Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
11,500 | <ASSISTANT_TASK:>
Python Code:
# Solution of the RRE
def x(t,x0=3,a=10.0,mu=1.0):
return (x0-a/mu)*np.exp(-t*mu)+a/mu
t = np.linspace(0,3)
x0list = np.array([0.5,1,15])
sns.set_palette("Reds",n_colors=3)
for x0 in x0list:
pl.plot(t,x(t,x0),linewidth=4)
pl.title('Population numbers for different initial condition... | <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 note that there is a stationary solution, $x(t)=a/\mu$. From the exponential in the solution, we can see that this is an attracting fixed poi... |
11,501 | <ASSISTANT_TASK:>
Python Code:
#The points awarded this cell corresopnd to partial credit and/or documentation
### BEGIN SOLUTION
def power(x, p=2):
'''Computes x^p
Args:
x: input number
p: input power, defaults to 2
returns: x^p as a floating point
'''
return x**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: Create a function which sums the numbers between its first (inclusive) and second argument (exclusive). For example, if you pass in 3 and 6, it ... |
11,502 | <ASSISTANT_TASK:>
Python Code:
well_PE_Miss = train.loc[train["PE"].isnull(),"Well Name"].unique()
well_PE_Miss
train.loc[train["Well Name"] == well_PE_Miss[0]].count()
train.loc[train["Well Name"] == well_PE_Miss[1]].count()
(train.groupby("Well Name"))["PE"].mean()
(train.groupby("Well Name"))["PE"].median()
train["... | <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: The two wells have all PE missed
Step2: The PE of all wells have no strong variance; For now, fillin the Missing value of median
Step3: ### Bu... |
11,503 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import pinkfish as pf
pf.update_cache_symbols(symbols=['msft', 'orcl', 'tsla'])
pf.remove_cache_symbols(symbols=['tsla'])
pf.update_cache_symbols()
# WARNING!!! - if you uncomment the line below, you'll wipe out
# all the symbols in your cache directory
#pf.remove_... | <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: Update time series for the symbols below.
Step2: Remove the time series for TSLA
Step3: Update time series for all symbols in the cache direct... |
11,504 | <ASSISTANT_TASK:>
Python Code:
# Imports here
import numpy as np
import torch.nn as nn
import torch.optim as optim
import torch
from torchvision import datasets,transforms,utils,models
import matplotlib.pyplot as plt
import os
import time
import copy
!ls -r flower_data/
data_dir = 'flower_data'
train_dir = data_dir + ... | <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: Load the data
Step2: Label mapping
Step3: Building and training the classifier
Step4: Save the checkpoint
Step5: testing
Step6: Loading the... |
11,505 | <ASSISTANT_TASK:>
Python Code:
import os.path as op
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse
from mne.simulation import simulate_stc, simulate_evoked
seed = 42
# parameters for inverse method
method = 'sLORETA'
snr = 3.
lambda2 = 1.... | <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: First, we set some parameters.
Step2: Load the MEG data
Step3: Estimate the background noise covariance from the baseline period
Step4: Gener... |
11,506 | <ASSISTANT_TASK:>
Python Code:
%run "../src/start_session.py"
%run "../src/recurrences.py"
%run "../src/sums.py"
from sympy.abc import i
from oeis import oeis_search, ListData
import knowledge
sys.setrecursionlimit(10000000)
s = oeis_search(id=45)
s(data_only=True)#, data_representation=ListData(upper_limit=20))
with... | <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: OEIS
Step2: Recurrence
Step3: Unfolding
Step4: Involution
Step5: Subsuming
Step6: We can abstract the following conjecture
Step7: Instanti... |
11,507 | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
%%bash
pip freeze | grep google-cloud-bigquery==1.6.1 || \
pip install google-cloud-bigquery==1.6.1
%%bigquery
-- LIMIT 0 is a free query; this allows us to check that the table exists.
SELECT * FROM babyweight.babyweight... | <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: Verify tables exist
Step2: Model 4
Step3: Get training information and evaluate
Step4: Now let's evaluate our trained model on our eval datas... |
11,508 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
# plotting and graphics settings
import matplotlib.pyplot as plt
%pylab inline
try:
import seaborn as sns # pretty graphics. not strictly necessary.
sns.set_context("notebook")
sns.set_style("whitegrid")
except:
pass # with less pretty graphics
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: We can then load the data from the text file and store each of the three columns as a vector
Step2: This is not very enlightening. Of course, w... |
11,509 | <ASSISTANT_TASK:>
Python Code:
#First let's make a function
def even_check(num):
if num%2 ==0:
return True
lst =range(20)
filter(even_check,lst)
filter(lambda x: x%2==0,lst)
<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:
Step1: Now let's filter a list of numbers. Note
Step2: filter() is more commonly used with lambda functions, this because we usually use filter for a ... |
11,510 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
from netgraph import Graph
fig, (ax1, ax2) = plt.subplots(1, 2)
triangle = [(0, 1), (0, 2), (1, 1), (1, 2), (2, 0)]
node_positions = {
0 : np.array([0.2, 0.2]),
1 : np.array([0.5, 0.8]),
2 : np.array([0.8... | <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: Node and edge label properties can also be changed individually after an
|
11,511 | <ASSISTANT_TASK:>
Python Code:
from migrating_catalyst import *
data = DataLoaders(loaders['train'], loaders['valid']).cuda()
@before_batch_cb
def cb(self, xb, yb): return (xb[0].view(xb[0].size(0), -1),),yb
metrics=[accuracy,top_k_accuracy]
learn = Learner(data, model, loss_func=F.cross_entropy, opt_func=Adam,
... | <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 use it in fastai, we first convert the Catalyst dict into a DataLoaders object
Step2: Using callbacks
Step3: The Catalyst example also modi... |
11,512 | <ASSISTANT_TASK:>
Python Code:
