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100 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Http Response
Usage
To construct a response packet you have a variety of facilities available.
Previously we saw how to parse HTTP responses using Http... | <ASSISTANT_TASK:>
Python Code:
from proxy.http.parser import HttpParser, httpParserTypes
from proxy.common.constants import HTTP_1_1
response = HttpParser(httpParserTypes.RESPONSE_PARSER)
response.code = b'200'
response.reason = b'OK'
response.version = HTTP_1_1
print(response.build_response())
Explanation: Http Respon... |
101 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Dictionaries
A dictionary is datatype that contains a series of key-value pairs. It is similar to a list except for that the indices of the values can ... | <ASSISTANT_TASK:>
Python Code:
fruit_season = {
'raspberry': 'May',
'apple' : 'September',
'peach' : 'July',
'grape' : 'August'
}
print(type(fruit_season))
print(fruit_season)
Explanation: Dictionaries
A dictionary is datatype that contains a series of key-value pairs. It is similar to a list ... |
102 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Drive LEDs with the Raspberry Pi GPIO pins
This notebook will walk you through using the Raspberry Pi General Purpose Input/Output (GPIO) pins to make ... | <ASSISTANT_TASK:>
Python Code:
#load GPIO library
import RPi.GPIO as GPIO
#Set BCM (Broadcom) mode for the pin numbering
GPIO.setmode(GPIO.BCM)
Explanation: Drive LEDs with the Raspberry Pi GPIO pins
This notebook will walk you through using the Raspberry Pi General Purpose Input/Output (GPIO) pins to make a LED light ... |
103 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
On-the-fly aluminum potential
For the scheme/workflow of the on-the-fly training, please refer to Vandermause et al..
In our FLARE python package, we ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from flare.gp import GaussianProcess
# make gp model
hyps = np.array([0.1, 1, 0.01])
hyp_labels = ['Signal Std', 'Length Scale', 'Noise Std']
cutoffs = {'threebody':3.9}
gp = \
GaussianProcess(kernels=['threebody'],
hyps=hyps,
... |
104 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Fully-Connected Neural Nets
In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation ... | <ASSISTANT_TASK:>
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_... |
105 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Deploying and Making Predictions with a Trained Model
Learning Objectives
- Deploy a model on Google CMLE
- Make online and batch predictions with a de... | <ASSISTANT_TASK:>
Python Code:
PROJECT = "cloud-training-demos" # Replace with your PROJECT
BUCKET = "cloud-training-bucket" # Replace with your BUCKET
REGION = "us-central1" # Choose an available region for Cloud MLE
TFVERSION = "1.14" # TF version for CMLE to use
import os
os.environ["BUCK... |
106 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Visualize Glass Brain
Step1: 1. Upload all statistical maps into the data folder
The data folder can be found in the same folder as this notebook. Jus... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
Explanation: Visualize Glass Brain
End of explanation
stats_file = '../test_data/ALL_N95_Mean_cope2_thresh_zstat1.nii.gz'
view = 'ortho'
colormap = 'RdBu_r'
threshold = '2.3'
black_bg
Explanation: 1. Upload all statistical maps into the data folder
The data folder can ... |
107 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Synchronous RVI (sRVI) on the 3-loop problem
Step1: For $\alpha=1$, sRVI does not converge on the (periodic) 3-loop problem.
Synchronous DVI (sDVI) on... | <ASSISTANT_TASK:>
Python Code:
alphas = [1.0, 0.999, 0.99, 0.9, 0.7, 0.5, 0.3, 0.1, 0.01, 0.001]
max_iters = 50000
epsilon = 0.001
init_v = np.zeros(env.num_states())
init_r_bar_scalar = 0
convergence_flags = np.zeros(alphas.__len__())
for i, alpha in enumerate(alphas):
alg = RVI_Evaluation(env, init_v, alpha, ref_... |
108 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Transformée de Fourier
Step1: TODO
Step2: \begin{eqnarray}
a_0 & = & \frac{1}{\pi} \int^{\pi}{-\pi} (\cos(t) + 2 \sin(t) - 3 \sin(2t) + 4) \cos(0... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib
matplotlib.rcParams['figure.figsize'] = (6, 6)
import math
import cmath # math functions for complex numbers
import numpy as np
import matplotlib.pyplot as plt
import ipywidgets
from ipywidgets import interact
import sympy as sp
# See: http://... |
109 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Create Some Fake Entires That demonstrates Plotting
In the constructor, give a title, number of total datasets that you want to plot side by side, a li... | <ASSISTANT_TASK:>
Python Code:
plot = ChristmasPlot('Fake', n_dataset=3, methods=['yass', 'kilosort', 'spyking circus'], logit_y=True, eval_type="Accuracy")
for method in plot.methods:
for i in range(plot.n_dataset):
x = (np.random.rand(30) - 0.5) * 10
y = 1 / (1 + np.exp(-x + np.random.rand()))
... |
110 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Introduction to Tethne
Step1: Once again, read() accepts a string containing a path to either a single DfR dataset, or a directory containing several.... | <ASSISTANT_TASK:>
Python Code:
from tethne.readers import dfr
Explanation: Introduction to Tethne: Working with data from the Web of Science
Now that we have the basics down, in this notebook we'll begin working with data from the JSTOR Data-for-Research (DfR) portal.
