Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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13,000 | <ASSISTANT_TASK:>
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... | <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: Load and check data
Step2: ## Analysis
Step3: Does improved weight pruning outperforms regular SET
Step4: Results not in line with what the p... |
13,001 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import keras
from keras.datasets import mnist
# Load the datasets
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# What is the type of X_train?
# What is the type of y_train?
# Find number of observations in training data
# Find number of observations in test... | <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: Basic data analysis on the dataset
Step2: Display Images
|
13,002 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
!pip install -q -U kfp
import os
os.environ['PROJECT_ID'] = 'yourProject' # Set your project.
os.environ['BUCKET'] = 'yourBucket' # Set your bucket.
os.environ['GKE_CLUSTER_NAME'] = 'yourCluster' # Set your GKE cluster name.
os.environ['GKE_CLUSTER_ZONE... | <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: Set environment variables
Step2: Run the Pipeline locally by using the Beam runner
Step3: Build the container image
Step4: Compile the TFX pi... |
13,003 | <ASSISTANT_TASK:>
Python Code:
5 == 5
5 == 4
1 == 1.0
5 == '5'
2 + 1 == 3
2 ** 3 - 1 == 8 - 1
'a' == 'a'
'Hello Buya' == "Hello Buya"
'hello buya' == 'Hello Buya'
type(True)
type(False)
5 == 3
1 != 1
5 > 3
5 < 3
'hello' == 'Hello'
'MARIO' == 'MARIO '
1.5 == 1
1.0 == 1
1 == 1.0
5 != 7 - 2
5 >= 5.0
'5' > '24' # ראו אז... | <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: <p style="text-align
Step2: <p style="text-align
Step3: <div class="align-center" style="display
Step4: <p style="text-align
|
13,004 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from ecell4 import *
import matplotlib.pylab as plt
import numpy as np
import seaborn
seaborn.set(font_scale=1.5)
import matplotlib as mpl
mpl.rc("figure", figsize=(6, 4))
def Hill(E, Km, nH):
return E ** nH / (Km ** nH + E ** nH)
data = np.array([[Hill(A, 0.5, 8) ... | <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: ブール演算
Step2: 試すと
Step3: ORゲート
Step4: 複雑に見えるが、$K_1=K_2$かつ$n_1=n_2$の場合を考えればヒル式と同じ
Step5: 実は分解を制御しても似たようなことはできる
Step6: 上の式においてA、Bがそれぞれ0もしくは十分に... |
13,005 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
pd.Series?
animales = ['Tigre', 'Oso', 'Camello']
pd.Series(animales)
numeros = [1, 2, 3]
pd.Series(numeros)
animales = ['Tigre', 'Oso', None]
pd.Series(animales)
numeros = [1, 2, None]
pd.Series(numeros)
import numpy as np
np.nan == None
np.nan == np.nan
print(np.... | <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: <br>
Step2: <br>
Step3: <br>
Step4: <br>
Step5: <br>
Step6: <br>
Step7: <br>
Step8: <br>
Step9: Búsqueda en una Serie
Step10: <br>
Step... |
13,006 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import this
import numpy as np
np.array([1,2,3])
a = np.array([[1,2,3], [4,5,6]])
a = np.array([1,2,3])
b = np.array([4,5,6])
a+b
a*b
a/b
a**b
np.array([1, 2, 4], dtype=np.float32)
a = np.array([1,2,3])
print(a.dtype)
print(a.astype(np.float64).dtype)
np.arange(2, 10, 3, ... | <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: Красивое лучше, чем уродливое.<br>
Step2: Типы данных в np.array
Step3: Создание массивов в numpy
Step4: Заполнение массива
Step5: Случайные... |
13,007 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
catsData = pd.read_csv('../data/cats.csv')
catsData.head()
%matplotlib inline
import matplotlib.pyplot as plt
catsData.Hwt.hist()
import numpy as np
normal_samples = np.random.normal(loc=-2, scale=0.5, size=500)
%matplotlib inline
plt.hist(normal_samples)
print 'Perc... | <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: The histogram tells us a few things
Step2: <img src="Normal_Distribution_PDF.svg">
|
13,008 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import matplotlib.pyplot as plt
% matplotlib inline
df = pd.read_csv('water_data_class.csv', encoding='latin-1')
df
# only countries with more than 25 million inhabitants and those who have values in every column (2002 that one with most non-values)
big_ones = df[df['p... | <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: 1) What is the average renewable freshwater resource?
Step2: 2) What is the average withdrawl rate in 2014?
Step3: 3) Which are the 5 countrie... |
13,009 | <ASSISTANT_TASK:>
Python Code:
import datetime
# scientific python add-ons
import numpy as np
import pandas as pd
# plotting stuff
# first line makes the plots appear in the notebook
%matplotlib inline
import matplotlib.pyplot as plt
# seaborn makes your plots look better
try:
import seaborn as sns
sns.set(rc=... | <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: SPA output
Step2: Speed tests
Step3: This numba test will only work properly if you have installed numba.
