Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
7,900 | <ASSISTANT_TASK:>
Python Code:
last_match_winner = defaultdict(int)
dataset['HomeTeamWonLast'] = 0
for index,row in dataset.iterrows():
home_team = row['home']
visitor_team = row['visitor']
teams = tuple(sorted([home_team,visitor_team]))
row['HomeTeamWonLast'] = 1 if last_match_winner[teams] == home_tea... | <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: 特征数增加后，决策
Step2: 决策树可以用这些特征值进行训练，但DecisionTreeClassifier仍把它们当作连续型特
Step3: 使用随机森林
|
7,901 | <ASSISTANT_TASK:>
Python Code:
# Import modules
import numpy as np
# Import PySwarms
import pyswarms as ps
# Some more magic so that the notebook will reload external python modules;
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
%load_ext autoreload
%autoreload 2
def distance(query,... | <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: IK as an Optimization Problem
Step2: We are going to use the distance function to compute the cost, the further away the more costly the positi... |
7,902 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import os
import pandas as pd
import datetime as dt
import matplotlib.pyplot as plt
import torch
from torch.utils.data import DataLoader
from deep4cast.forecasters import Forecaster
from deep4cast.models import WaveNet
from deep4cast.datasets import TimeSeriesDataset
im... | <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: Divide into train and test
Step3: We've also found that it is not necessary to train on the full dataset, so we here select a 1... |
7,903 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
def strip_parentheses(col, df):
'''
splits single column strings of "value (error)" into two columns of value and error
input:
-string name of column to split 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: The tables define the value and error as a string
Step2: Table 1 - Basic data on sources
Step3: Table 3- IRAC photometry
Step4: Convert spect... |
7,904 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
df = pd.read_csv('kyphosis.csv')
df.head()
sns.pairplot(df,hue='Kyphosis',palette='Set1')
from sklearn.model_selection import train_test_split
X = df.drop('Kyphosis',axis=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: Get the Data
Step2: EDA
Step3: Train Test Split
Step4: Decision Trees
Step5: Prediction and Evaluation
Step6: Tree Visualization
Step7: Ra... |
7,905 | <ASSISTANT_TASK:>
Python Code:
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import scipy.stats as st
from sci_analysis import analyze
%matplotlib inline
import numpy as np
import scipy.stats as st
from sci_analysis import analyze
np.random.seed(987654321)
data = st.norm.rvs(size=1000)
analyze(... | <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 will tell python to import the sci-analysis function analyze().
Step2: Now, sci-analysis should be ready to use. Try the following code
St... |
7,906 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import active
import experiment
import logistic_regression as logr
from sklearn import datasets # The Iris dataset is imported from here.
from IPython.display import display
import matplotlib.pyplot as plt
%matplotlib inline
%load_ext autoreload
%autoreload 1
%aimport 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: Importing and processing the Iris data set
Step2: Experimental procedure
Step3: The experiment
|
7,907 | <ASSISTANT_TASK:>
Python Code:
upload_dir = './sketch'
import boto
runThis = 0
if runThis:
conn = boto.connect_s3()
b = conn.create_bucket('sketchpad_basic_pilot2_sketches')
all_files = [i for i in os.listdir(upload_dir) if i != '.DS_Store']
for a in all_files:
print a
k = b.new_key(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: build stimulus dictionary
Step2: upload stim dictionary to mongo (db = 'stimuli', collection='sketchpad_basic_recog')
Step4: crop 3d objects
|
7,908 | <ASSISTANT_TASK:>
Python Code:
width = 20
height = 5*9
width * height
tax = 8.25 / 100
price = 100.50
price * tax
price + _
round(_, 2)
print('spam email')
# This would cause error
print('doesn't')
# One way of doing it correctly
print('doesn\'t')
# Another way of doing it correctly
print("doesn't")
print('''
Usag... | <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: Calculator
Step2: Strings
Step3: show ' and " in a string
Step4: span multiple lines
Step5: slice and index
Step6: Index in the Python way
... |
7,909 | <ASSISTANT_TASK:>
Python Code:
NAME = "Michelle Appel"
NAME2 = "Verna Dankers"
NAME3 = "Yves van Montfort"
EMAIL = "michelle.appel@student.uva.nl"
EMAIL2 = "verna.dankers@student.uva.nl"
EMAIL3 = "yves.vanmontfort@student.uva.nl"
%pylab inline
plt.rcParams["figure.figsize"] = [20,10]
def true_mean_function(x):
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: Lab 3
Step2: Part 1
Step3: 1. Sampling from the Gaussian process prior (30 points)
Step4: 1.2 computeK( X1, X2, thetas ) (10 points)
Step5: ... |
7,910 | <ASSISTANT_TASK:>
Python Code:
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
from gensim import corpora, models, similarities
dictionary = corpora.Dictionary.load('/tmp/deerwester.dict')
corpus = corpora.MmCorpus('/tmp/deerwester.mm') # comes from the first ... | <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: Similarity Interface
Step2: To follow Deerwester’s example, we first use this tiny corpus to define a 2-dimensional LSI space
Step3: Now suppo... |
7,911 | <ASSISTANT_TASK:>
Python Code:
import h2o
import imp
from h2o.estimators.kmeans import H2OKMeansEstimator
# Start a local instance of the H2O engine.
h2o.init();
iris = h2o.import_file(path="https://github.com/h2oai/h2o-3/raw/master/h2o-r/h2o-package/inst/extdata/iris_wheader.csv")
iris.describe()
try:
imp.find_... | <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 next step of using H2O is to parse and load data into H2O's in-memory columnar compressed storage. Today we will be using the Iris flower d... |
7,912 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
# returns a random d dimensional vector, a direction to peturb in
def direction(d,t):
# if type == uniform
if(t == 'u'):
return np.random.uniform(-2/np.sqrt(d), 2/np.sqrt(d), d)
elif(t == 'n'):
return np.random.normal(0, 1/np.sqrt(d), 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: So from the histograms above we can see all these methods give us points on the unit sphere. (Uniform gives us almost) But are they all uncorrel... |
7,913 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from cStringIO import StringIO
import matplotlib.pyplot as plt
import caffe
from IPython.display import clear_output, Image, display
import cv2
import PIL.Image
import os
os.chdir("start_deep/")
caffe.set_mode_cpu()
caffe.set_device(0)
caffe.set_mode_gpu()
net = caff... | <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: Caffe computation mode
Step2: GPU
Step3: Network loading and tests
Step4: The cell bellows checks that opencv and its python bindings are pro... |
7,914 | <ASSISTANT_TASK:>
Python Code:
class Node :
def __init__(self , data ) :
self . data = data
self . next = None
def fun1(head ) :
if(head == None ) :
return
fun1(head . next )
print(head . data , end = "▁ ")
def fun2(start ) :
if(start == None ) :
return
print(start . data , end =... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
7,915 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
sns.set_style('white')
from scipy.interpolate import griddata
from scipy.interpolate import interp1d
from scipy.interpolate import interp2d
xb=np.array([-5,-4,-3,-2,-1,0,1,2,3,4,5])
yb=np.array([-... | <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: Sparse 2d interpolation
Step2: The following plot should show the points on the boundary and the single point in the interior
Step3: Use meshg... |
7,916 | <ASSISTANT_TASK:>
Python Code:
# Import all necessary libraries, this is a configuration step for the exercise.
# Please run it before the simulation code!
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
# Show the plots in the Notebook.
plt.switch_backend("nbagg")
# Initial... | <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. Initialization of setup
Step2: 2. Finite Volumes setup
Step3: 3. Initial condition
Step4: 4. Solution for the scalar advection problem
|
7,917 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import random, datetime
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import statsmodels.api as sm
from scipy.stats import norm
from scipy.stats.stats import pearsonr
# str, int, float
str(3)
"chengjun wang"
# int
int('5')
# float
float('7.1')
ra... | <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: Variable Type
Step2: dir & help
Step3: type
Step4: Data Structure
Step5: 定义函数
Step6: For 循环
Step7: map
Step8: if elif else
Step9: while循... |
7,918 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
% matplotlib inline
from thinkbayes2 import Hist, Pmf, Suite
pmf = Pmf()
for x in [1,2,3,4,5,6]:
pmf[x] = 1
pmf.Print()
pmf.Normalize()
pmf.Print()
pmf = Pmf([1,2,3,4,5,6])
pmf.Print()
pmf.Prob(1)
pmf[1]
pmf = Pmf()
pmf['Bowl... | <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 Pmf class
Step2: To be true probabilities, they have to add up to 1. So we can normalize the Pmf
Step3: The return value from Normalize i... |
7,919 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
%matplotlib inline
import openpathsampling as paths
import numpy as np
old_store = paths.Storage("mstis_bootstrap.nc", mode='r')
print("PathMovers: "+ str(len(old_store.pathmovers)))
print("Samples: " + str(len(old_store.samples)))
print("Ensembles... | <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: Setting up the simulation
Step2: A lot of information can be recovered from the old storage, and so we don't have the recreate it. However, we ... |
7,920 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
a = np.array([1,2,3,4,5,6])
# Print the contents of a
a
print("The vector a has " + str(a.ndim) + " dimension(s) and has the shape " + str(a.shape) + ".")
m = np.array([[1,2,3], [4,5,6]])
m
print("The matrix m has " + str(m.ndim) + " dimension(s) and has the shape "... | <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: Make a vector with 6 elements
Step2: Get some information about the vector
Step3: Create a matrix like this
Step4: Get some information about... |
7,921 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import YouTubeVideo
YouTubeVideo('Ti5zUD08w5s')
YouTubeVideo('jmsFC0mNayM')
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
def parab(x):
return x**2
x = np.linspace(0,1)
y = parab(x)
plt.fill_between(x,y)
plt.text(0.8,0.2,'$\mathcal{D}$',fo... | <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: Integración Montecarlo tipo 1
Step2: Entonces, lo que queremos es aproximar el área de la región $\mathcal{D}$. Llamaremos esta área $A(\mathca... |
7,922 | <ASSISTANT_TASK:>
Python Code:
# Author: Ivana Kojcic <ivana.kojcic@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# Kostiantyn Maksymenko <kostiantyn.maksymenko@gmail.com>
# Samuel Deslauriers-Gauthier <sam.deslauriers@gmail.com>
# License: BSD-3-Clause
import os.path as op
import numpy as ... | <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 order to simulate source time courses, labels of desired active regions
Step3: Create simulated source activity
Step4: Here,
Step5: Simul... |
7,923 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
my_list = [2, 5, 7, 8]
my_list
type(my_list)
multi_list = [[1, 2, 3], [4, 5, 6]]
#
my_array = np.array(my_list)
type(my_array)
my_array.dtype
multi_array.shape
multi_array = np.array([[1, 2, 3], [4, 5, 6]], np.int32)
#
#
#
# Pandas DataFrames as table elements
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: Lists in native Python
Step2: This list is one-dimensional, let's make it multidimensional!
Step3: How do we access the 6 element in the secon... |
7,924 | <ASSISTANT_TASK:>
Python Code:
empty_dictionary = {}
print empty_dictionary
filled_dictionary = {'first_name': 'abhinav', 'last_name': 'upadhyay'}
print filled_dictionary
food_menu = {}
food_menu['pizza'] = 300
food_menu['sandwich'] = 30
food_menu['tea'] = 10
print food_menu
pizza_price = food_menu['pizza']
print 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: Creating dictionary with values
Step2: Adding values to the dictionary
Step3: Notice the ordering of the items in the output above
Step4: Del... |
7,925 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%matplotlib notebook
from threeML import *
import os
trigger="GRB110731A"
dec=-28.546
ra=280.52
xrt_dir='xrt'
xrt = SwiftXRTLike("XRT",pha_file=os.path.join(xrt_dir,"xrt_src.pha"),
bak_file=os.path.join(xrt_dir,"xrt_bkg.pha"),
rsp_... | <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 XRT data
Step2: Load GBM data
Step3: View the light curve
Step4: Make energy selections and check them out
Step5: Setup the model
Step6... |
7,926 | <ASSISTANT_TASK:>
Python Code:
# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step3: Fast Style Transfer for Arbitrary Styles
Step4: Let's get as well some images to play with.