# Create hidden linear model.
w_true = [-0.3, 0.5]
polybasis = lambda x, p: PolynomialFeatures(p).fit_transform(x)
linear_model = lambda x, w=w_true: polybasis(x, len(w) - 1).dot(w).reshape(len(x), 1)
utils.plot(({'x': np.linspace(-1., 1.)[:, None], 'model': linear_model},))
# Make noisy... | <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: Least squares parameter estimation
Step2: More generally, the task is to estimate the model weights $\weights$ from the linear model
Step3: Ba... |
11,513 | <ASSISTANT_TASK:>
Python Code:
# First check the Python version
import sys
if sys.version_info < (3,4):
print('You are running an older version of Python!\n\n',
'You should consider updating to Python 3.4.0 or',
'higher as the libraries built for this course',
'have only been tested in... | <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: Session 4
Step2: <a name="part-1---pretrained-networks"></a>
Step3: Now we can load a pre-trained network's graph and any labels. Explore the... |
11,514 | <ASSISTANT_TASK:>
Python Code:
%%cython
cdef f1(int x):
return x*x
cpdef f2(int x):
return x*x
cpdef f3(int x):
return f1(x)
#dir()
f2(3)
f1(3)
f3(3)
%%cython
cpdef fibseq(float[:] x):
cdef int n
cdef int i
n = len(x)
x[0] = 1.
x[1] = 1.
for i in range(2,n):
x[i] = x[i... | <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: f1 is not visible since defined via "cdef"
Step2: filling an numpy array
Step3: basic
Step4: distance function (pure python and cython)
|
11,515 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
SECH_FWHM_CONV = 1./2.6339157938
t_width = 1.0*SECH_FWHM_CONV # [τ]
print('t_width', t_width)
mb_solve_json =
{
"atom": {
"fields": [
{
"coupled_levels": [[0, 1]],
"rabi_freq_t_args": {
"n_pi": 2.0,
"centre": 0.0,
... | <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: Two-Level
Step2: We'll just check that the pulse area is what we want.
Step3: Solve the Problem
Step4: Plot Output
Step5: Analysis
|
11,516 | <ASSISTANT_TASK:>
Python Code:
import synimagegen
import matplotlib.pyplot as plt
import numpy as np
import os
%matplotlib inline
ground_truth,cv,x_1,y_1,U_par,V_par,par_diam1,par_int1,x_2,y_2,par_diam2,par_int2 = synimagegen.create_synimage_parameters(None,[0,1],[0,1],[256,256],dt=0.0025)
frame_a = synimagegen.genera... | <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: Example 1
Step2: Example 2
Step3: Example 3
|
11,517 | <ASSISTANT_TASK:>
Python Code:
%tensorflow_version 2.x
import os
import numpy as np
import tensorflow as tf
from tqdm import tqdm
from matplotlib import pyplot
%matplotlib inline
print("Tensorflow version " + tf.__version__)
WEIGHTS_FILE='./bayesian_fashionMNIST.h5'
GITHUB_REPO='https://github.com/rahulremanan/python_... | <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: Specify variables
Step2: Fashion MNIST dataset
Step3: Define the Bayesian deep-learning model
Step4: Using the TPU
Step5: Train
Step6: Trai... |
11,518 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
rides[:24*10].plot(x='dteday', y='cnt')
dummy_fields = ['seas... | <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: Load and prepare the data
Step2: Checking out the data
Step3: Dummy variables
Step4: Scaling target variables
Step5: Splitting the data into... |
11,519 | <ASSISTANT_TASK:>
Python Code:
import glob # to extend file name pattern to list
import cv2 # OpenCV for image processing
from cv2 import aruco # to find ArUco markers
import numpy as np # for matrices
import matplotlib.pyplot as plt # ... | <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: Template matching
Step2: Both the image used for processing and the template are converted to grayscale images to boost efficiency.
Step3: Cha... |
11,520 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
employments = pd.read_csv('employment_above_15.csv')
employments[0:5]
#Selecting a column and displaying its first 5 elements
employments.get('1991')[0:5]
employments.get('Country')[0:5]
def max_employment(countries, employment):
i = employ... | <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: Importing it takes some time
Step2: 05 - NumPy Arrays
Step3: Let's look at the element type of few array which numpy calls dtype
Step4: |S11 ... |
11,521 | <ASSISTANT_TASK:>
Python Code:
# import os module
import os
os.getcwd()
# The following command provides the details of the imported package definition
# help(os.listdir())
# save the following code as example.py
def add(a,b):
return a+b
# now you can import example.py
# import example
# example.add(5,4)
import m... | <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: User Defined Module
Step2: Import with renaming
Step3: from...import statement
Step4: To import all definitions from the module just specify ... |
11,522 | <ASSISTANT_TASK:>
Python Code:
def add_together(one, two):
one = one + two
return one
def mutiply_and_add(one, two):
one = add_together(one, two)
return one * one
temparary_value = mutiply_and_add(2, 3)
print(temparary_value)
print(mutiply_and_add(2, 3))
number_1 = 10
number_2 = 30
print(len(str(number... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: Interating over a collection
Step3: Iterating over a list of strings
Step4: Default Parameters
Step6: Bonus
|
11,523 | <ASSISTANT_TASK:>
Python Code:
from ieml.usl.usl import usl
u = usl("[E:.b.E:B:.- E:S:. (E:.-wa.-t.o.-' E:.-'wu.-S:.-'t.o.-',)(a.T:.-) > ! E:.l.- (E:.wo.- E:S:.-d.u.-')]")
u.check()
print(u)
u1 = usl("[E:.b.E:B:.- E:S:. (E:.-'wu.-S:.-'t.o.-', E:.-wa.-t.o.-' )(a.T:.-) > ! E:.l.- (E:.wo.- E:S:.-d.u.-')]")
u1.check()
prin... | <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: The ieml lexicons are stored on github, they have to be downloaded first
Step2: The ieml.ieml_database.IEMLDatabase is responsible of reading a... |
11,524 | <ASSISTANT_TASK:>
Python Code:
%%bash
pio init-model \
--model-server-url http://prediction-python3.community.pipeline.io \
--model-type python3 \
--model-namespace default \
--model-name python3_zscore \
--model-version v1 \
--model-path .