Step4: The numba calculation takes ... |
13,010 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'sandbox-1', 'toplevel')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name... | <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: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
13,011 | <ASSISTANT_TASK:>
Python Code:
# Import the pandas and numpy libraries
import pandas as pd
import numpy as np
# Read a file with an absolute path
ufo = pd.read_csv('/Users/josiahdavis/Documents/GitHub/python_data_analysis/ufo_sightings.csv')
# Alterntively, read the the file using a relative path
ufo = pd.read_csv('ufo... | <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: Summarize the data that was just read in
Step2: Filtering and Sorting Data
Step3: Sorting
Step4: Modifying Columns
Step5: Handling Missing V... |
13,012 | <ASSISTANT_TASK:>
Python Code:
display(mglearn.plots.plot_logistic_regression_graph())
display(mglearn.plots.plot_single_hidden_layer_graph())
display(mglearn.plots.plot_two_hidden_layer_graph())
from sklearn.neural_network import MLPClassifier
from sklearn.model_selection import train_test_split
from sklearn.datase... | <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: MLP feedforward neural network
Step2: Parameter
Step3: Tuning Neural Networks
Step4: MLP with two layers for smoother boundary
Step5: L2 Pen... |
13,013 | <ASSISTANT_TASK:>
Python Code:
def hello(a,b):
return a+b
# Lazy definition of function
hello(1,1)
hello('a','b')
class Person:
def __init__(self,age,salary):
self.age = age
self.salary = salary
def out(self):
print(self.age)
print(self.salary)
a = Person(30,10000)
a.out()
... | <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: Class
Step2: This is a basic class definition, the age and salary are needed when creating this object. The new class can be invoked like this
... |
13,014 | <ASSISTANT_TASK:>
Python Code:
import os
from pathlib import Path
testfolder = str(Path().resolve().parent.parent / 'bifacial_radiance' / 'TEMP' / 'Tutorial_09')
if not os.path.exists(testfolder):
os.makedirs(testfolder)
print ("Your simulation will be stored in %s" % testfolder)
# VARIABLES of the simulation:
lat... | <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: <a id='step1a'></a>
Step2: <a id='step1b'></a>
Step3: <a id='step1c'></a>
Step4: <a id='step1d'></a>
Step5: <a id='step2'></a>
Step6: <a id... |
13,015 | <ASSISTANT_TASK:>
Python Code:
import os
import glob
import re
import nestly
%load_ext rpy2.ipython
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
library(gridExtra)
library(phyloseq)
## BD for G+C of 0 or 100
BD.GCp0 = 0 * 0.098 + 1.66
BD.GCp100 = 1 * 0.098 + 1.66
workDir = '/home/nick/notebook/SIPSim/dev/fullCyc... | <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: Nestly
Step2: Checking amplicon fragment BD distribution
Step3: fragments w/ diffusion + DBL
Step4: BD min/max
Step5: Plotting number of tax... |
13,016 | <ASSISTANT_TASK:>
Python Code:
from sklearn import datasets
X, y = datasets.make_hastie_10_2(n_samples=12000, random_state=1)
#make random test and train set
from sklearn import cross_validation
from sklearn.cross_validation import train_test_split
train_x, test_x, train_y, test_y = train_test_split(X, y, test_size=0.3... | <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: Neural Network
Step2: If you already trained the dataset there will be a pickle file with the trained network available. Now underneath we test... |
13,017 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('fivethirtyeight')
df = pd.read_csv('data/historical_loan.csv')
df.head()
df.years = df.years.fillna(np.mean(df.years))
#Load the preprocessing module
from sklearn import preprocessing... | <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: Preprocessing the Data
Step2: Accuracy Metrics
Step3: Build Models and Evaluate
Step4: Decision Tree Model - Shallow
Step5: Decision Tree Mo... |
13,018 | <ASSISTANT_TASK:>
Python Code:
sigmoid = lambda x: 1/(1+np.exp(-x))
sigmoid_prime = lambda x: sigmoid(x)*(1-sigmoid(x))
xx = np.linspace(-10, 10, 1000)
plt.plot(xx, sigmoid(xx));
plt.plot(xx, sigmoid_prime(xx));
%cd /home/dockeruser/neural-networks-and-deep-learning/src
%ls
import mnist_loader
import network2
training... | <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: 교차 엔트로피 오차 함수 (Cross-Entropy Cost Function)
Step6: 과최적화 문제
Step10: Hyper-Tangent Activation and Rectified Linear Unit (ReLu) Activation
Step11... |
13,019 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccr-iitm', 'sandbox-2', 'atmos')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", ... | <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: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
13,020 | <ASSISTANT_TASK:>
Python Code:
%pwd
import pandas as pd
names2010 = pd.read_csv('/resources/yob2010.txt', names=['name', 'sex', 'births'])
names2010
names2010.groupby('sex').births.sum()
def add_prop(group):
# Integer division floors
births = group.births.astype(float)
group['prop'] = births / births.sum... | <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: http
Step2: Total number of birth in year 2010 by sex
Step3: Insert prop column for each group
Step4: Verify that the prop clumn sums to 1 wi... |
13,021 | <ASSISTANT_TASK:>
Python Code:
%run db2.ipynb
%%sql -q
CREATE FUNCTION SYSTOOLS.JSON_TABLE(
INJSON BLOB(16M), INELEM VARCHAR(2048), RETTYPE VARCHAR(100))
RETURNS TABLE(TYPE INTEGER, VALUE VARCHAR(2048))
LANGUAGE C
PARAMETER STYLE SQL
PARAMETER CCSID UNICODE
NO SQL
NOT FENCED
DETERMINISTIC
NO EXTERNA... | <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: Table of Contents
Step2: Back to Top
Step3: If SYSTOOLS is not part of the path, you can update it with the following SQL.
Step4: From this p... |
13,022 | <ASSISTANT_TASK:>
Python Code:
#Uplaod the data into the notbook and select the rows that will be used after previous visual inspection of the datasets
datadir = 'D:/Users/Borja.gonzalez/Desktop/Thinkful-DataScience-Borja'
gatrain = pd.read_csv('gender_age_train.csv',usecols=['device_id','gender','age','group'] )
gates... | <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: 2. Dataset Creation
Step2: The dataset stands as follows. There are more than 2.8 million entries being all the values of the cells integers
St... |
13,023 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import glob
from sympy import *
import numpy
import matplotlib.pyplot as plt
import pandas
init_printing()
x,t,a,b= symbols('x t a b')
u = 1+a*exp(1/(10*t))*sin(2*pi/b*x)
u
f = diff(u, t) + diff(u, x)
f
str(u).replace('**', '^')
str(f).replace('**', '^')
filenames ... | <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:
Step1: Define the Manufactured solution
Step2: Compute the forcing function.