The JSTOR DfR portal gives researchers access to
bi... |
111 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Title
Step1: Load Iris Flower Dataset
Step2: Standardize Features
Step3: Create Logistic Regression
Step4: Train Logistic Regression
Step5: Create... | <ASSISTANT_TASK:>
Python Code:
# Load libraries
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
Explanation: Title: Logistic Regression
Slug: logistic_regression
Summary: How to train a logistic regression in scikit-learn.
Date: 2017-09-2... |
112 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
CitiBike Ridership Trends - MBA Data project 2017
Sarah Love
Tefiro Kituuka Serunjogi
This project endeavors to understand usership trends amongst Citi... | <ASSISTANT_TASK:>
Python Code:
import sys # system module
import pandas as pd # data package
import matplotlib as mpl # graphics package
import matplotlib.pyplot as plt # graphics module
import datetime as dt # date and time module
... |
113 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Network analysis of the data.
The goal of this notebook is to uncover several constructions inside the dataset which may help us to uncover fraud. With... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
Explanation: Network analysis of the data.
The goal of this notebook is to uncover several constructions inside the dataset which may help us to uncover fraud. With that, we can... |
114 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Deep Learning
Assignment 2
Previously in 1_notmnist.ipynb, we created a pickle with formatted datasets for training, development and testing on the not... | <ASSISTANT_TASK:>
Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import numpy as np
import tensorflow as tf
from six.moves import cPickle as pickle
from six.moves import range
Explanation: Deep Learning
Assi... |
115 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Train RNN
Here we give a complete example of what using Equinox normally looks like day-to-day.
In this example we'll train an RNN to classify clockwis... | <ASSISTANT_TASK:>
Python Code:
import math
import jax
import jax.lax as lax
import jax.numpy as jnp
import jax.random as jrandom
import optax # https://github.com/deepmind/optax
import equinox as eqx
Explanation: Train RNN
Here we give a complete example of what using Equinox normally looks like day-to-day.
In this ex... |
116 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Pesquisar por Tweets
Na API Rest também podemos utilizar o método search para procurar por tweets que combinam com o termo definido.
O método contém al... | <ASSISTANT_TASK:>
Python Code:
import tweepy
consumer_key = ''
consumer_secret = ''
access_token = ''
access_token_secret = ''
autorizar = tweepy.OAuthHandler(consumer_key, consumer_secret)
autorizar.set_access_token(access_token, access_token_secret)
api = tweepy.API(autorizar)
print(api)
Explanation: Pesquisar por Tw... |
117 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Build a DNN using the Keras Functional API
Learning objectives
Review how to read in CSV file data using tf.data.
Specify input, hidden, and output lay... | <ASSISTANT_TASK:>
Python Code:
# You can use any Python source file as a module by executing an import statement in some other Python source file
# The import statement combines two operations; it searches for the named module, then it binds the
# results of that search to a name in the local scope.
import os, json, ma... |
118 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
How to Build a RuleBasedProfiler
This Notebook will demonstrate the steps we need to take to generate a simple RuleBasedProfiler by initializing the co... | <ASSISTANT_TASK:>
Python Code:
import great_expectations as ge
from ruamel import yaml
from great_expectations.core.batch import BatchRequest
from great_expectations.rule_based_profiler.rule.rule import Rule
from great_expectations.rule_based_profiler.rule_based_profiler import RuleBasedProfiler, RuleBasedProfilerResul... |
119 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Level 1
In diesem Level werden wir unsere ersten Zeilen Python kennenlernen. Wir werden lernen, was ein integer, ein float, ein string ist, wie wir mit... | <ASSISTANT_TASK:>
Python Code:
# Für die Standardausgabe benutzen wir die print() Funktion
print("Hallo Welt!")
# Wir können mit Kommata getrennt auch mehrere Werte ausgeben:
print("foo", "bar")
# Mit der help() Funktionen zeigen wir uns
# die Hilfe der print() Funktion an:
help(print)
# Ausgabe mit Seperatoren:
print(... |
120 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
This notebook demonstrates the LeNet model.
First we load some dependencies for our code.
Step1: Now we can start to define the actual convolution cod... | <ASSISTANT_TASK:>
Python Code:
import numpy
import theano
import theano.tensor as T
from logistic_sgd import LogisticRegression
from mlp import HiddenLayer
Explanation: This notebook demonstrates the LeNet model.
First we load some dependencies for our code.
End of explanation
from theano.tensor.signal import downsampl... |
121 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Bayesian MLP for MNIST using preconditioned SGLD
We use the Jax Bayes library
by James Vuckovic
to fit an MLP to MNIST using SGD, and SGLD (with RMS... | <ASSISTANT_TASK:>
Python Code:
%%capture
!pip install git+https://github.com/deepmind/dm-haiku
!pip install git+https://github.com/jamesvuc/jax-bayes
import haiku as hk
import jax.numpy as jnp
from jax.experimental import optimizers
import jax
import jax_bayes
import sys, os, math, time
import numpy as onp
import numpy... |
122 | <SYSTEM_TASK:>
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
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Problem:
I have a csv file which looks like | <ASSISTANT_TASK:>
Python Code:
from sklearn.cluster import KMeans
df = load_data()
kmeans = KMeans(n_clusters=2)
labels = kmeans.fit_predict(df[['mse']])
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123 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Control Flow
Step1: NOTE on notation
* _x, _y, _z, ...