%%bash
pio predict \
--model-test-request-path ./data/test_requ... | <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: Predict with Model (CLI)
Step2: Predict with Model under Mini-Load (CLI)
Step3: Predict with Model (REST)
|
11,525 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.html import widgets
from IPython.html import svgwrite
from IPython.display import display
s =
<svg width="100" height="100">
<circle cx="50"... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: Interact with SVG display
Step4: Write a function named draw_circle that draws a circle using SVG. Your function should take the parameters of ... |
11,526 | <ASSISTANT_TASK:>
Python Code:
import numpy as np # used for generating random numbers
def int_to_big(x):
if x == 0:
return [0]
z = []
while x > 0:
t = x % 10
z.append(t)
x //= 10
trim(z)
return z
def big_to_int(x):
z, p = 0, 1
for d in x:
z += p * 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: Convertion utility functions
Step2: Multiplication utility functions
Step3: Karatsuba's algorithm
Step4: Multiplication and testing
Step5: G... |
11,527 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
reviews = pd.read_csv('reviews.txt', header=None)
labels = pd.read_csv('labels.txt', header=None)
from collections import Counter
total_counts = Counter()
for idx,... | <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: Preparing the data
Step2: Counting word frequency
Step3: Let's keep the first 10000 most frequent words. As Andrew noted, most of the words in... |
11,528 | <ASSISTANT_TASK:>
Python Code:
# Start pylab inline mode, so figures will appear in the notebook
%matplotlib inline
import numpy as np
# Generating a random array
X = np.random.random((3, 5)) # a 3 x 5 array
print(X)
# Accessing elements
# get a single element
print(X[0, 0])
# get a row
print(X[1])
# get a column
pri... | <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: Numpy Arrays
Step2: There is much, much more to know, but these few operations are fundamental to what we'll
Step3: Matplotlib
Step4: There a... |
11,529 | <ASSISTANT_TASK:>
Python Code:
n = 10000
steps_to_exit = []
for i in range(n):
x = 0
steps = 0
while -7 < x < 7:
x += np.random.choice([-1, 1]) # step left or right
steps += 1
steps_to_exit.append(steps)
print("Gemiddeld aantal stappen tot suiker: {:.3f}".format(mean(steps_to_exit)... | <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: Oefening 2
Step2: Oefening 3
Step3: Oefening 4
Step4: Met behulp van Monte Carlo simulatie kun je de integraal wel vrij eenvoudig benaderen. ... |
11,530 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
from IPython.core.display import HTML
from rsplib.processing import RSPSource, StreamReasoner
jasper = StreamReasoner("http://jasper", 8183);
jasper.status()
jasper.register_stream("AarhusTrafficData158505", "http://aarhustrafficdata158505:4001/sgraph"... | <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: Now one we assume that RDF Stream are up and running from Part 1. If you did not followed part one please follow the link below and complete the... |
11,531 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
a = tf.constant([2])
b = tf.constant([3])
c = tf.add(a,b)
session = tf.Session()
result = session.run(c)
print(result)
session.close()
with tf.Session() as session:
result = session.run(c)
print(result)
Scalar = tf.constant([2])
Vector = tf.constant(... | <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: <a id="ref3"></a>
Step2: After that, let's make an operation over these variables. The function tf.add() adds two elements (you could also use ... |
11,532 | <ASSISTANT_TASK:>
Python Code:
# -*- coding: utf-8 -*-
import os
import re
import time
import codecs
import argparse
TIME_FORMAT = '%Y-%m-%d %H:%M:%S'
BASE_FOLDER = "C:/Users/sethf/source/repos/chinesepoem/" # os.path.abspath(os.path.dirname(__file__))
DATA_FOLDER = os.path.join(BASE_FOLDER, 'data')
DEFAULT_FIN = os.pa... | <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: 读取数据，去掉不用的数据
Step2: 分词实验
Step3: 分词不是很成功，我们转向直接用汉字字符来代替分段，我们保留标点符号
|
11,533 | <ASSISTANT_TASK:>
Python Code:
a = spot.translate('a U b U c')
a.show('.#')
a.highlight_edges([2, 4, 5], 1)
a.highlight_edge(6, 2).highlight_states((0, 1), 0)
print(a.to_str('HOA', '1'))
print()
print(a.to_str('HOA', '1.1'))
b = spot.translate('X (F(Ga <-> b) & GF!b)'); b
r = b.accepting_run(); r
r.highlight(5) # ... | <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: The # option of print_dot() can be used to display the internal number of each transition
Step2: Using these numbers you can selectively hightl... |
11,534 | <ASSISTANT_TASK:>
Python Code:
def build_dictionaries(mess):
discharge, charge, impedance = {}, {}, {}
for i, element in enumerate(mess):
step = element[0][0]
if step == 'discharge':
discharge[str(i)] = {}
discharge[str(i)]["amb_temp"] = str(element[1][0][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: Save as json
Step2: Loop through all files
Step3: 2. Example of loading and plotting impedance data
Step4: Plot
|
11,535 | <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... | <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: 使用 Keras 和 Tensorflow Hub 对电影评论进行文本分类
Step2: 下载 IMDB 数据集
Step3: 探索数据
Step4: 我们再打印下前十个标签。
Step5: 构建模型
Step6: 现在让我们构建完整模型：
Step7: 层按顺序堆叠以构建分... |
11,536 | <ASSISTANT_TASK:>
Python Code:
import dowhy
from dowhy import CausalModel
from rpy2.robjects import r as R
%load_ext rpy2.ipython
import numpy as np
import pandas as pd
import graphviz
import networkx as nx
np.set_printoptions(precision=3, suppress=True)
np.random.seed(0)
def make_graph(adjacency_matrix, labels=None)... | <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: Utility function
Step2: Experiments on the Auto-MPG dataset
Step3: Causal Discovery with Causal Discovery Tool (CDT)
Step4: As you can see, n... |
11,537 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
from IPython.display import Image
import base64
Image(data=base64.decodestring("iVBORw0KGgoAAAANSUhEUgAAAMYAAABFCAYAAAARv5krAAAYl0lEQVR4Ae3dV4wc1bYG4D3YYJucc8455yCSSIYrBAi4EjriAZHECyAk3rAID1gCIXGRgIvASIQr8UTmgDA5imByPpicTcYGY+yrbx+tOUWpu2e6u7qnZ7qXVFP... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: We're going to be building a model that recognizes these digits as 5, 0, and 4.