Step3: Build a string of the exact and forcing function to be copied to ... |
13,024 | <ASSISTANT_TASK:>
Python Code:
# In IPython or the IPython notebook, it's easiest to use the pylab magic, which
# imports matplotlib, numpy, and scipy.
# The matplotlib notebook flag means that plots will be shown interactively in the
# notebooks, rather than in pop-up windows.
%matplotlib notebook
import numpy as np
i... | <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: 2. Creating Figures
Step2: First, create an empty figure with 2 subplots
Step3: Now let's actually plot the data using the plot method on an a... |
13,025 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import Image
from IPython.html.widgets import interact, interactive, fixed
Image('fermidist.png')
def fermidist(energy, mu, kT):
Compute the Fermi distribution at energy, mu and kT.
return... | <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: Exploring the Fermi distribution
Step3: In this equation
Step4: Write a function plot_fermidist(mu, kT) that plots the Fermi distribution $F(\... |
13,026 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(seed=1)
import math
try:
import torch
except ModuleNotFoundError:
%pip install -qq torch
import torch
from torch import nn
from torch.nn import functional as F
from torch.utils import data
!mkdir figures # for s... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Please find jax implementation of this notebook here
Step4: Basics
Step6: Tokenization
Step9: Vocabulary
Step10: Here are the top 10 words (... |
13,027 | <ASSISTANT_TASK:>
Python Code:
!pip install git+https://github.com/openai/baselines > ~/pip_install_baselines.log
!OPENAI_LOGDIR=$HOME/logs/cartpole-ppo OPENAI_LOG_FORMAT=csv python -m baselines.run --alg=ppo2 --env=CartPole-v0 --num_timesteps=30000 --nsteps=128
from baselines.common import plot_util as pu
results = ... | <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: For all algorithms in baselines summary data is saved into a folder defined by logger. By default, a folder $TMPDIR/openai-<date>-<time> is used... |
13,028 | <ASSISTANT_TASK:>
Python Code:
#Importation des librairies utilisées
import unicodedata
import time
import pandas as pd
import numpy as np
import random
import nltk
import re
import collections
import itertools
import warnings
warnings.filterwarnings("ignore")
import matplotlib.pyplot as plt
import seaborn as sb
sb.s... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: nltk
Step2: Les données
Step3: Bien que déjà réduit par rapport au fichier original du concours, contenant plus de 15M de lignes, le fichier c... |
13,029 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
def print_sum(a, b):
Print the sum of the arguments a and b.
print(a+b)
interact(print_sum, a=(-10.0,10.0,... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Interact basics
Step3: Use the interact function to interact with the print_sum function.
Step5: Write a function named print_string that prin... |
13,030 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
x_axis = np.arange(0+1, len(historical)+1)
plt.plot(x_axis, historical_opening, 'b', x_axis, historical_closing, 'r')
plt.xlabel('Day')
plt.ylabel('Price ($)')
#plt.figure(figsize=(20,10))
plt.title("Stock price: Opening vs Closing")
plt.show();
plt.plot(x_axis, histo... | <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: Historical opening, closing, high, low
Step2: Volume vs Average Volume
Step3: Convert the data collected into numpy arrays
Step4: Stack the d... |
13,031 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
# from ..data.deeplearningai17761.lr_utils import load_dataset
def load_dataset():
train_dataset = h5py.File('../data/deeplearningai17761/train_catvnoncat.h5', "... | <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:
Step1: 深度学习中的许多报错都来自于矩阵/向量尺寸不匹配。 如果你可以保持矩阵/向量的尺寸不变，那么将消除大多错误。
Step2: 预处理数据集的常见步骤是：
Step5: 建立神经网络的主要步骤是： 1.定义模型结构（例如输入特征的数量） 2.初始化模型的参数 3.循环：
Step7: ... |
13,032 | <ASSISTANT_TASK:>
Python Code:
from collections import Counter, defaultdict
from functools import partial
import math, random
def entropy(class_probabilities):
클래스에 속할 확률을 입력하면 엔트로피를 계산하라
return sum(-p * math.log(p, 2) for p in class_probabilities if p)
#각 클래스 레이블의 확률은 별도로 계산
#엔트로피를 구할 때는 어떤 레이블에 어떤 확률값이 주어졌는지까... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step1: 17. decision trees
Step3: 파티션의 엔트로피
Step6: 의사결정나무 만들기
Step8: ~~~
Step9: 이제 학습용 데이터로부터 실제 나무를 구축!!!
