__file__)
files = [ os.path.split(_)[-1] for _ in glob.glob(js_path + "/*.js") ]
print(",".join(files))
path = "/static/snap/"
js_libs = [path + _ for _ in files ]
import notebook... |
10,267 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras
時系列データの予測
Step1: データセットの作成
Step2: 活性化関数にsigmoidやtanhを使うときは入力のスケールに大きな影響をうける
入力は[0, 1]に正規化するとよい
scikit-learnの... | Python Code:
%matplotlib inline
import pandas
import matplotlib.pyplot as plt
dataset = pandas.read_csv('data/international-airline-passengers.csv',
usecols=[1], engine='python', skipfooter=3)
plt.plot(dataset)
plt.show()
dataset
Explanation: Time Series Prediction with LSTM Recurrent Neural N... |
10,268 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Predicting Marginal Generating Units in ERCOT
Project Goals and Model Choices
The goal of this project is to build a model that can predict the type of fossil marginal generating units (MGU)... | Python Code:
from IPython.display import SVG
SVG('https://www.dropbox.com/s/k8ac0la03hkjo5f/ERCOT%20power%20plants%202007.svg?raw=1')
Explanation: Predicting Marginal Generating Units in ERCOT
Project Goals and Model Choices
The goal of this project is to build a model that can predict the type of fossil marginal gener... |
10,269 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Basics
Step1: Let's create a simple regular 10x10 degree grid with grid points at the center of each 10x10 degree cell.
First by hand to understand what is going on underneath
Step2: These... | Python Code:
import pygeogrids.grids as grids
import numpy as np
Explanation: Basics
End of explanation
# create the longitudes
lons = np.arange(-180 + 5, 180, 10)
print(lons)
lats = np.arange(90 - 5, -90, -10)
print(lats)
Explanation: Let's create a simple regular 10x10 degree grid with grid points at the center of ea... |
10,270 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Compare Solutions - Homogenous 3D
Brendan Smithyman | November 2015
This notebook shows comparisons between the responses of the different solvers.
Step1: Error plots for MiniZephyr vs. the... | Python Code:
import sys
sys.path.append('../')
import numpy as np
from zephyr.backend import MiniZephyr25D, SparseKaiserSource, AnalyticalHelmholtz
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib
%matplotlib inline
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('p... |
10,271 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
MixUp and Friends
Callbacks that can apply the MixUp (and variants) data augmentation to your training
Step1: Most Mix variants will perform the data augmentation on the batch, so to implem... | Python Code:
from fastai.vision.all import *
#|export
def reduce_loss(
loss:Tensor,
reduction:str='mean' # PyTorch loss reduction
)->Tensor:
"Reduce the loss based on `reduction`"
return loss.mean() if reduction == 'mean' else loss.sum() if reduction == 'sum' else loss
#|export
class MixHandler(Callbac... |
10,272 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Annonce
La branche étudiante de l'IEEE propose, ce jeudi 25 février de 13 h 30 à 17 h 30, une formation à Mathematica pour donner les bases de ce logiciel. L'inscription est nécessaire.
http... | Python Code:
from __future__ import division
Explanation: Annonce
La branche étudiante de l'IEEE propose, ce jeudi 25 février de 13 h 30 à 17 h 30, une formation à Mathematica pour donner les bases de ce logiciel. L'inscription est nécessaire.