Step3: Working with the images
Step4: The first 10 pixels are ... |
11,538 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.html import widgets
from IPython.display import display, SVG
s = ' <svg width="100" height="100"> <circle cx="50" cy="50" r="20" fill="aquamari... | <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: Interact with SVG display
Step3: Write a function named draw_circle that draws a circle using SVG. Your function should take the parameters of ... |
11,539 | <ASSISTANT_TASK:>
Python Code:
import os
CWD = os.getcwd()
import os
import girder_client
from pandas import read_csv
from histomicstk.annotations_and_masks.polygon_merger import Polygon_merger
from histomicstk.annotations_and_masks.masks_to_annotations_handler import (
get_annotation_documents_from_contours, )
AP... | <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. Connect girder client and set parameters
Step2: 2. Polygon merger
Step3: Required arguments for initialization
Step4: maskpaths
Step5: No... |
11,540 | <ASSISTANT_TASK:>
Python Code:
# import requirments
from IPython.display import Image
from IPython.display import display
from IPython.display import HTML
from datetime import *
import json
from copy import *
from pprint import *
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import json
from 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: JS with IPython?
Step2: Python data | D3 Viz
Step3: Passing data from IPython to JS
Step6: Passing data from JS to IPython
Step7: Click "Set... |
11,541 | <ASSISTANT_TASK:>
Python Code:
class Dog:
def __init__(self, name):
self.age = 0
self.name = name
self.noise = "Woof!"
self.food = "dog biscuits"
def make_sound(self):
print(self.noise)
def eat_food(self):
print("Eating " + self.food + ".")
... | <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: In the above examples, it becomes clear that there is much repetition, and we can make the code more compact. Let us abstract common functionali... |
11,542 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from sklearn.neural_network import MLPRegressor
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from sklearn.metrics import r2_score # in order to test the resu... | <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: Importing preprocessing data
Step2: Sorting out data (for plotting purposes)
Step3: Artificial Neural Network (Gridsearch, DO NOT RUN)
Step4:... |
11,543 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 1
%matplotlib inline
from pygsf.geometries.shapes.space3d import *
p1 = Point3D(1.0, 2.4, 0.2) # definition of a PPoint3Doint instance
p2 = Point3D(0.9, 4.2, 10.5)
p1.distance(p2) # 3D distance between two points
pl1 = CPlane3D.fromPoints(Point3D(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: 1. Introduction
Step2: We import all classes/methods from the geometry sub-module
Step3: 2. Basic spatial data types
Step4: When considering ... |
11,544 | <ASSISTANT_TASK:>
Python Code:
# Useful Functions
def mode(l):
# Count the number of times each element appears in the list
counts = {}
for e in l:
if e in counts:
counts[e] += 1
else:
counts[e] = 1
# Return the elements that appear the most times
... | <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: Exercise 1
Step3: b. Mean of returns
Step4: Exercise 2
Step5: b. Median of the returns.
Step6: Exercise 3
Step7: b. Mode of... |
11,545 | <ASSISTANT_TASK:>
Python Code:
import warnings
from sklearn.exceptions import ConvergenceWarning
warnings.filterwarnings("ignore", category=ConvergenceWarning)
import itertools
import time
import numpy as np
import pandas as pd
from sklearn import model_selection
from sklearn import linear_model
from sklearn import met... | <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: Prepare Data
Step2: Prepare Hyperparameters
Step3: Run Validation
Step4: Pick the best hyperparameters and train the full data
Step5: Calcul... |
11,546 | <ASSISTANT_TASK:>
Python Code:
#Verify we are in the lesson1 directory
%pwd
%matplotlib inline
import os, sys
sys.path.insert(1, os.path.join(sys.path[0], '../utils'))
from utils import *
from vgg16 import Vgg16
from PIL import Image
from keras.preprocessing import image
from sklearn.metrics import confusion_matrix
cu... | <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: Note
Step2: Create validation set and sample
Step3: This was original output
Step4: Training & 10% for Validation numbers
Step5: Finetuning ... |
11,547 | <ASSISTANT_TASK:>
Python Code:
## only needed for plotting in a jupyter notebook.
%matplotlib inline
## Code Block 1
import copy
import numpy as np
from matplotlib import pyplot as plt
from landlab import imshow_grid
from landlab.components import OverlandFlow, FlowAccumulator
from landlab.io import read_esri_ascii
##... | <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: Now we import the data for the watershed we want to route flow on. You will want to change this code block for the different scenarios. Initiall... |
11,548 | <ASSISTANT_TASK:>
Python Code:
import o2sclpy
import matplotlib.pyplot as plot
import numpy
import sys
plots=True
if 'pytest' in sys.modules:
plots=False
link=o2sclpy.linker()
link.link_o2scl()
fc=o2sclpy.find_constants(link)
ħc=fc.find_unique('ħc','MeV*fm')
print('ħc = %7.6e\n' % (ħc))
cu=link.o2scl_settings.ge... | <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: Link the O$_2$scl library
Step2: Get the value of $\hbar c$ from an O$_2$scl find_constants object
Step3: Get a copy (a pointer to) the O$_2$s... |
11,549 | <ASSISTANT_TASK:>
Python Code:
import stable_baselines
stable_baselines.__version__
import os
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import gym
from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import 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: Import Policy, RL agent, ...
Step3: Define a Callback Function
Step4: Create and wrap the environment
Step5: Define and train the PPO agent
S... |
11,550 | <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 = (0, 10)
DON'T MODIFY ... | <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... |
11,551 | <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... | <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: Ragged Tensors
Step2: Overview
Step3: There are also a number of methods and operations that are
Step4: And just like normal tensors, you can... |
11,552 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
%matplotlib inline
import plotly.tools as tls
import plotly.plotly as py
import cufflinks as cf
import plotly
plotly.offline.init_notebook_mode()
cf.offline.go_offline()
df = 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: The previous import code requires that you have pandas, numpy and matplotlib installed. If you are using conda
Step2: Import data file with pan... |
11,553 | <ASSISTANT_TASK:>
Python Code:
#--- Libraries
import pandas as pd # stats packages
import numpy as np # linear algebra packages
import matplotlib.pyplot as plt # ploting packages
import seaborn as sns # more plotting routines
from scipy.stats import beta # funti... | <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: Example
Step2: Extended sampling
|
11,554 | <ASSISTANT_TASK:>
Python Code:
# %load partSix.py
# Neural Networks Demystified
# Part 6: Training
#
# Supporting code for short YouTube series on artificial neural networks.
#
# Stephen Welch
# @stephencwelch
## ----------------------- Part 1 ---------------------------- ##
import numpy as np
# X = (hours sleeping, 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: ✅ DO THIS
Step2: 2. Modify code to be more flexible
Step3: 3. Use our ANN on the "Digits" dataset.
Step5: The following is copied and p... |
11,555 | <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... | <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: tf.function으로 성능 향상하기
Step2: 에러 출력을 위한 헬퍼 함수를 정의합니다
Step3: 기초
Step4: 다른 함수 내부에 사용할 수 있습니다.
Step5: tf.function은 즉시 실행 모드 보다 빠릅니다. 특히 그래프에 작은 ... |
11,556 | <ASSISTANT_TASK:>
Python Code:
#Load the necessary modules
from mechanize import Browser
import pandas as pd
from IPython.core.display import HTML
import requests
def extract_sub_string(string, start, finish):
extract a substring between the 'start' substring and the first occurence of 'finish' substring afte... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: We need a function to parse the HTML data after extracting the result.
Step3: Now we extract the result pages against each of the id(1 to 66000... |
11,557 | <ASSISTANT_TASK:>
Python Code:
from nipype import Function
def square_func(x):
return x ** 2
square = Function(["x"], ["f_x"], square_func)
square.run(x=2).outputs.f_x
from nipype import MapNode
square_node = MapNode(square, name="square", iterfield=["x"])
square_node.inputs.x = [0, 1, 2, 3]
square_node.run().out... | <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 see that this function just takes a numeric input and returns its squared value.
Step2: What if we wanted to square a list of numbers? We co... |
11,558 | <ASSISTANT_TASK:>
Python Code:
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_... | <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: Flower power
Step2: ConvNet Codes
Step3: Below I'm running images through the VGG network in batches.
Step4: Building the Classifier
Step5: ... |
11,559 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
# standard imports
import numpy as np
import matplotlib.pyplot as plt
import skrf as rf
rf.stylely()
P_f = 1 # forward power in Watt
Z = 50 # source internal impedance, line characteristic impedance and load impedance
L = 10 # line length in [m]
freq = rf.Frequency(... | <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: A simple transmission line
Step2: Assuming the source generates an input power of $P_f$ with a phase $\phi$, with such a line the voltage and c... |
11,560 | <ASSISTANT_TASK:>
Python Code:
import seaborn as sns
import metapack as mp
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from IPython.display import display
from publicdata.chis import *
%matplotlib inline
sns.set_context('notebook')
idx = pd.IndexSlice # Convenience redefinition.