Step10: 랜덤포레스트
|
13,033 | <ASSISTANT_TASK:>
Python Code:
from os import path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.forward import make_forward_dipole
from mne.evoked import combine_evoked
from mne.simulation import simulate_evoked
from nilearn.plotting import plot_anat
from nilearn.datasets import load_mni... | <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: Let's localize the N100m (using MEG only)
Step2: Calculate and visualise magnetic field predicted by dipole with maximum GOF
Step3: Estimate t... |
13,034 | <ASSISTANT_TASK:>
Python Code:
import logging
import time
import numpy as np
import scipy.stats as ss
import matplotlib.pyplot as plt
import sklearn
import pandas as pd
from sklearn import datasets
from sklearn import svm
import pylab as pl
from matplotlib.colors import ListedColormap
import sklearn as sk
from sklearn... | <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:
Step1: Les modules suivants vous avez installées avec pip à partir de requirements.txt. Ou bien vous avez installé anaconda (Mac ou Windows), et dans ... |
13,035 | <ASSISTANT_TASK:>
Python Code:
import asyncio
loop = asyncio.get_event_loop()
def hello_world():
print('Hello World!')
loop.stop()
loop.call_soon(hello_world)
loop.run_forever()
async def aprint(text):
await asyncio.sleep(1)
print(text)
return 42
loop.run_until_complete(aprint('Hello world!'))
as... | <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:
Step1: Run a simple callback as soon as possible
Step2: Coroutine Examples
Step3: You can use as many awaits as you like in a couroutine
Step4: All ... |
13,036 | <ASSISTANT_TASK:>
Python Code:
fileh5 = '/home/alessio/Desktop/Noise_Or_Not/m-only_IR_longer_with_nac_2_1_0000/allInput.h5'
inp = qp.readWholeH5toDict(fileh5)
wf2 = np.zeros_like(inp['potCube'],dtype=complex)
allp,allg,allt,alls = wf2.shape
wf = wf2[:,:,:,0].reshape(allp,allg,allt,1)
dime = allp*allg*allt
print(dime,al... | <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:
Step1: Title
Step2: This is along phi. I take the G element of the kin matrix corresponding to the second derivative.
Step3: column or rows?
|
13,037 | <ASSISTANT_TASK:>
Python Code:
import sympy as sym
sym.init_printing()
x, y = sym.symbols('x y')
expr = 3*x**2 + sym.log(x**2 + y**2 + 1)
expr
expr.subs({x: 17, y: 42}).evalf()
%timeit expr.subs({x: 17, y: 42}).evalf()
import math
f = lambda x, y: 3*x**2 + math.log(x**2 + y**2 + 1)
f(17, 42)
%timeit f(17, 42)
g = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We will look at an arbitrary expression $f(x, y)$
Step2: One way to evaluate above expression numerically is to invoke the subs method followed... |
13,038 | <ASSISTANT_TASK:>
Python Code:
# Like this first line, anything following a hash character (for the rest of that line) is considered a comment, and won't be run as code
text_str = "Congratulations, you've just run some Python code!"
print(text_str)
print(text_str)
text = "Weill Cornell Medicine" # An example of a... | <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:
Step1: You will notice that the output of the cell is placed immediately underneath the cell, and that a number appears to the left of the cell to indi... |
13,039 | <ASSISTANT_TASK:>
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... | <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: Load and check data
Step2: ## Analysis
Step3: Results
|
13,040 | <ASSISTANT_TASK:>
Python Code:
def cnv2utf8(mstr):
#print mstr
#print urllib.quote(mstr.encode(u"utf8"))
return urllib.quote(mstr.encode(u"utf8"))
class MyPrettyPrinter(pprint.PrettyPrinter):
def format(self, object, context, maxlevels, level):
if isinstance(object, unicode):
return... | <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:
Step1: 可以看json中文字的程式碼
Step2: 傳入六個變數，回傳一個Json
Step3: url = u"http
|
13,041 | <ASSISTANT_TASK:>
Python Code:
import torch
import numpy as np
import math
import matplotlib.pyplot as plt
num_samples = 7
torch.manual_seed(123)
order = 3
W_gnd = torch.randn(order + 1)
left = -3
right = 3
variance = torch.randn(1)[0] * 10
# print('variance', variance)
# help(torch.arange)
x1 = torch.arange(left, righ... | <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: Looking at https
Step2: sample from the dist
|
13,042 | <ASSISTANT_TASK:>
Python Code:
x = -3
if x > 0:
print("Value is positive")
elif x < 0:
print("Value is negative")
else:
print("Value is zero")
gene = "BRCA2"
geneExpression = -1.2
if geneExpression < 0:
print(gene, "is downregulated")
elif geneExpression > 0:
print(gene, "is upregulated")
... | <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: The general form of writing out such combined conditional statements is as follows
Step2: For very simple conditional checks, you can write the... |
13,043 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
# You can add anything you need as you work
F_META = '../Day2/dsfp_ztf_meta.npy'
F_FEATS = '../Day2/dsfp_ztf_feats.npy'
D_STAMPS = '../Day2/dsfp_ztf_png_stamps'
meta_np = np.load(F_META)
feats_np =... | <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: 0b. Data Location
Step2: 0c. Load Data
|
13,044 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
from desiutil.log import get_logger, DEBUG
log = get_logger()
import seaborn as sns
sns.set(style='white', font_scale=1.1, palette='Set2')
%matplotlib inline
healpixel = 26030
nside = 64
seed = 555
rand = np.random.... | <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:
Step1: To keep the calculations below manageable we specify a single nside=64 healpixel in an arbitrary location of the DESI footprint.