Step5: Import TF Hub module
Step6: The signature ... |
7,927 | <ASSISTANT_TASK:>
Python Code:
import datetime
import numpy as np
# Install and pin to versions that seem to work together
!pip3 install pandas-gbq==0.10.0 google-cloud-bigquery==1.11.2 google-api-core==1.8.2
!pip3 install matplotlib
# Inline all matplotlib plots
%matplotlib inline
from google.cloud import bigquery
# ... | <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: Kubeflow Stats
Step2: Compute cluster stats
Step3: Number of new deployments
Step4: Number of active deployments
Step5: Compute histogram of... |
7,928 | <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... |
7,929 | <ASSISTANT_TASK:>
Python Code:
def sat(f):
return f.cumsum(axis=1).cumsum(axis=0)
def satarea(sat,r0_c0,r1_c1):
a,b,c,d = 0,0,0,0
r0,c0 = r0_c0
r1,c1 = r1_c1
if ((r0 - 1 >= 0) and (c0 - 1 >= 0)):
a = sat[r0-1,c0-1]
if (r0 - 1 >= 0):
b = sat[r0-1,c1]
if (c0 - 1 >= 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: Examples
Step2: Numerical example
Step3: Image example
Step4: Calculating a rectangle area with SAT (Summed Area Table)
|
7,930 | <ASSISTANT_TASK:>
Python Code:
import pyquil.quil as pq
import pyquil.forest as forest
from pyquil.gates import *
qvm = forest.Connection()
p = pq.Program()
p.inst(X(0)).measure(0, 0)
print p
classical_regs = [0] # A list of which classical registers to return the values of.
qvm.run(p, classical_regs)
qvm.run(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: Next, we want to open a connection to the QVM.
Step2: Now we can make a program by adding some Quil instruction using the inst method on a Prog... |
7,931 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
import numpy
# Path for TubeTK libs and bin
#Values takend from TubeTK launcher
#sys.path.append("C:/src/TubeTK_Python_ITK/SlicerExecutionModel-build/GenerateCLP/")
#sys.path.append("C:/src/TubeTK_Python_ITK/SlicerExecutionModel-build/GenerateCLP/Release")
#sys.path.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: Initialization
Step2: Read the input images
Step3: STEP 1
Step4: STEP 2
Step5: STEP 3
Step6: STEP 4
Step7: STEP 5
Step8: STEP 6
Step9: S... |
7,932 | <ASSISTANT_TASK:>
Python Code:
%load_ext Cython
%%cython
import math
def erathostene_sieve(int n):
cdef list primes = [False, False] + [True] * (n - 1) # from 0 to n included
cdef int max_divisor = math.floor(math.sqrt(n))
cdef int i = 2
for divisor in range(2, max_divisor + 1):
if primes[divis... | <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 51
Step2: Let's try to obtain the examples given in the problem statement, with the smallest prime giving a 6-sized family being 13 and... |
7,933 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
def param_plot():
this function creates the graph on page 189 of Sargent Macroeconomic Theory, second edition, 1987
fig, ax = plt.subplots(figsize=(12, 8))
ax.set_aspect('equal')
# Set axis
xmin, ymin = -3, -2
xmax... | <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: OOP in Action
Step3: Explanation of the graph
Step5: none
Step7: Manual or “by hand” root calculations
Step9: none
Step10: none
Step11: no... |
7,934 | <ASSISTANT_TASK:>
Python Code:
from nltk.corpus import propbank
pb_instances = propbank.instances()
print(pb_instances)
inst = pb_instances[103]
print("File ID:", inst.fileid)
print("Sentence Number:", inst.sentnum)
print("Word Number:", inst.wordnum)
inst.tagger
inst.inflection
infl = inst.inflection
infl.form, infl.... | <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: Each propbank instance defines the following member variables
Step2: The location of the predicate and of the arguments are encoded using Propb... |
7,935 | <ASSISTANT_TASK:>
Python Code:
%gui
from PyQt5 import QtWidgets
b1 = QtWidgets.QPushButton("Click Me")
%gui qt5
from PyQt5 import QtWidgets
b1 = QtWidgets.QPushButton("Click Me")
b1.show()
def on_click_cb():
print("Clicked")
b1.clicked.connect(on_click_cb)
%connect_info
!jupyter kernel list
%qtconsole
b1.show()... | <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 pop up will appear saying
Step2:
Step3: Now, if you click the button, the callback will be called.