#pkg = mp.j... | <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: RASP Diabetes Rates
Step3: Poverty, Age and Race
Step4: Compare to CHIS
Step5: AskCHIS, By Race, 55-64
Step6: AskCHIS, By Race, 55-64, Male
... |
11,561 | <ASSISTANT_TASK:>
Python Code:
# Create a list of countries, then print the results
allies = ['USA','UK','France','New Zealand',
'Australia','Canada','Poland']; allies
# Print the length of the list
len(allies)
# Add an item to the list, then print the results
allies.append('China'); allies
# Sort list, then ... | <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: Tuples
Step2: Dictionaries
Step3: Sets
|
11,562 | <ASSISTANT_TASK:>
Python Code:
### Imports
from smact import Element, element_dictionary, ordered_elements
from smact.screening import smact_filter
from datetime import datetime
import itertools
import multiprocessing
all_el = element_dictionary() # A dictionary of all element objects
# Say we are just interested in... | <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 define the elements we are interested in
Step2: We will investiage ternary M1-M2-O combinations exhaustively, where M1 and M2 are different ... |
11,563 | <ASSISTANT_TASK:>
Python Code:
import em1ds as zpic
#v_the = 0.001
v_the = 0.02
#v_the = 0.20
electrons = zpic.Species( "electrons", -1.0, ppc = 64, uth=[v_the,v_the,v_the])
sim = zpic.Simulation( nx = 500, box = 50.0, dt = 0.0999/2, species = electrons )
sim.filter_set("sharp", ck = 0.99)
#sim.filter_set("gaussian", c... | <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 run the simulation up to a fixed number of iterations, controlled by the variable niter, storing the value of the EM field $E_z$ at every tim... |
11,564 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.__version__
__author__ = "kyubyong. kbpark.linguist@gmail.com. https://github.com/kyubyong"
x = np.array([0., 1., 30, 90])
print "sine:", np.sin(x)
print "cosine:", np.cos(x)
print "tangent:", np.tan(x)
x = np.array([-1., 0, 1.])
print "inverse sine:", np.arcsin(x2... | <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: Trigonometric functions
Step2: Q2. Calculate inverse sine, inverse cosine, and inverse tangent of x, element-wise.
Step3: Q3. Convert angles f... |
11,565 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df=pd.read_csv('https://raw.githubusercontent.com/'
'sassoftware/sas-viya-programming/master/data/cars.csv')
df.head(10)
df.dtypes
df[['MSRP','Horsepower']].describe()
df.mean()
subdf=df[['Make','Model','Horsepower']]
subdf.head(15)
df=df.set_index... | <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: The function that reads CSV files into DataFrames is called read_csv. In the simplest form, you supply it with a filename or URL. The cars data ... |
11,566 | <ASSISTANT_TASK:>
Python Code:
if 2 + 3 == 5:
x = 5 + 3
mensaje = "Verdadero!"
else:
x = 5 - 3
mensaje = "Falso!"
print(x)
print(mensaje)
type(True)
type(5)
type(3.1416)
lista_vacia = []
print(lista_vacia)
#O equivalentemente
lista_vacia = list()
print(lista_vacia)
semana = ["Lunes", "Martes", ... | <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: De esta manera, Python estandariza el aspecto del código desde la definición del lenguaje.
Step2: Los operadores para variables booleanas son s... |
11,567 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Joan Massich <mailsik@gmail.com>
#
# License: BSD Style.
import os.path as op
import mne
from mne.channels.montage import get_builtin_montages
from mne.datasets import fetch_fsaverage
from mne.viz import set_3d_title, ... | <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: Check all montages against a sphere
Step2: Check all montages against fsaverage
|
11,568 | <ASSISTANT_TASK:>
Python Code::
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(ds.data, ds.target, test_size = 0.20)
<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:
|
11,569 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from semana2_datos import *
X_1 = np.array([[1,x] for x, y in data_1])
Y_1 =... | <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: Dataset
Step2: Gráficos
Step3: Modelo a partir de la ecuación normal de mínimos cuadrados
Step4: Ahora, graficamos la recta contra los datos ... |
11,570 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
pd?
pd.Categorical
cdr = pd.read_excel('data/CDR_data.xlsx', dtype={0:str, 1:str})
cdr.head()
cdr["Direction"].value_counts()
cdr.loc[cdr["Direction"] == "Incoming", "Dir"] = "->"
cdr.loc[cdr["Direction"] == "Missed", "Dir"] = "-X"
cdr['Call'] = cdr['Out'] + cdr['Di... | <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: Interaktive Hilfe
Step2: Die weitere Funktionalität der Pandas-Bibliothek können wir erkunden, indem wir die Methoden von Pandas ansehen. Dazu ... |
11,571 | <ASSISTANT_TASK:>
Python Code:
import spot
from spot.seminator import seminator
from spot.jupyter import display_inline
import buddy
spot.setup(show_default=".n")
aut1 = spot.automaton(HOA: v1
States: 3 Start: 0 AP: 1 "a"
Acceptance: 1 Inf(0) --BODY--
State: 0 [0] 0 [!0] 1 [0] 2
State: 1 [!0] 1 [0] 0 {0}
State: 2 [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:
Step2: We first create an example automaton using HOA.
Step3: The following semi-deterministic automata demonstrate three strategies to "cut", i.e., w... |
11,572 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import graphviz
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error as mse
from sklearn.model_selection import cross_val_score
from sklearn.model_selectio... | <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 preprocessing
Step2: Regression tree
Step3: Randomly defined train and test set
Step4: Know, we want to define the max_depht parameter t... |
11,573 | <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')
plt.plot(df.index,df['Grit'])
df.head(10)
df.mean()
pd.to_numeric(df, errors='ignore')
y=df["Age"]
z=df["Gri... | <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: Who has Grit?