Step2: Specify... |
13,045 | <ASSISTANT_TASK:>
Python Code:
!pip install -U sklearn
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn as skl
import sklearn.linear_model as lm
import scipy.io as sio
!pip install -U okpy
from client.api.notebook import Notebook
ok = Noteboo... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step1: Today's lab covers
Step2: Let's load in the data
Step3: Question 1
Step4: Question 2
Step5: Question 4
Step6: Question 5
Step7: Question 6... |
13,046 | <ASSISTANT_TASK:>
Python Code:
truth = "This is some text.\nMore text, but on a different line!\nInsert your favorite meme here.\n"
pred = read_file_contents("q1data/file1.txt")
assert truth == pred
retval = -1
try:
retval = read_file_contents("nonexistent/path.txt")
except:
assert False
else:
assert retval... | <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: Part B
Step2: Part C
|
13,047 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
!mkdir data
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
data_dir = 'data/'
if not isdir(data_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Getting the data
Step2: These SVHN files are .mat files typically used with Matlab. However, we can load them in with scipy.io.loadmat which we... |
13,048 | <ASSISTANT_TASK:>
Python Code:
import stix2
from stix2 import AttackPattern, Environment, MemoryStore
env = Environment(store=MemoryStore())
ap1 = AttackPattern(
name="Phishing",
external_references=[
{
"url": "https://example2",
"source_name": "some-source2",
},
],
)... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Campaign Example
Step2: Identity Example
Step3: Indicator Example
Step4: If the patterns were identical the result would have been 100.
Step5... |
13,049 | <ASSISTANT_TASK:>
Python Code:
a = 10
print(a)
import time
time.sleep(10)
import sys
from ctypes import CDLL
# This will crash a Linux or Mac system
# equivalent calls can be made on Windows
# Uncomment these lines if you would like to see the segfault
# dll = 'dylib' if sys.platform == 'darwin' else 'so.6'
# libc = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: There are two other keyboard shortcuts for running code
Step2: If the Kernel dies you will be prompted to restart it. Here we call the low-leve... |
13,050 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.DataFrame({'codes':[[71020], [77085], [36415], [99213, 99287], [99234, 99233, 99233]]})
def g(df):
for i in df.index:
df.loc[i, 'codes'] = sorted(df.loc[i, 'codes'])
df = df.codes.apply(pd.Series)
cols = list(df)
for i in range(len(cols)... | <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:
|
13,051 | <ASSISTANT_TASK:>
Python Code:
#from imp import *
#s=load_source('sygma','/home/nugrid/nugrid/SYGMA/SYGMA_online/SYGMA_dev/sygma.py')
%pylab nbagg
import sygma as s
reload(s)
print s.__file__
#import matplotlib
#matplotlib.use('nbagg')
#import matplotlib.pyplot as plt
#matplotlib.use('nbagg')
#import numpy as np
from s... | <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:
Step1: Pick two isotopes, H-1 and Fe-56 and check total production
Step2: Note
Step3: SNII and SNIa
|
13,052 | <ASSISTANT_TASK:>
Python Code:
x = 12
y = 10
z = x + y
x = x + y
y = z + y
x = 1 + 2 * 3 / 4
y = (1 + 2) * (3 / 4)
z = 1 + 2 * (3 / 4)
print(x)
print(y)
print(z)
pi = 3.141592653589793
r = 12 / 2
vol = (4/3) * pi * (r**3)
print(vol)
vol=14137
r3 = vol / ((4/3) * pi)
r = r3**(1/3)
print(round(r))
hours=10
if hours >... | <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:
Step1: Once you have calculated what you think x, y and z are, add print statements to the code above and run it to check. Make sure you understand the... |
13,053 | <ASSISTANT_TASK:>
Python Code:
sess = tf.Session()
print(str(sess.run(hello),encoding = "utf-8"))
# print(sess.run(hello))
sess.close()
a = tf.constant(1234, dtype=tf.float32)
b = tf.constant(5000, dtype=tf.float32)
print(a)
print(b)
add_op = a + b
print(add_op)
with tf.Session() as sess:
print(sess.run(add_op))
ad... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 마크다운으로 메모 작성하기...!
|
13,054 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.collections
import geopandas as gpd
import open_cp.network
import open_cp.sources.chicago
import open_cp.geometry
#data_path = os.path.join("/media", "disk", "Data")
data_path = os.path.join("..", "..", "..", "..", "..",... | <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: Geometry
Step2: Event data
Step3: Save for later
Step4: With old data
|
13,055 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'besm-2-7', 'atmos')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "email... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
13,056 | <ASSISTANT_TASK:>
Python Code:
df=pd.read_csv("311-2014.csv", nrows=200000)
dateutil.parser.parse(df['Created Date'][0])
def parse_date(str_date):
return dateutil.parser.parse(str_date)
df['created_datetime']=df['Created Date'].apply(parse_date)
df.index=df['created_datetime']
df['Complaint Type'].describe()
df.g... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: What was the most popular type of complaint, and how many times was it filed?
Step2: Make a horizontal bar graph of the top 5 most frequent com... |
13,057 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import requests as req
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import ttest_ind, ttest_rel
from scipy.stats import gaussian_kde
from statsmodels.formula.api import ols, mixedlm, gee
from statsmodels.s... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Carregando dados de IDH-M da Wikipedia
Step2: Análise
Step3: Testando hipótese
Step4: A resposta de diversos testes, para um nível de 5% de s... |
13,058 | <ASSISTANT_TASK:>
Python Code:
APIKEY="AIzaSyBQrrl4SZhE3QtxsnbjY2WTdgcBz0G0Rfs" # CHANGE
print APIKEY
PROJECT_ID = "qwiklabs-gcp-14067121d7b1d12c" # CHANGE
print PROJECT_ID
BUCKET = "qwiklabs-gcp-14067121d7b1d12c" # CHANGE
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT_ID
from googleapicl... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: <h2> Define an API calling function </h2>
Step2: <h2> Test the Sentiment Analysis </h2>
Step3: <h2>Use the Dataproc cluster to run a Spark job... |
13,059 | <ASSISTANT_TASK:>
Python Code:
ciphertxt = open('cipher.txt', 'r')
cipher = ciphertxt.read().split(',') #Splits the ciphertxt into a list, splits at every ,
cipher = [int(i) for i in cipher]
ciphertxt.close()
search = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y',... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Below are the lists I created that will help me narrow my search. I created the list called search because the key was only allowed to contain 3... |
13,060 | <ASSISTANT_TASK:>
Python Code:
%%bash
source activate py2env
pip uninstall -y google-cloud-dataflow
conda install -y pytz==2018.4
pip install apache-beam[gcp] tensorflow_transform==0.8.0
%%bash
pip freeze | grep -e 'flow\|beam'
import tensorflow as tf
import apache_beam as beam
print(tf.__version__)
# change these to ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: You need to restart your kernel to register the new installs running the below cells
Step3: <h2> Save the query from earlier </h2>
Step4: <h2>... |
13,061 | <ASSISTANT_TASK:>
Python Code:
% matplotlib inline
from numpy import zeros, zeros_like, ones, vstack, mod, loadtxt
import matplotlib.pyplot as plt
from numpy.linalg import pinv
def h(theta, x):
y_estimated = 0.