http://ieee.aees.be/fr/accueil/25-francais/activites/conferences/151-formati... |
10,273 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Pandas Data Munging
Step1: ...and you want to filter it on some criteria. Pandas makes that easy with Boolean Indexing
Step2: This works great right? Unfortunately not, because once we
Ste... | Python Code:
import pandas as pd
df = pd.DataFrame({'Number' : [100,200,300,400,500], 'Letter' : ['a','b','c', 'd', 'e']})
df
Explanation: Pandas Data Munging: Avoiding that 'SettingWithCopyWarning'
If you use Python for data analysis, you probably use Pandas for Data Munging. And if you use Pandas, you've probably com... |
10,274 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Demo 1 - Single forward problem [local]
Brendan Smithyman | bsmithym@uwo.ca | March, 2015
Import NumPy
Step1: Import plotting tools from matplotlib and set format defaults
Step2: Base syst... | Python Code:
import numpy as np
Explanation: Demo 1 - Single forward problem [local]
Brendan Smithyman | bsmithym@uwo.ca | March, 2015
Import NumPy
End of explanation
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib
%matplotlib inline
from IPython.display import set_matplotlib_formats
set_ma... |
10,275 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Nested Statements and Scope
Now that we have gone over on writing our own functions, its important to understand how Python deals with the variable names you assign. When you create a variab... | Python Code:
x = 25
def printer():
x = 50
return x
print (x)
print (printer())
Explanation: Nested Statements and Scope
Now that we have gone over on writing our own functions, its important to understand how Python deals with the variable names you assign. When you create a variable name in Python the name is ... |
10,276 | 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_noing10_200_512_04drb/encdec_noing10_200_512_04drb.json'
log_file = '/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing10_200_512_04drb/encdec_noing10_200_512_04drb_logs.json'
import json
import matp... |
10,277 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Introduction
Now, you'll use what you learned in the previous tutorial to improve the efficiency of several queries.
Before you get started, run the following cell to set everything up.
Step... | Python Code:
# Set up feedback system
from learntools.core import binder
binder.bind(globals())
from learntools.sql_advanced.ex4 import *
print("Setup Complete")
Explanation: Introduction
Now, you'll use what you learned in the previous tutorial to improve the efficiency of several queries.
Before you get started, run ... |
10,278 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Table of Contents
<p><div class="lev1 toc-item"><a href="#Measure-Dynamic-Functional-Connectivity" data-toc-modified-id="Measure-Dynamic-Functional-Connectivity-1"><span class="toc-item-num"... | Python Code:
try:
%load_ext autoreload
%autoreload 2
%reset
except:
print 'NOT IPYTHON'
from __future__ import division
import os
import sys
import glob
import numpy as np
import pandas as pd
import seaborn as sns
import scipy.stats as stats
import statsmodels.api as sm
import scipy.io as io
import h5py... |
10,279 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
draw a pairplot using python for tips dataset and for column sex in the given dataset
| Python Code::
sns.pairplot(tips , hue = ''sex', palette = 'coolwarm')
|
10,280 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Benchmarking of various implementations of FADDEEVA's error functions
I. Setup
Import the multiprecision library mpmath as a reference for accuracy benchmarks
Step1: Import the rest of the ... | Python Code:
import mpmath
Explanation: Benchmarking of various implementations of FADDEEVA's error functions
I. Setup
Import the multiprecision library mpmath as a reference for accuracy benchmarks:
End of explanation
import numpy as np
import scipy
import ctypes
import sys
Explanation: Import the rest of the usual lo... |
10,281 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Read OpendTect horizons
The best way to export horizons from OpendTect is with these options
Step1: IL/XL and XY, multi-line header, multiple attributes
Load everything (default)
X and Y ar... | Python Code:
import gio
ds = gio.read_odt('../../data/OdT/3d_horizon/Segment_ILXL_Single-line-header.dat')
ds
ds['twt'].plot()
Explanation: Read OpendTect horizons
The best way to export horizons from OpendTect is with these options:
x/y and inline/crossline
with header (single or multi-line, it doesn't matter)
choose ... |
10,282 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Exploracion y Visualizacion de Bases de Datos
Para el siguiente ejemplo tomaremos como referencia el archivo snie1213.csv que se encuentra en la carpeta data, esta base de datos contiene los... | Python Code:
# librerias
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.formula.api as sm
import seaborn as sns
%matplotlib inline
plt.style.use('ggplot')
# leer archivo
data = pd.read_csv('../data/snie1213.csv', low_memory=False)
# verificar su contenido
data.head()
data.info... |
10,283 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Raspberry Pi Programming
GPIO digital output
Following example sets PIN18 to High(3.3V).