Step2: Q5. Given x, return the truth value of NOT x element-wise. | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import tensorflow as tf
import numpy as np
from datetime import date
date.today()
author = "kyubyong. https://github.com/Kyubyong/tensorflow-exercises"
tf.__version__
np.__version__
sess = tf.InteractiveSession()
Explanation: Control Flow
End of expla... |
124 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Parameters and Example Data
Step1: True Changepoints
Step2: Estimated Changepoints with GFGL smoother
Step3: Visualising Graphical Models
To visuali... | <ASSISTANT_TASK:>
Python Code:
y = np.load('../data/y.npy')
sigma = np.load('../data/sigma.npy')
sigma_inv = np.load('../data/sigma_inv.npy')
T = 90 # Steps
K = 2 # Changepoints
P = 10 # Variables
M = 5 # Active Edges
eps = 0.000001 # Edge threshold epsilon
Explanation: Parameters and Example Data
End of explanation
ed... |
125 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
This notebook will serve to demonstrate Retina functionality within the Jupyter notebook as it relates to Matplotlib.
Retina, Matplotlib, and the Jupyt... | <ASSISTANT_TASK:>
Python Code:
import retina.core.axes
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
fig = plt.figure()
ax1 = plt.subplot('111', projection='Fovea2D')
Explanation: This notebook will serve to demonstrate Retina functionality within the Jupyter notebook as it relates to Matplotlib... |
126 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Generate a Cubic Lattice with an Interpenetrating Dual Cubic Lattice
(Since version 1.6) OpenPNM offers two options for generating dual networks. This... | <ASSISTANT_TASK:>
Python Code:
import scipy as sp
import openpnm as op
import matplotlib.pyplot as plt
%matplotlib inline
wrk = op.Workspace() # Initialize a workspace object
wrk.loglevel=50
Explanation: Generate a Cubic Lattice with an Interpenetrating Dual Cubic Lattice
(Since version 1.6) OpenPNM offers two options... |
127 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Para este exemplo, vamos usar o dataset Iris, que pode ser obtido aqui
Step1: Agora, vamos separar os dados entre as medidas e as espécies.
Step2: Ag... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import decomposition
from sklearn import datasets
tabela = pd.read_csv("exemplo_7/iris.data",header=None,sep=',')
tabela
tabela.columns=['sepal_len', 'sepal_wid', 'petal_len', 'petal_wid', 'class']
tabela
... |
128 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Varcode - Quick Start
Varcode is a library for working with genomic variant data in Python and predicting the impact of those variants on protein seque... | <ASSISTANT_TASK:>
Python Code:
from varcode import Variant
from pyensembl import ensembl_grch37
Explanation: Varcode - Quick Start
Varcode is a library for working with genomic variant data in Python and predicting the impact of those variants on protein sequences.
What this means is that this library can help you anno... |
129 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Tutorial "Algorithmic Methods for Network Analysis with NetworKit" (Part 4)
Determining Important Nodes (cont'd)
Betweenness Centrality
If you interpre... | <ASSISTANT_TASK:>
Python Code:
from networkit import *
%matplotlib inline
cd ~/workspace/NetworKit
G = readGraph("input/PGPgiantcompo.graph", Format.METIS)
# Code for 7-1)
# exact computation
# Code for 7-2)
# approximate computation
Explanation: Tutorial "Algorithmic Methods for Network Analysis with NetworKit" (Part ... |
130 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Advanced Pandas
Step1: <a id=want></a>
The want operator
We need to know what we're trying to do -- what we want the data to look like. To borrow a ph... | <ASSISTANT_TASK:>
Python Code:
import sys # system module
import pandas as pd # data package
import matplotlib.pyplot as plt # graphics module
import datetime as dt # date and time module
import numpy as np # foundation for pa... |
131 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<a href="https
Step1: DDSP Training
This notebook demonstrates the libraries in https
Step2: Get a batch of data
Step3: Get a distribution strategy
... | <ASSISTANT_TASK:>
Python Code:
# Copyright 2021 Google LLC. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless ... |
132 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<a href="http
Step1: Make a grid and set boundary conditions.
Step2: Here we make the initial grid of elevation of zeros with a very small amount of ... | <ASSISTANT_TASK:>
Python Code:
# Code block 1
import copy
import numpy as np
from matplotlib import pyplot as plt
from landlab import RasterModelGrid, imshow_grid
from landlab.components import (
ChannelProfiler,
ChiFinder,
FlowAccumulator,
SteepnessFinder,
StreamPowerEroder,
)
from landlab.io impor... |
133 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Copyright 2020 The TensorFlow Authors.