for theta_i, x_i in zip(theta, x):
y_estimated += theta_i*x_i
return y_estimated
def J(t... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Introduction et notations
Step2: Application
Step3: Nous sommes pour l'instant intéressé uniquement par les années en poste et les salaires. O... |
13,062 | <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 ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Simulate Tasks
Step2: 3.0.1a visualize synaptic matrix (sample subject)
Step3: 3.0.1 Visualize actual estimated 'intrinsic FC's from Pearson F... |
13,063 | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter, AutoMinorLocator)
import pandas as pd
os.getcwd()
os.chdir('..')
os.getcwd()
sys.path.append('../scripts')
import bicorr_plot a... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Move up a directory for easier access
Step2: Find the other data
Step3: Which do I want to plot on the same distribution?
Step4: General plot... |
13,064 | <ASSISTANT_TASK:>
Python Code:
import torch as T
import torch.autograd
from torch.autograd import Variable
import numpy as np
'''
Define a scalar variable, set requires_grad to be true to add it to backward path for computing gradients
It is actually very simple to use backward()
first define the computation graph, th... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Simplicity of using backward()
Step2: The simple operations defined a forward path $z=(2x)^3$, $z$ will be the final output Variable we would l... |
13,065 | <ASSISTANT_TASK:>
Python Code:
import pints
import pints.toy
import numpy as np
import matplotlib.pyplot as plt
# Create log pdf
log_pdf = pints.toy.NealsFunnelLogPDF()
# Plot marginal density
levels = np.linspace(-7, -1, 20)
x = np.linspace(-10, 10, 100)
y = np.linspace(-10, 10, 100)
X, Y = np.meshgrid(x, y)
Z = [[log... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We can also sample independently from this toy LogPDF, and add that to the visualisation
Step2: We now try to sample from the distribution with... |
13,066 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
#the above call us to display the seaborn plots within the IPython notebook
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv("/Users/.../Machine Learning Competitions/Kaggle/Right Whale Recognition Challenge/features/rgbHisto... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: As you can see, the images in the Kaggle data set are far from being evenly distributed. Many classes have fewer than ten observations while, on... |
13,067 | <ASSISTANT_TASK:>
Python Code:
# Uncomment to install required python modules
# !sh ../utils/setup.sh
# Add custom utils module to Python environment
import os
import sys
sys.path.append(os.path.abspath(os.pardir))
from gps_building_blocks.cloud.utils import bigquery as bigquery_utils
from utils import model
from utils... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Set paramaters
Step2: Next, let's configure modeling options.
Step3: Train the model
Step4: Next cell triggers model training job in BigQuery... |
13,068 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import ext_datos as ext
import procesar as pro
import time_plot as tplt
dia1 = ext.extraer_data('dia1')
cd ..
dia2 = ext.extraer_data('dia2')
cd ..
dia3 = ext.extraer_data('dia3')
cd ..
dia4 = ext.extraer_data('dia4')
motoresdia1 = pro.procesar(dia1)
motoresdia2 = p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Importamos las librerías creadas para trabajar
Step2: Generamos los datasets de todos los días
Step3: Se procesan las listas anteriores, se co... |
13,069 | <ASSISTANT_TASK:>
Python Code:
MODEL_NAME = 'class-model-01'
TRAIN_DATA_FILES_PATTERN = 'data/train-*.tfrecords'
VALID_DATA_FILES_PATTERN = 'data/valid-*.tfrecords'
TEST_DATA_FILES_PATTERN = 'data/test-*.tfrecords'
RESUME_TRAINING = False
PROCESS_FEATURES = True
EXTEND_FEATURE_COLUMNS = True
MULTI_THREADING = True
HEA... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 1. Define Dataset Metadata
Step2: 2. Define Data Input Function
Step3: b. Data pipeline input function
Step4: 3. Define Feature Columns
Step5... |
13,070 | <ASSISTANT_TASK:>
Python Code:
from openhunt.mordorutils import *
spark = get_spark()
mordor_file = "https://raw.githubusercontent.com/OTRF/mordor/master/datasets/small/windows/execution/host/empire_launcher_vbs.zip"
registerMordorSQLTable(spark, mordor_file, "mordorTable")
df = spark.sql(
'''
SELECT `@timestamp`, Ho... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Download & Process Mordor Dataset
Step2: Analytic I
Step3: Analytic II
Step4: Analytic III
Step5: Analytic IV
Step6: Analytic V
Step7: Ana... |
13,071 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
view_sentence_range = (5, 10)
DON'T MODIFY AN... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Language Translation
Step3: Explore the Data
Step6: Implement Preprocessing Function
Step8: Preprocess all the data and save it
Step10: Chec... |
13,072 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.random.seed(42) # Setting the random seed
# a vector: the argument to the array function is a Python list
v = np.random.rand(10)
v
# a matrix: the argument to the array function is a nested Python list
M = np.random.rand(10, 2)
M
# v is a vector, and has only one d... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We can index elements in an array using the square bracket and indices
Step2: If we omit an index of a multidimensional array it returns the wh... |
13,073 | <ASSISTANT_TASK:>
Python Code:
some_global_variable = 6
def my_function(arg):
This is a docstring.