Python's import statement includes external library
GPIO.setmode function defines which numbering met... | Python Code:
import RPi.GPIO as GPIO
GPIO.setmode(GPIO.BCM) # use GPIO numbering, see output of gpio command
#GPIO.setmode(GPIO.BOARD) # use Physical pin numbering
GPIO.setup(18, GPIO.OUT) # PIN18 (Physical:Pin12) : Output
GPIO.output(18, GPIO.HIGH) # Ping18 -> High (3.3V)
Explanation: Raspberry Pi Programming
GPIO di... |
10,284 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
<h1 align = 'center'> Neural Networks Demystified </h1>
<h2 align = 'center'> Part 6
Step1: So far we’ve built a neural network in python, computed a cost function to let us know how well o... | Python Code:
from IPython.display import YouTubeVideo
YouTubeVideo('9KM9Td6RVgQ')
Explanation: <h1 align = 'center'> Neural Networks Demystified </h1>
<h2 align = 'center'> Part 6: Training </h2>
<h4 align = 'center' > @stephencwelch </h4>
End of explanation
%pylab inline
#Import code from previous videos:
from partFiv... |
10,285 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Methods for Sampling from the Unit Simplex
1. Uniform Distributions on the Unit Simplex
A $\textit{n}$-unit simplex (https
Step2: 2. Exponential Distributions on the Unit Simplex
The expone... | Python Code:
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
import ternary
## generate order statistics from uniform distribution between 0 and 1
U = [[np.random.uniform(), np.random.uniform()] for i in range(50000)]
for u in U:
u.sort()
## calculate the spacing and plot the sampling result... |
10,286 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This paper presents a novel set of stimuli, which we refer to as "logpolar gratings" or "scaled gratings". These stimuli are sinusoidal gratings whose spatial frequency decreases as the reci... | Python Code:
# import necessary packages
%matplotlib inline
%load_ext autoreload
%autoreload 2
import sys
sys.path.append('..')
import sfp
import pandas as pd
import torch
import numpy as np
import matplotlib.pyplot as plt
from tqdm.auto import tqdm
import seaborn as sns
import pyrtools as pt
Explanation: This paper p... |
10,287 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
SVM classification/SMOTE oversampling for an imbalanced data set
Date created
Step1: <h3>II. Preprocessing </h3>
We process the missing values first, dropping columns which have a large num... | Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split as tts
from sklearn.ensemble import RandomForestCl... |
10,288 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
LVA Tables (Storage Geometry)
We can retrieve the geometry of a given storage.
We can also set the geometry of a storage (or set multiple storages to the same geometry)
Step1: Specifying fu... | Python Code:
lva = v.model.node.storages.lva('IrrigationOnlyStorage')
lva
scaled_lva = lva * 2
scaled_lva
# v.model.node.storages.load_lva(scaled_lva) # Would load the same table into ALL storages
# v.model.node.storages.load_lva(scaled_lva,nodes=['StorageOnlyStorage','BothStorage']) # Will load into two storages
v... |
10,289 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Regression Week 5
Step1: Load in house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
Step2: Create new features
As in Week 2, ... | Python Code:
import sys
sys.path.append('C:\Anaconda2\envs\dato-env\Lib\site-packages')
import graphlab
Explanation: Regression Week 5: Feature Selection and LASSO (Interpretation)
In this notebook, you will use LASSO to select features, building on a pre-implemented solver for LASSO (using GraphLab Create, though you ... |
10,290 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
ES-DOC CMIP6 Model Properties - Seaice
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributors
Step3: Document Publication
Specify ... | Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cams', 'cams-csm1-0', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: CAMS
Source ID: CAMS-CSM1-0
Topic: Seaice
Sub-Topics: Dynamics, Thermodyn... |
10,291 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
CIFAR–10 Codealong
Codealong of Radek Osmulski's notebook establishing a CIFAR-10 baseline with the Fastai ImageNet WideResNet22. For a Fastai CV research collaboration.