Step1: TF Lattice 집계 함수 모델
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="htt... | <ASSISTANT_TASK:>
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 writin... |
134 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Name
Data processing by creating a cluster in Cloud Dataproc
Label
Cloud Dataproc, cluster, GCP, Cloud Storage, KubeFlow, Pipeline
Summary
A Kubeflow P... | <ASSISTANT_TASK:>
Python Code:
%%capture --no-stderr
KFP_PACKAGE = 'https://storage.googleapis.com/ml-pipeline/release/0.1.14/kfp.tar.gz'
!pip3 install $KFP_PACKAGE --upgrade
Explanation: Name
Data processing by creating a cluster in Cloud Dataproc
Label
Cloud Dataproc, cluster, GCP, Cloud Storage, KubeFlow, Pipeline
S... |
135 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
ES-DOC CMIP6 Model Properties - Seaice
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contributor... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'sandbox-3', 'seaice')
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: MIROC
Source ID: SANDBOX-3
Topic: Seaice
Sub-Topics: Dyn... |
136 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Build a fraud detection model on Vertex AI
Step1: <table align="left">
<td>
<a href="https
Step2: Install the latest version of the Vertex AI c... | <ASSISTANT_TASK:>
Python Code:
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
137 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Gradient Checking
Welcome to the final assignment for this week! In this assignment you will learn to implement and use gradient checking.
You are par... | <ASSISTANT_TASK:>
Python Code:
# Packages
import numpy as np
from testCases import *
from gc_utils import sigmoid, relu, dictionary_to_vector, vector_to_dictionary, gradients_to_vector
Explanation: Gradient Checking
Welcome to the final assignment for this week! In this assignment you will learn to implement and use gr... |
138 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Copyright 2020 The TensorFlow Probability Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: TensorFlow Probability on JA... | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable l... |
139 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Introduction to BigQuery ML - Predict Birth Weight
Learning Objectives
Use BigQuery to explore the natality dataset
Create a regression (linear regress... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
Explanation: Introduction to BigQuery ML - Predict Birth Weight
Learning Objectives
Use BigQuery to explore the natality dataset
Create a regression (linear regression) model in BQML
Evaluate the performance of your machine learning model
Make predictions w... |
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Description:
SETUP
Step1: Autosipper
Step2: Manifold
Step3: Micromanager
Step4: Preset
Step5: ACQUISITION
Step6: MM Get info
Step7: Video
Step8: SNAP CV2
St... | <ASSISTANT_TASK:>
Python Code:
import time
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
Explanation: SETUP
End of explanation
# config directory must have "__init__.py" file
# from the 'config' directory, import the following classes:
from config import Motor, ASI_Controller... |
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Description:
These examples are tests for scc_info on alternating automata.
Step1: universal edges are handled as if they were many distinct existencial edges from... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import display
import spot
spot.setup(show_default='.bas')
spot.automaton('''
HOA: v1
States: 2
Start: 0&1
AP: 2 "a" "b"
acc-name: Buchi
Acceptance: 1 Inf(0)
--BODY--
State: 0
[0] 0
[!0] 1
State: 1
[1] 1 {0}
--END--
''')
Explanation: These examples are tests for scc_i... |
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Description:
Course 12-752
Step1: Section 2
Step2: Section 3
Step3: Display variable power
Step4: Use parser function from dateutil module to convert the timest... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime as dt
import scipy.spatial.distance as dist
%matplotlib inline
Explanation: Course 12-752: Term Project
Project Title: Comparison of the accuracy of linear regression model based on the granularity of d... |
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Description:
This notebook was prepared by Donne Martin. Source and license info is on GitHub.
Spark
IPython Notebook Setup
Python Shell
DataFrames
RDDs
Pair RDDs
R... | <ASSISTANT_TASK:>
Python Code:
!pyspark
Explanation: This notebook was prepared by Donne Martin. Source and license info is on GitHub.
Spark
IPython Notebook Setup
Python Shell
DataFrames
RDDs
Pair RDDs
Running Spark on a Cluster
Viewing the Spark Application UI
Working with Partitions
Caching RDDs
Checkpointing RDDs
W... |
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Description:
Random Variables
Frequently, when an experiment is performed, we are interested mainly in some function of the outcome as opposed to the actual outcome... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
from itertools import product
# from IPython.core.display import HTML
# css = open('media/style-table.css').read() + open('media/style-notebook.css').read()
# HTML('<style>{}</style>'.format(css))
one_toss = np.array(['H', 'T'])
tw... |
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Description:
DBSCAN Lab
在此 notebook 中，我们将使用 DBSCAN 来聚集几个数据集。我们将研究更改参数（ε 和 min_samples）会如何更改生成的聚类结构。
Step1: 下面是第一个数据集：
Step2: 我们使用 DBSCAN 的默认设置对其进行聚类，看看会发生什么。我们希望它... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
dataset_1 = pd.read_csv('blobs.csv')[:80].values
Explanation: DBSCAN Lab
在此 notebook 中，我们将使用 DBSCAN 来聚集几个数据集。我们将研究更改参数（ε 和 min_samples）会如何更改生成的聚类结构。
End of explanation
%matplotlib inline
import dbscan_lab_helper as helper
helper.plot_dataset(dataset_1)
Explanation... |
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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="I... | <ASSISTANT_TASK:>
Python Code:
debug_flag = False
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"><span class="toc-item-num">1 </span>Introduction</a></span></li><li><span><a hre... |
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Description:
Predicting house prices using k-nearest neighbors regression
In this notebook, you will implement k-nearest neighbors regression. You will
Step1: Load... | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('C:\Anaconda2\envs\dato-env\Lib\site-packages')
import graphlab
Explanation: Predicting house prices using k-nearest neighbors regression
In this notebook, you will implement k-nearest neighbors regression. You will:
* Find the k-nearest neighbors of a given q... |
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Description:
Release of hammer-cli gem
Requirements
push access to https
Step1: Update the following notebook settings
Step2: Ensure the repo is up to date
Step3:... | <ASSISTANT_TASK:>
Python Code:
%cd ..