Step4: Now the Jupyter QtConsole will... |
7,936 | <ASSISTANT_TASK:>
Python Code:
import heapq
nums = [1, 8, 2, 23, 7, -4, 18, 23, 42, 37, 2]
print(heapq.nlargest(3, nums)) # Prints [42, 37, 23]
print(heapq.nsmallest(3, nums)) # Prints [-4, 1, 2]
portfolio = [
{'name': 'IBM', 'shares': 100, 'price': 91.1},
{'name': 'AAPL', 'shares': 50, 'price': 543.22... | <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 2
Step2: Example 2
|
7,937 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
def lin_regplot(X, y, model):
plt.scatter(X, y, c='blue')
plt.plot(X, model.predict(X), color='red')
return
X = np.array([ 1, 2, 3, 4, 5])[:, np.newaxis]
y = np.array([ 1, 2, 3, 4, 5])
ne_lr = LinearRegression(solver='nor... | <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: <br>
Step2: <br>
Step3: <br>
Step4: <br>
|
7,938 | <ASSISTANT_TASK:>
Python Code:
MU = 3.9
N = int(10E4)
INITIAL = 0.5
MIN_SIZE = 2
MAX_SIZE = 26
BITS_RANGE = array(list(range(MIN_SIZE, MAX_SIZE + 1)))
def generate(x, mu, n):
current = x
for _ in range(n):
yield current
current = mu * current * (1 - current)
def bin_to_dec(sequence, bits):
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: Generate a sequence
Step2: Entropies
Step3: Now we have mappings from binary block size to entropies of sequences
Step4: Now we will calculat... |
7,939 | <ASSISTANT_TASK:>
Python Code:
from utils import load_buzz, select, write_result
from features import featurize, get_pos
from containers import Questions, Users, Categories
%matplotlib inline
import numpy as np
from scipy import linalg
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn import mixtur... | <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: Right, now, you can use those module.
Step3: B. Modeling
Step4: n_iter=10
|
7,940 | <ASSISTANT_TASK:>
Python Code:
couleurs = ["rouge", "orange", "jaune", "vert", "bleu", "indigo", "violet"]
tailles = ["page", "homme", "demi patron", "patron", "grand patron"]
[(couleur, taille) for couleur in couleurs for taille in tailles]
couleurs_et_tailles = ((couleur, taille) for couleur in couleurs for taille 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: As you can see, it doesn't generate the complete list like the listcomp above. Genexps don't produce entire lists in memory. You need to iterate... |
7,941 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import YouTubeVideo
# WATCH THE VIDEO IN FULL-SCREEN MODE
YouTubeVideo("JXJQYpgFAyc",width=640,height=360) # Numerical integration
# Put your code here
import math
Nstep = 10
begin = 0.0
end = 3.1415926
dx = (end-begin)/Nstep
sum = 0.0
xpos = 0.0
for i in range(Nst... | <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: Question 1
Step2: Question 2
Step4: Question 3
|
7,942 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import mne
import os.path as op
from matplotlib import pyplot as plt
# Load an example dataset, the preload flag loads the data into memory now
data_path = op.join(mne.datasets.sample.data_path(), 'MEG',
'sample', 'sample_audvis_r... | <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: Continuous data is stored in objects of type
Step2: Information about the channels contained in the
Step3: You can also pass an index direct... |
7,943 | <ASSISTANT_TASK:>
Python Code:
from bs4 import BeautifulSoup
import requests
r = requests.get('https://en-marche.fr/emmanuel-macron/le-programme')
soup = BeautifulSoup(r.text, 'html.parser')
proposals = soup.find_all(class_='programme__proposal')
proposals = [p for p in proposals if 'programme__proposal--category' 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: On peut maintenant extraire le lien vers l'image.