some_global_variable = 1
return some_global_variable
print(my_function(5))
some_global_variable
%time some_list = [x**x for x in range(9001)]
!sudo python3.6 -m pip install matplotlib
%matplotlib not... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: <img src="resources/jupyter-main-logo.svg" alt="Jupyter" height="200" width="200">
Step3: Magic
Step4: Bash
Step5: HTML
Step6: Embed YouTube... |
13,074 | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.2,<2.3"
%matplotlib inline
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
print(b.get_parameter(qualifier='distance', context='system'))
print(b.get_parameter(q... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: As always, let's do imports and initialize a logger and a new bundle. See Building a System for more details.
Step2: Relevant Parameters
Step3... |
13,075 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import display
import pandas
%%writefile data.csv
Date,Open,High,Low,Close,Volume,Adj Close
2012-06-01,569.16,590.00,548.50,584.00,14077000,581.50
2012-05-01,584.90,596.76,522.18,577.73,18827900,575.26
2012-04-02,601.83,644.00,555.00,583.98,28759100,581.48
2012-03-0... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Pandas
Step2: Here is a small amount of stock data for APPL
Step3: Read this as into a DataFrame
Step4: And view the HTML representation
Step... |
13,076 | <ASSISTANT_TASK:>
Python Code:
# Import libraries
import numpy as np
%config InlineBackend.figure_format = 'retina'
%matplotlib inline
import matplotlib.pyplot as plt
kernel_fast = np.array([0, .5, 1, .8, .4, .2, .1, 0])
kernel_slow = np.hstack([np.arange(0,1,.2),np.arange(1,0,-.04)])
plt.figure(figsize=(5,6))
plt.sub... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 1. Define kernels of neuronal response to static gratings
Step2: 2. Estimate neural response to preferred and opposite directions
|
13,077 | <ASSISTANT_TASK:>
Python Code:
#@title Copyright 2020 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/L... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: <table class="tfo-notebook-buttons" align="left">
Step2: Sentences
Step3: Run the model
Step5: Semantic similarity
|
13,078 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from bigbang.archive import Archive
from bigbang.archive import load as load_archive
import bigbang.parse as parse
import bigbang.graph as graph
import bigbang.mailman as mailman
import bigbang.process as process
import networkx as nx
import matplotlib.pyplot as plt
imp... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Set a valid date frame for building the network.
Step2: Filter data according to date frame and export to .gexf file
|
13,079 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import networkx as nx
import seaborn as sns
%matplotlib inline
edges = np.genfromtxt('0.edges', dtype="int", delimiter=" ")
G = nx.read_edgelist('0.edges', delimiter=" ")
def total_edges(edges):
return (len(G.nodes())*(len(G.nodes()-1)))/2
def p_edges(edges):
re... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Ejercicios Comparación Tamaño del componente Gigante
|
13,080 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import HTML
HTML('../style/course.css') #apply general CSS
from IPython.display import HTML
from ipywidgets import interact
HTML('../style/code_toggle.html')
def FS_coeffs(x, m, func, T=2.0*np.pi... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Import section specific modules
Step3: 2.3. Fourier Series<a id='math
Step4: That should be good enough for our purposes here. Next we create ... |
13,081 | <ASSISTANT_TASK:>
Python Code:
# Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
from __future__ import print_function
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a n... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps
Step2: Inline Qu... |
13,082 | <ASSISTANT_TASK:>
Python Code:
blocks = []
# Define Neo blocks
bl0 = neo.Block(name='First block')
bl1 = neo.Block(name='Second block')
bl2 = neo.Block(name='Third block')
print("bl0.segments = ", bl0.segments)
print("bl1.segments = ", bl1.segments)
print("bl2.segments = ", bl2.segments)
blocks = [bl0, bl1, bl2]
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Write a nwb file
Step2: Read the NWB file
Step3: Using Neo NWBIO
|
13,083 | <ASSISTANT_TASK:>
Python Code:
#@test {"skip": true}
!pip install --quiet --upgrade tensorflow-federated
!pip install --quiet --upgrade nest-asyncio
import nest_asyncio
nest_asyncio.apply()
import collections
import time
import tensorflow as tf
import tensorflow_federated as tff
source, _ = tff.simulation.datasets.emn... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: トレーニングするモデルの定義
Step2: リモートエグゼキュータのセットアップ
Step3: トレーニングの実行
|
13,084 | <ASSISTANT_TASK:>
Python Code:
# Load neccessary libraries changed pandas import for convinience
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklea... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Using 8 estimators (usage of one per core if 8 cores (jobs) are used)
Step2: Plot of the training time in seconds of each RFC against the numbe... |
13,085 | <ASSISTANT_TASK:>
Python Code:
# get date of last tag
from subprocess import Popen, PIPE
x, err = Popen('git log -1 --tags --simplify-by-decoration --pretty="%ai"| cat', stdin=PIPE, stdout=PIPE, stderr=PIPE, shell=True).communicate()
start_date = x.split()[0].decode('utf-8')
start_date
# today's date
import datetime
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: This notebook will generate a file in the current directory with the name "changelog_VERSION.md". You can edit and append this on front of the C... |
13,086 | <ASSISTANT_TASK:>
Python Code:
print('Esto es un mensaje')
# NOTA: en Python, las líneas que comienzan con # son comentarios
# El intérprete no las lee. Los humanos sí deberíamos leerlas :-)
mivariable = 34
edad = 25
year = 1992
print(mivariable)
print(year)
print('mivariable')
print('year')
print('El niño come manza... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Variables y tipos de datos
Step2: En Python podemos utilizar como nombre de variable cualquier secuencia de caracteres alfanuméricos, siempre q... |
13,087 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
cd /Users/grefe950/evolve/dmestar/trk/
def loadTrack(filename):
return np.genfromtxt(filename, usecols=(0, 1, 2, 3, 4, 5))
masses = [0.1, 0.5, 1.0, 1.5]
# directory extensions
gs98_dir = 'gs98/p000/a0/amlt1884'
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Quick mass track loader
Step2: Preliminary definitions, including masses and file extensions.