Step2: AHHH
Step3: This is the way my quantitative data looks. Most of the column headers are self explanatory, but i'll go into... |
11,574 | <ASSISTANT_TASK:>
Python Code:
import iris
import numpy as np
a1b = iris.load_cube(iris.sample_data_path('A1B_north_america.nc'))
e1 = iris.load_cube(iris.sample_data_path('E1_north_america.nc'))
print(e1.summary(True))
print(a1b)
scenario_difference = a1b - e1
print(scenario_difference)
#
# edit space for user code
... | <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: 6.1 Cube Arithmetic<a id='arithmetic'></a>
Step2: Notice that the resultant cube's name is now unknown. Also, the resultant cube's attributes ... |
11,575 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
# From Python lists or iterators
n1 = np.array( [0,1,2,3,4,5,6] )
n2 = np.array( range(6) )
# Using numpy iterators
n3 = np.arange( 10, 20, 0.1)
n3
<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:
Step1: Vector creation
|
11,576 | <ASSISTANT_TASK:>
Python Code:
import loman
comp = loman.Computation()
holdings = Type,Symbol,Qty,CostBasis
Equity,AVGO,126,22680
Equity,EVHC,349,22685
Equity,STT,287,22673
Equity,DAL,454,22700
Equity,DY,283,22640
Future,ESM7,-1,0
Cash,USD,2000,
comp.add_node('holdings', value=holdings)
from io import StringIO
import... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: The first thing we shall need is holdings data. For this example, we assume that holdings data is provided in a CSV format, and insert that CSV ... |
11,577 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from numba import jit
import math
import dbscanf2py # Import the extension module file dbscanf2py.so
# A pure python funcion
def sum_0(arr):
M, N = arr.shape
result = 0
for i in range(M):
for j in ... | <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: Sum function
Step2: Let's %timeit
Step3: Factorial functions
Step4: Let's %timeit
Step7: Dbscan clustering algorithm
Step8: Dbscan with F2P... |
11,578 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_boston
bunch = load_boston()
print(bunch.DESCR)
X, y = pd.DataFrame(data=bunch.data, columns=bunch.feature_names.astype(str)), bunch.target
X.head()
SEED = 22
np.random.seed = SEED
from sklearn.model_selection import train_test_split
X_train, X_test, 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: Зафиксируем генератор случайных чисел для воспроизводимости
Step2: Домашка!
Step3: Измерять качество будем с помощью метрики среднеквадратично... |
11,579 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd # For data frames.
import matplotlib.pyplot as plt # For plotting.
from skidl.pyspice import * # For describing circuits and interfacing to ngspice.
!ls -F ~/tmp/skywater-pdk/libraries/sky130_fd_pr/latest/cells/
!ls -F ~/tmp/skywater-pdk/libraries/... | <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 Skywater PDK
Step2: For my purposes, I only needed a simple NFET and PFET to build some logic gates. I figured 1.8V versions of the... |
11,580 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division, print_function
%matplotlib inline
#path = "data/state/"
path = "data/state/sample/"
from importlib import reload # Python 3
import utils; reload(utils)
from utils import *
from IPython.display import FileLink
batch_size=64
batches = get_batches(path+'tra... | <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: Setup batches
Step2: Rather than using batches, we could just import all the data into an array to save some processing time. (In most examples... |
11,581 | <ASSISTANT_TASK:>
Python Code:
def search(start, goal, next_states):
Frontier = { start }
Visited = set()
Parent = { start: start }
while Frontier:
NewFrontier = set()
for s in Frontier:
for ns in next_states(s):
if ns not in Visited and ns not in Frontier:... | <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: Given a state and a parent dictionary Parent, the function path_to returns a path leading to the given state.
Step2: Display Code
Step3: The f... |
11,582 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from numba import vectorize, jit, float64
from quantecon.util import tic, toc
import matplotlib.pyplot as plt
α = 4
n = 200
x = np.empty(n)
x[0] = 0.2
for t in range(n-1):
x[t+1] = α * x[t] * (1 - x[t])
plt.plot(x)
plt.show()
def quad(x0, n):
x = x0
... | <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: Problem 1
Step2: Here's a typical time series
Step3: Here's a function that simulates for n periods, starting from x0, and returns only the fi... |
11,583 | <ASSISTANT_TASK:>
Python Code:
#$HIDE_INPUT$
import pandas as pd
ks = pd.read_csv('../input/kickstarter-projects/ks-projects-201801.csv',
parse_dates=['deadline', 'launched'])
ks.head(6)
print('Unique values in `state` column:', list(ks.state.unique()))
# Drop live projects
ks = ks.query('state != "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: The state column shows the outcome of the project.
Step2: Using this data, how can we use features such as project category, currency, funding ... |
11,584 | <ASSISTANT_TASK:>
Python Code:
# Necessary imports
import time
from IPython import display
import numpy as np
from matplotlib.pyplot import imshow
from PIL import Image, ImageOps
import tensorflow as tf
%matplotlib inline
from tensorflow.examples.tutorials.mnist import input_data
# Read the mnist dataset
mnist = input_... | <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: Our feed forward neural network will look very similar to our softmax classifier. However, now we have multiple layers and non-linear activation... |
11,585 | <ASSISTANT_TASK:>
Python Code:
import shap
import transformers
import nlp
import torch
import numpy as np
import scipy as sp
# load a BERT sentiment analysis model
tokenizer = transformers.DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")
model = transformers.DistilBertForSequenceClassification.from_pr... | <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: Single instance text plot
Step2: Multiple instance text plot
Step3: Summarizing text explanations
Step4: Note that how you summarize the impo... |
11,586 | <ASSISTANT_TASK:>
Python Code:
# These are the libraries will be useing for this lab.
import torch
import matplotlib.pylab as plt
import torch.functional as F
# Create a tensor x
x = torch.tensor(2.0, requires_grad = True)
print("The tensor x: ", x)
# Create a tensor y according to y = x^2
y = x ** 2
print("The resu... | <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: <!--Empty Space for separating topics-->
Step2: Then let us create a tensor according to the equation $ y=x^2 $.