Explanation: Release of hammer-cli gem
Requirements
push access to https://github.com/theforeman/hammer-cli
push access to rubygems.org for hammer-cli
sudo yum install transifex-client python-slugify asciidoc
ensure neither the git push or gem push don't require interractive auth. ... |
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Description:
Arrays for Numerical work?
Step2: You can see, adding two lists just results in a longer list, catenation of the two. | <ASSISTANT_TASK:>
Python Code:
a = [1,2,3]
b = [4,5,6]
c = a+b
print(c)
Explanation: Arrays for Numerical work?
End of explanation
a.append(b)
print(a)
def sum(data):
sum the elements of an array
asum = 0.0
for i in data:
asum = asum + i
return asum
# the length of the array is defined her... |
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Description:
View in Colaboratory
<img height="60" width="120" src="https
Step1: visit http
Step2: What happen when we change the tree depth?
Step3: Try by yours... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
from sklearn.preprocessing import Imputer
from sklearn import tree
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
% matplotlib inline
#train_df = pd.read_csv("titanic.csv")
#test_df = pd.read_csv("titanic_test.csv")
from google.colab imp... |
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Description:
Pandas Crash Course
<img style="float
Step1: Pandas Series and DataFrame objects
There are two main data structures in pandas
Step2: Dataframes use t... | <ASSISTANT_TASK:>
Python Code:
# Python imports
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
Explanation: Pandas Crash Course
<img style="float: right; border:3px solid black" src="images/10_Panda_DailyMail_7_Nov_2013.jpg" border="5" width=30%>
Pandas is a Python packa... |
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Description:
<h2 align="center">点击下列图标在线运行HanLP</h2>
<div align="center">
<a href="https
Step1: 加载模型
HanLP的工作流程是先加载模型，模型的标示符存储在hanlp.pretrained这个包中，按照NLP任务归类。
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Python Code:
!pip install hanlp -U
Explanation: <h2 align="center">点击下列图标在线运行HanLP</h2>
<div align="center">
<a href="https://colab.research.google.com/github/hankcs/HanLP/blob/doc-zh/plugins/hanlp_demo/hanlp_demo/zh/pos_stl.ipynb" target="_blank"><img src="https://colab.research.google.com/assets... |
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Description:
Tutorial Outline
Introduction
Preprerequisites
How does it work?
Authentication
Authentication keys
MongoDB Collection
Starting a Stream
Stream Listen... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import tweepy
import matplotlib.pyplot as plt
import pymongo
import ipywidgets as wgt
from IPython.display import display
from sklearn.feature_extraction.text import CountVectorizer
import re
from datetime import datetime
%matplotlib inline
Explanati... |
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Description:
Ejemplo cuencas
En el siguiente ejemplo se presentan las funcionalidades básicas de la herramienta wmf.Stream y wmf.Basin
dentro de los temas tocados s... | <ASSISTANT_TASK:>
Python Code:
#Paquete Watershed Modelling Framework (WMF) para el trabajo con cuencas.
from wmf import wmf
Explanation: Ejemplo cuencas
En el siguiente ejemplo se presentan las funcionalidades básicas de la herramienta wmf.Stream y wmf.Basin
dentro de los temas tocados se presenta:
Trazado de corrient... |
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Description:
HTTPX
<img src="https
Step1: Using httpbin.org | <ASSISTANT_TASK:>
Python Code:
from datetime import datetime
Explanation: HTTPX
<img src="https://raw.githubusercontent.com/encode/httpx/master/docs/img/butterfly.png" alt="HTTPX Logo" style="width: 400px;"/>
https://www.python-httpx.org/
A next-generation HTTP client for Python.
HTTPX is a fully featured HTTP client f... |
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Description:
<table>
<tr align=left><td><img align=left src="../images/CC-BY.png">
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy
import matplotlib.pyplot as plt
Explanation: <table>
<tr align=left><td><img align=left src="../images/CC-BY.png">
<td>Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Ma... |
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Description:
<center>
<img src="../../img/ods_stickers.jpg">
Открытый курс по машинному обучению. Сессия №3
<center>Автор материала
Step1: Здесь у нас текст вопрос... | <ASSISTANT_TASK:>
Python Code:
!head -1 hw8_data/stackoverflow.10kk.tsv
!head -1 hw8_data/stackoverflow_10mln.tsv
Explanation: <center>
<img src="../../img/ods_stickers.jpg">
Открытый курс по машинному обучению. Сессия №3
<center>Автор материала: программист-исследователь Mail.Ru Group Юрий Кашницкий
<center> Домашнее ... |
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Description:
Tutorial "Algorithmic Methods for Network Analysis with NetworKit" (Part 3)
Determining Important Nodes
There are a number of ways to measure the impor... | <ASSISTANT_TASK:>
Python Code:
from networkit import *
%matplotlib inline
cd ~/workspace/NetworKit
G = readGraph("input/MIT8.edgelist", Format.EdgeListTabZero)
# Code for 3-3) and 3-4)
%matplotlib inline
import matplotlib.pyplot as plt
# def avgFriendDegree(v):
count = 0 # count the number of persons whose friends have... |
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Description:
Experiment
Step1: Load and check data
Step2: ## Analysis
Experiment Details
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Python Code:
%load_ext autoreload
%autoreload 2
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import tabulate
import pprint
import click
import numpy as np
import pandas as pd
from ray.tune.commands import *
from nupi... |
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Description:
Copyright 2018 Google LLC.