Step3: It's quite curious as to why the GAS07 an... |
13,088 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
dict = {'abc':'1/2/2003', 'def':'1/5/2017', 'ghi':'4/10/2013'}
df = pd.DataFrame({'Member':['xyz', 'uvw', 'abc', 'def', 'ghi'], 'Group':['A', 'B', 'A', 'B', 'B'], 'Date':[np.nan, np.nan, np.nan, np.nan, np.nan]})
def g(dict, df):
df["Date"] = df[... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
13,089 | <ASSISTANT_TASK:>
Python Code:
def my_function(a, b):
This function sum together two variables (if they are summable).
return a + b
my_function(2, 5)
my_function("Spam ", "eggs")
my_function([1, 2, "A"], [5, 5.3])
def my_function(arg1, arg2, kwarg1=0, kwarg2=0):
This function accepts two... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Funkce a třídy
Step2: Funkce může být opakovaně použita kde sčítání různých argumentů (čísel, textu i listů)
Step4: Poznámka
Step7: Třídy
Ste... |
13,090 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import torch
lens = load_data()
max_len = max(lens)
mask = torch.arange(max_len).expand(len(lens), max_len) < lens.unsqueeze(1)
mask = mask.type(torch.LongTensor)
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
13,091 | <ASSISTANT_TASK:>
Python Code:
from feature_selector import FeatureSelector
import pandas as pd
air_quality = pd.read_csv('data/AirQualityUCI.csv')
air_quality['Date'] = pd.to_datetime(air_quality['Date'])
air_quality['Date'] = (air_quality['Date'] - air_quality['Date'].min()).dt.total_seconds()
air_quality['Time'] = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Air Quality Dataset
Step2: Insurance Dataset
|
13,092 | <ASSISTANT_TASK:>
Python Code:
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
weather = pd.read_table('data/daily_weather.tsv')
stations = pd.read_table('data/stations.tsv')
usage = pd.read_table('data/usage_2012.tsv')
w... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: read in data
Step2: repeat data fixing from previous exercise
Step3: 1a. Plot the daily temperature over the course of the year. (This should ... |
13,093 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
#export
from exp.nb_12a import *
path = datasets.untar_data(datasets.URLs.IMDB)
ll = pickle.load(open(path/'ll_lm.pkl', 'rb'))
bs,bptt = 128,70
data = lm_databunchify(ll, bs, bptt)
vocab = ll.train.proc_x[1].vocab
# ! wget http://fil... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Data
Step2: Finetuning the LM
Step3: In our current vocabulary, it is very unlikely that the ids correspond to what is in the vocabulary used ... |
13,094 | <ASSISTANT_TASK:>
Python Code:
gPlayers = [0, 1]
gStart = 0
def set_bits(Bits):
result = 0
for b in Bits:
result |= 1 << b # bitwise or 2**b
return result
"{:b}".format(set_bits([0, 1, 4]))
assert set_bits([0, 1, 4]) == 2**0 + 2**1 + 2**4
def set_bit(n):
return 1 << n
assert set_bit(7) == 2 ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: States are represented as integers. The first 9 bits encode the marks of the player X, the next 9 bits encode the marks of the player O. Initi... |
13,095 | <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... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Channel Attribution -- Building Blocks of Interpretability
Step2: Setup (feel free to skip)
Step3: BarsWidget
Step4: Spritemaps
Step5: Attri... |
13,096 | <ASSISTANT_TASK:>
Python Code:
from scipy import matrix, sqrt, pi, cos, sin, set_printoptions
p0 = 2224.0 # converted from kN to Newton
rho1 = 183E-6 ; rho2 = 368E-6 # converted from μm to m
w1 = 16.0 ; w2 = 25.0
th1 = 15.0 ; th2 = 55.0
d2r = pi/180.
cos1 = cos(d2r*th1) ; cos2 = cos(d2r*th2)
sin1 = sin(d2r*th1) ; sin2 ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Determination of $\zeta$
|
13,097 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
import numpy
import scipy.stats
import matplotlib.pyplot as pyplot
from IPython.html.widgets import interact, fixed
from IPython.html import widgets
# seed the random number generator so we all get the same results
numpy.random.seed(17)
# so... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: To explore statistics that quantify effect size, we'll look at the difference in height between men and women. I used data from the Behavioral ... |
13,098 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from sklearn import preprocessing
from sklearn import metrics
from sklearn.metrics import accuracy_score
from sklearn.ensemble import AdaBoostClassifier
from sklearn.neighbors import KNeighborsClassifier
import xgboost as xgb
import numpy as np
kobe... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Loading data
Step2: encoding catagorical features
Step3: splitting data into test and train
Step4: seperating features and class in both test... |
13,099 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3-veg-lr', 'toplevel')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
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