Step3: Then let us take the d... |
11,587 | <ASSISTANT_TASK:>
Python Code:
print(__doc__)
import numpy as np
np.random.seed(1234)
import matplotlib.pyplot as plt
from skopt.space import Space
from skopt.sampler import Sobol
from skopt.sampler import Lhs
from skopt.sampler import Halton
from skopt.sampler import Hammersly
from skopt.sampler import Grid
from scipy... | <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: Random sampling
Step2: Sobol
Step3: Classic latin hypercube sampling
Step4: Centered latin hypercube sampling
Step5: Maximin optimized hyper... |
11,588 | <ASSISTANT_TASK:>
Python Code:
%%bash
pull_force_overwrite_local
%%html
<iframe width=800 height=600 src="http://pipeline.io"></iframe>
import requests
url = 'http://169.254.169.254/computeMetadata/v1/instance/network-interfaces/0/access-configs/0/external-ip'
headers = {'Metadata-Flavor': 'Google'}
r = requests.get(... | <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: PipelineAI
Step2: All Code in GitHub Repo
Step3: Get Allocation Index
Step4: Helper Scripts
Step5: Find Script from Anywhere
Step6: Show pu... |
11,589 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import edward as ed
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from edward.models import Normal
plt.style.use('ggplot')
def build... | <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: First, simulate a toy dataset of 50 observations with a cosine relationship.
Step2: Next, define a two-layer Bayesian neural network. Here, we ... |
11,590 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import pinkfish as pf
import strategy
# format price data
pd.options.display.float_format = '{:0.2f}'.format
%matplotlib inline
# set size of inline plots
'''note: rcParams can't be in same cell as import matplotlib
or... | <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: Some global data
Step2: Run Strategy
Step3: View logs
Step4: Generate strategy stats - display all available stats
Step5: Equity curve
Step6... |
11,591 | <ASSISTANT_TASK:>
Python Code:
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
trainset = torchvision.datasets.MNIST(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.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: Define a Neural Network
Step2: Define a Loss function and optimizer
Step3: Train the network
Step4: Test the network on the test data
Step5: ... |
11,592 | <ASSISTANT_TASK:>
Python Code:
# Import statements
from pymatgen import Structure, Lattice, MPRester, Molecule
from pymatgen.analysis.adsorption import *
from pymatgen.core.surface import generate_all_slabs
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from matplotlib import pyplot as plt
%matplotlib inline... | <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 create a simple fcc structure, generate it's distinct slabs, and select the slab with a miller index of (1, 1, 1).
Step2: We make an instanc... |
11,593 | <ASSISTANT_TASK:>
Python Code:
conda update --all
conda create -n fauenv python=3
conda info -e
activate fauenv
conda install -n fauenv numpy scipy matplotlib scikit-learn scikit-image ipython ipython-notebook
conda install -n fauenv nose pip anaconda-client pillow ujson flask jinja2 natsort joblib numba pyside
... | <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: A new environment capsule with preset libraries installed for one or more of your projects can be created. fauenv is the name of the new python ... |
11,594 | <ASSISTANT_TASK:>
Python Code:
print x
type(x)
y=np.ones((2,3))
print y
z=np.arange(2,8,1)
alpha=np.reshape(z,(3,2))
print alpha
beta= np.random.randn(3,4)
print beta
gamma=beta*2.0
print gamma
a=[3,4,5]
a=np.array(a)
type(a)
a=np.random.randint(0,10,(2,3))
b=np.random.randint(0,10,(2,3))
print a
print b
print "eleme... | <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: I need a matrix like this
Step2: np array operator
Step3: Sliccing
Step4: Goto opencv/build/python/2.7 folder.
|
11,595 | <ASSISTANT_TASK:>
Python Code:
work_directory_name = 'kubeflow'
! mkdir -p $work_directory_name
%cd $work_directory_name
## Download kfctl v0.7.0
! curl -LO https://github.com/kubeflow/kubeflow/releases/download/v0.7.0/kfctl_v0.7.0_linux.tar.gz
## Unpack the tar ball
! tar -xvf kfctl_v0.7.0_linux.tar.gz
## Creat... | <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 kfctl
Step2: If you are using AI Platform Notebooks, your environment is already authenticated. Skip the following cell.
Step3: Set u... |
11,596 | <ASSISTANT_TASK:>
Python Code:
from pomegranate import *
import numpy as np
%pylab inline
rigged = State( DiscreteDistribution({'H': 0.8, 'T': 0.2}), name="rigged" )
unrigged = State( DiscreteDistribution({'H': 0.5, 'T': 0.5}), name="unrigged" )
non_cheater = HiddenMarkovModel( name="non-cheater" )
non_cheater.add_st... | <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: Let's start by creating our hmm's. Since this is a simple example, we will only have 2 states, rigged and unrigged, with discrete distributions.... |
11,597 | <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... | <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: TFX — Running a simple pipeline manually in a Colab Notebook
Step2: Import packages
Step3: Check the versions
Step4: Download example data
St... |
11,598 | <ASSISTANT_TASK:>
Python Code:
## Fake dataset:
class Sampler:
def __init__(self, prob_table):
total_prob = 0.0
if type(prob_table) is dict:
for key, value in prob_table.items():
total_prob += value
elif type(prob_table) is list:
prob_table_gen = {}
... | <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: Parts of Speech
Step2: Simple Grammar
Step4: Utilities
Step5: Create a Mapping from numbers to words
Step6: To send our sentences in one big... |
11,599 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%load_ext autoreload
%autoreload 2
import os, sys, time
import pickle as pkl
import numpy as np
import pandas as pd
from sklearn.base import BaseEstimator
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OneVsRestClassifier
from sklearn... | <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: Load data.
Step2: Feature normalisation.
Step3: Naive baseline
Step6: Binary relevance baseline
Step7: Cross validation according to F1.
Ste... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.