Step1: Cat vs. Dog Image Classification
Exercise 2
Step2: These are just a few of the options available (for more, see the... | <ASSISTANT_TASK:>
Python Code:
# 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, sof... |
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Description:
Example 1
Step1: Load time series data
Step2: There are a few supported file formats. AT2 files can be loaded as follows
Step3: Create site profile... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pysra
%matplotlib inline
# Increased figure sizes
plt.rcParams["figure.dpi"] = 120
Explanation: Example 1 : Time series SRA
Time series analysis to compute surface response spectrum and site
amplification functions.
End of explana... |
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Description:
Function Approximation with a Multilayer Perceptron
This code is provided as supplementary material of the lecture Machine Learning and Optimization in... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
function_select = 5
def myfun(x):
functions = {
1: np.power(x,2), # quadratic function
2: np.sin(x), # sinus
3: np.sign(x), # signum
4: np.exp(x), # expone... |
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Description:
Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
Th... | <ASSISTANT_TASK:>
Python Code:
# Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes hand... |
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Description:
Data BootCamp Project
Step1: Who has Grit?
Hockey has always been a eloquent yet brutal sport, in this analysis I'm finding the player that embodies t... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd #PandasPandas
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
print('PandaPandaPanda ', pd.__version__)
df=pd.read_csv('NHLQUANT.csv')
Explanation: Data BootCamp Project
End of explanation
plt.plot(df.index,df['Grit'])
Explanation: Who has Grit?
H... |
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Description:
This IPython Notebook illustrates the use of the openmc.mgxs.Library class. The Library class is designed to automate the calculation of multi-group cr... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import math
import matplotlib.pyplot as plt
import numpy as np
import openmc
import openmc.mgxs
Explanation: This IPython Notebook illustrates the use of the openmc.mgxs.Library class. The Library class is designed to automate the calculation of multi-group cross sectio... |
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Description:
Copyright 2020 The TensorFlow Authors.
Step1: データ増強
<table class="tfo-notebook-buttons" align="left">
<td><a target="_blank" href="https
Step2: データ... | <ASSISTANT_TASK:>
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 writin... |
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Description:
Copyright 2019 The TensorFlow Authors.
Step1: Get started with TensorBoard
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_bl... | <ASSISTANT_TASK:>
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 writin... |
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Description:
Compare the different ICA algorithms in MNE
Different ICA algorithms are fit to raw MEG data, and the corresponding maps
are displayed.
Step1: Read an... | <ASSISTANT_TASK:>
Python Code:
# Authors: Pierre Ablin <pierreablin@gmail.com>
#
# License: BSD (3-clause)
from time import time
import mne
from mne.preprocessing import ICA
from mne.datasets import sample
print(__doc__)
Explanation: Compare the different ICA algorithms in MNE
Different ICA algorithms are fit to raw ME... |
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Description:
Converting $\LaTeX$ to <span style="font-variant
Step1: Now data contains the text that is stored in this file.
Step2: Let us look at the output file... | <ASSISTANT_TASK:>
Python Code:
with open('example.tex') as f:
data = f.read()
Explanation: Converting $\LaTeX$ to <span style="font-variant:small-caps;">Html</span>
The purpose of the following exercise is to implement a translator from $\LaTeX$ to
MathML. $\LaTeX$ is a document markup language
that is especially... |
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Description:
Moving through a physical experience
Some examples of IoT application that ping a digital twin
This doc does not really teach anything new, but it will... | <ASSISTANT_TASK:>
Python Code:
from azure.identity import AzureCliCredential
from azure.digitaltwins.core import DigitalTwinsClient
# using yaml instead of
import yaml
import uuid
# using altair instead of matplotlib for vizuals
import numpy as np
import pandas as pd
# you will get this from the ADT resource at portal... |
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Description:
Structures like these are encoded in "PDB" files
Entries are determined by columns in the file, not by spaces between the columns
Step1: Predict what ... | <ASSISTANT_TASK:>
Python Code:
#record atom_name chain x y z occupancy atom_type
# | | | | | | | |
#ATOM 1086 CG LYS A 141 -4.812 9.683 2.584 1.00 26.78 N0
# | | | ... |
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Description:
Sentiment Classification & How To "Frame Problems" for a Neural Network
by Andrew Trask
Twitter
Step1: Note
Step2: Lesson
Step3: Project 1
Step4: W... | <ASSISTANT_TASK:>
Python Code:
def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].uppe... |
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Description:
Performance Benchmarking for KV Drive
The goal of these set of experiments is to characterize the variability across platforms in a systematic and cons... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import random
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
pd.set_option("display.max_rows", 8)
Explanation: Performance Benchmarking for KV Drive
The goal of these set of experiments is to characterize the variability acr... |
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Description:
The first step in any data analysis is acquiring and munging the data
Our starting data set can be found here
Step1: Problems
Step2: Problems
Step3: ... | <ASSISTANT_TASK:>
Python Code:
running_id = 0
output = [[0]]
with open("E:/output.txt") as file_open:
for row in file_open.read().split("\n"):
cols = row.split(",")
if cols[0] == output[-1][0]:
output[-1].append(cols[1])
output[-1].append(True)
else:
outpu... |
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Description:
Batch Normalization – Practice
Batch normalization is most useful when building deep neural networks. To demonstrate this, we'll create a convolutional... | <ASSISTANT_TASK:>
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 demonstrat... |
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Description:
Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate ne... | <ASSISTANT_TASK:>
Python Code:
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the b... |
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Description:
Singular value decomposition (SVD)
The singular value decompostion of a real-valued $m \times n$ matrix $\boldsymbol{A}$ is
Step1: Note
Step2: Perfor... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
Explanation: Singular value decomposition (SVD)
The singular value decompostion of a real-valued $m \times n$ matrix $\boldsymbol{A}$ is:
$$
\boldsymbol{A} = \boldsymbol{U} \bolds... |
178 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<a id='top'> </a>
Author
Step1: Cosmic-ray composition effective area analysis
Table of contents
Load simulation DataFrame and apply quality cuts
Defi... | <ASSISTANT_TASK:>
Python Code:
%load_ext watermark
%watermark -u -d -v -p numpy,matplotlib,scipy,pandas,sklearn,mlxtend
Explanation: <a id='top'> </a>
Author: James Bourbeau
End of explanation
%matplotlib inline
from __future__ import division, print_function
from collections import defaultdict
import os
import numpy a... |
179 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Working with events
This tutorial describes event representation and how event arrays are used to
subselect data.
As usual we'll start by importing the... | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False)... |
180 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Plotting a builtin waveform
In the tools sub-package is a module called plot_source_wave which can be used to plot any of the builtin waveforms in time... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from gprMax.waveforms import Waveform
from tools.plot_source_wave import check_timewindow, mpl_plot
w = Waveform()
w.type = 'ricker'
w.amp = 1
w.freq = 25e6
timewindow = 300e-9
dt = 8.019e-11
timewindow, iterations = check_timewindow(timewindow, dt)
plt = mpl_plot(w, ti... |
181 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Data
Both datasets are text collections from this site.
TCP-ECCO (170mb uncompressed) can be downloaded here
Lincoln (700kb uncompressed) can be downl... | <ASSISTANT_TASK:>
Python Code:
def get_lincoln():
for filepath in sorted(glob.glob('Lincoln/*.txt')):
with open(filepath, 'r', encoding='latin') as f:
doc = f.read()
yield {'filepath': filepath, 'doc': doc}
def get_TCP():
for filepath in sorted(glob.glob('TCP-ECCO/*.txt')):
... |
182 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Sparse Linear Inverse Demo with AMP
In this demo, we illustrate how to use the vampyre package for a simple sparse linear inverse problem. The proble... | <ASSISTANT_TASK:>
Python Code:
import os
import sys
vp_path = os.path.abspath('../../')
if not vp_path in sys.path:
sys.path.append(vp_path)
import vampyre as vp
Explanation: Sparse Linear Inverse Demo with AMP
In this demo, we illustrate how to use the vampyre package for a simple sparse linear inverse problem. ... |
183 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Linear Elasticity in 2D for 3 Phases
Introduction
This example provides a demonstration of using PyMKS to compute the linear strain field for a three-p... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt
n = 21
n_phases = 3
from pymks.tools import draw_microstructures
from pymks.datasets import make_delta_microstructures
X_delta = make_delta_microstructures(n_phases=n_phases, size=(n, ... |
184 | <SYSTEM_TASK:>
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Description:
The selectors module provides a platform-independent abstraction layer on top of the platform-specific I/O monitoring functions in select.
Operating Mo... | <ASSISTANT_TASK:>
Python Code:
# %load selectors_echo_server.py
import selectors
import socket
mysel = selectors.DefaultSelector()
keep_running = True
def read(connection, mask):
"Callback for read events"
global keep_running
client_address = connection.getpeername()
print('read({})'.format(client_addre... |
185 | <SYSTEM_TASK:>
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Description:
Face verification
Goals
train a network for face similarity using triplet loss
work data augmentation, generators and hard negative mining
Dataset
We w... | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
# If you have a GPU, execute the following lines to restrict the amount of VRAM used:
gpus = tf.config.experimental.list_physical_devices('GPU')
if len(gpus) > 1:
print("Using GPU {}".format(gpus[0]))
tf.config.experimental.set_visible_devices(gpus[0], 'GPU... |
186 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contribu... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mri', 'sandbox-3', 'ocnbgchem')
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: MRI
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topics... |
187 | <SYSTEM_TASK:>
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Description:
Logistic Regression with L2 regularization
In this notebook, you will implement your own logistic regression classifier with L2 regularization. You wil... | <ASSISTANT_TASK:>
Python Code:
import os
import zipfile
import string
import numpy as np
import pandas as pd
from sklearn import linear_model
from sklearn.feature_extraction.text import CountVectorizer
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Logistic Regression with L2 regularization
In this not... |
188 | <SYSTEM_TASK:>
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Description:
Text Classification with spaCy
This walkthrough is based on this spaCy tutorial.