Wayne Nixalo –– 2018... | Python Code:
%matplotlib inline
%reload_ext autoreload
%autoreload 2
from fastai.conv_learner import *
# fastai/imagenet-fast/cifar10/models/ repo
from imagenet_fast_cifar_models.wideresnet import wrn_22
from torchvision import transforms, datasets
# allows you to enable the inbuilt cudnn auto-tuner to find the
# best... |
10,292 |
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
Problem:
Shap Summary Plots
| Python Code::
import shap
shap.initjs()
shap_values = shap.TreeExplainer(model).shap_values(X_train)
shap.summary_plot(shap_values, X_train, plot_type="bar")
shap.summary_plot(shap_values, X_train)
|
10,293 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
This is duck typing, and it is everywhere although you did not notice it
Duck typing
Step1: EAFP | Python Code:
# Compare
def let_duck_swim_and_quack(d):
if hasattr(d, "swim") and hasattr(d, "quack"):
d.swim()
d.quack()
else:
print "It does not look like a duck"
raise AttributeError
def let_duck_swim_and_quack(d):
try:
d.swim()
d.quack()
except Attribut... |
10,294 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Configure the plotting machinery
Step1: Configure the rendering of this notebook with CSS
Step2: Response Analysis in the Frequency Domain<br> <small>an Example</small>
Samples
Step3: We ... | Python Code:
%pylab inline
%config InlineBackend.figure_format = 'svg'
import json
s = json.load( open("mplrc.json") )
matplotlib.rcParams.update(s)
matplotlib.rcParams['figure.figsize'] = 9,4
black="#404060" # plots containing "real black" elements look artificial
Explanation: Configure the plotting machinery
End of e... |
10,295 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Математическая статистика
Практическое задание 5
В данном задании предлагается провести некоторое исследование модели линейной регрессии и критериев для проверки статистических гипотез, в ча... | Python Code:
import numpy as np
import scipy.stats as sps
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
Explanation: Математическая статистика
Практическое задание 5
В данном задании предлагается провести некоторое исследование модели линейной регрессии и критериев для проверки статистических г... |
10,296 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Shots are fired isotropically from a point and hit a position sensitive detector
There is no scattering
y is fixed to be 1 away
Step1: if both are unknown
Step2: Now repeat all this updati... | Python Code:
# generate some data
with pm.Model() as model:
x = pm.Cauchy(name='x', alpha=0, beta=1)
trace = pm.sample(10000, njobs=4)
pm.traceplot(trace)
sampledat = trace['x']
trace.varnames, trace['x']
sns.distplot(sampledat, kde=False, norm_hist=True)
# plt.hist(sampledat, 200, normed=True);
plt.yscale(... |
10,297 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
Embedding a Feedforward Cascade in a Recurrent Network
Alex Williams 10/24/2015
If you are viewing a static version of this notebook (e.g. on nbviewer), you can launch an interactive session... | Python Code:
from __future__ import division
from scipy.integrate import odeint,ode
from numpy import zeros,ones,eye,tanh,dot,outer,sqrt,linspace,pi,exp,tile,arange,reshape
from numpy.random import uniform,normal,choice
import pylab as plt
import numpy as np
%matplotlib inline
Explanation: Embedding a Feedforward Casca... |
10,298 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
A Neural Network using Numpy on Bike Sharing Time Series dataset
In this project, we'll build a neural network and use it to predict daily bike rental ridership.
Step1: Load and prepare the... | Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Explanation: A Neural Network using Numpy on Bike Sharing Time Series dataset
In this project, we'll build a neural network and use it to predict daily bike rental riders... |
10,299 | Given the following text description, write Python code to implement the functionality described below step by step
Description:
练习 1：写程序，可由键盘读入用户姓名例如Mr. right，让用户输入出生的月份与日期，判断用户星座，假设用户是金牛座，则输出，Mr. right，你是非常有性格的金牛座！
Step1: 练习 2：写程序，可由键盘读入两个整数m与n(n不等于0)，询问用户意图，如果要求和则计算从m到n的和输出，如果要乘积则计算从m到n的积并输出，如果要求余数则计算m除以n的余数的值并输出，... | Python Code:
name = input("请输入您的姓名：")
date = float(input("请输入您出生的月份.日期："))
if 3.21 <= date <= 4.19:
print(name,"，你是非常有性格的白羊座！")
elif 4.20 <= date <= 5.20:
print(name,"，你是非常有性格的金牛座！")
elif 5.21 <= date <= 6.21:
print(name,"，你是非常有性格的双子座！")
elif 6.22 <= date <= 7.22:
print(name,"，你是非常有性格的巨蟹座！")
elif 7.23 <... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.