Train a convolutional neural network text classifier on the
IMDB datas... | <ASSISTANT_TASK:>
Python Code:
# Python >3.5
!pip install verta
!pip install spacy==2.1.6
!python -m spacy download en
Explanation: Text Classification with spaCy
This walkthrough is based on this spaCy tutorial.
Train a convolutional neural network text classifier on the
IMDB dataset, using the TextCategorizer compone... |
189 | <SYSTEM_TASK:>
Given the following text problem statement, write Python code to implement the functionality described below in problem statement
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Problem:
Say I have two dataframes: | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df1 = pd.DataFrame({'Timestamp': ['2019/04/02 11:00:01', '2019/04/02 11:00:15', '2019/04/02 11:00:29', '2019/04/02 11:00:30'],
'data': [111, 222, 333, 444]})
df2 = pd.DataFrame({'Timestamp': ['2019/04/02 11:00:14', '2019/04/02 11:00:15', '2019/04/02... |
190 | <SYSTEM_TASK:>
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Description:
2A.data - Classification, régression, anomalies - énoncé
Le jeu de données Wine Quality Data Set contient 5000 vins décrits par leurs caractéristiques ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
from jyquickhelper import add_notebook_menu
add_notebook_menu()
Explanation: 2A.data - Classification, régression, anomalies - énoncé
Le jeu de données Wine Quality Data Set contient 5000 vins décrits par leurs caractéristiques chimiques ... |
191 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Why Violin Plots are Awesome for Feature Engineering
Using NLP to Identify Similar Products
At Wayfair, technology and data expertise enable data scien... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from fuzzywuzzy import fuzz
import numpy as np
# some settings to be used throughout the notebook
pd.set_option('max_colwidth', 70)
wf_colors = ["#C7DEB1","#9763A4"]
# make some fake data for a de... |
192 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Text Data Explanation Benchmarking
Step1: Load Data and Model
Step2: Class Label Mapping
Step3: Define Score Function
Step4: Create Explainer Objec... | <ASSISTANT_TASK:>
Python Code:
import copy
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import shap.benchmark as benchmark
import shap
import scipy as sp
import nlp
import torch
pd.set_option('display.max_columns', None... |
193 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Principal Component Analysis with Iris Dataset
Step1: Load Iris dataset
The Iris Dataset here.
This data sets consists of 3 different types of irises’... | <ASSISTANT_TASK:>
Python Code:
from sklearn import datasets
from sklearn.decomposition import PCA
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from mpl_toolkits.mplot3d import Axes3D
# %matplotlib inline
%matplotlib notebook
Explanation: Principal Component Analysis with Iris Dataset
End of... |
194 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Notebook 3.3
Step1: Newick tree files
We learned in notebook 3.2 that the file format to store phylogenetic trees is simply a text file containing a s... | <ASSISTANT_TASK:>
Python Code:
import toytree
Explanation: Notebook 3.3: Newick Assignment
Complete the notebook then download as an HTML file (toolbar -> File -> Download as) and submit your assignment by emailing to Natalie (natalie.niepoth@columbia.edu).
End of explanation
newick = "((a,b),(c, d));"
tre = toyt... |
195 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contribu... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'thu', 'sandbox-2', 'ocnbgchem')
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: THU
Source ID: SANDBOX-2
Topic: Ocnbgchem
Sub-Topics... |
196 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Manuscript4 - Computational Model -- Group-level for Fig. 4
Master code for Ito et al., 2017¶
Takuya Ito (takuya.ito@rutgers.edu)
The model (see Stern ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from scipy import sparse
% matplotlib inline
import scipy.stats as stats
import statsmodels.api as sm
import CompModel_v7 as cm
cm = reload(cm)
import multiprocessing as mp
import sklearn.preprocessing as preprocessing
import sklearn.svm ... |
197 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
A simple python application
Step1: 1.- Introduction to Python dictionaries
In this practice we will extensively use Python Dictionaries, in this preli... | <ASSISTANT_TASK:>
Python Code:
import goslate # pip install goslate
from bs4 import BeautifulSoup # pip install beautifulsoup4
import urllib2 # pip install requests
Explanation: A simple python application: universal translator
In this lab we will develop a universal text translator by using the Google translation web... |
198 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
<a href="http
Step1: The class <code>plot_error_surfaces</code> is just to help you visualize the data space and the parameter space during training a... | <ASSISTANT_TASK:>
Python Code:
# Import the libraries we need for this lab
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
import torch
from torch.utils.data import Dataset, DataLoader
import torch.nn as nn
Explanation: <a href="http://cocl.us/pytorch_link_top">
<img src="https:... |
199 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Python Libraries For Better Code Insights
Snoop - Never Use print Again
Step1: Snooping on execution
Step2: Snooping on referenced functions
Step3: ... | <ASSISTANT_TASK:>
Python Code:
ROMAN = [
(1000, "M"),
( 900, "CM"),
( 500, "D"),
( 400, "CD"),
( 100, "C"),
( 90, "XC"),
( 50, "L"),
( 40, "XL"),
( 10, "X"),
( 9, "IX"),
( 5, "V"),
( 4, "IV"),
( 1, "I"),
]
def to_roman(number: int):
result = ""
for... |
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