Step2: On peut afficher ceci dans le notebook.
Step5: On peut répeter ce processus et faire ... |
7,944 | <ASSISTANT_TASK:>
Python Code:
# Importing pandas to read CSV file
import pandas as pd
# Read breast cancer csv file to pandas data frame data
data = pd.read_csv('wisconsin_breast_cancer.csv')
# Display the first 5 rows of the csv file
data.head()
data.shape # It is always a good idea to understand your data
# There ar... | <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: Delete rows with missing data
Step2: Getting ready to do classification
Step3: Now let us create a confusion matrix to identify sensitivity sp... |
7,945 | <ASSISTANT_TASK:>
Python Code:
MAX = 10005
MOD = 1000000007
def countNum(idx , sum , tight , num , len1 , k ) :
if(len1 == idx ) :
if(sum == 0 ) :
return 1
else :
return 0
if(dp[idx ][sum ][tight ] != - 1 ) :
return dp[idx ][sum ][tight ]
res = 0
if(tight == 0 ) :
limit = num[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:
|
7,946 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('/Users/c242587/Desktop/projects/git/ngboost')
from ngboost import NGBRegressor
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
X, Y = load_boston(True)
X_train, X_test,... | <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 estimated distributional parameters at a set of points is easy. This returns the predicted mean and standard deviation of the first ... |
7,947 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
%matplotlib inline
def clf_preds(accuracy, truth_vector):
# accuracy of classifier
# truth_vector is the actual value of the target
preds = []
for i in range(len(truth)):
pred = np.random.ch... | <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 Power of Ensembles
Step2: The accuracy of a majority-vote system from an ensemble of weak classifiers is a big improvement on the accuracy ... |
7,948 | <ASSISTANT_TASK:>
Python Code:
def caps(val):
caps returns double the value of the provided value
return val*2
a = caps("TEST ")
print(a)
print(caps.__doc__)
a = caps(1234)
print(a)
def is_valid(data):
if 10 in data:
return True
return False
a = is_valid([10, 200, 33, "asf"])
print(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: Functions
Step3: In the above example, we have caps as function, which takes val as argument and returns val * 2.
Step4: Functions can return ... |
7,949 | <ASSISTANT_TASK:>
Python Code:
# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
os.environ['TFVERSION'] = '1.13'
%%bash
gcloud config se... | <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 that we have the TensorFlow code working on a subset of the data, we can package the TensorFlow code up as a Python module and train it on C... |
7,950 | <ASSISTANT_TASK:>
Python Code:
def vol(rad):
pass
def ran_check(num,low,high):
pass
def ran_bool(num,low,high):
pass
ran_bool(3,1,10)
def up_low(s):
pass
def unique_list(l):
pass
unique_list([1,1,1,1,2,2,3,3,3,3,4,5])
def multiply(numbers):
pass
multiply([1,2,3,-4])
def palindrome(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: Write a function that checks whether a number is in a given range (Inclusive of high and low)
Step2: If you only wanted to return a boolean
Ste... |
7,951 | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.0,<2.1"
%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()
b['rpole@primary'] = 1.8
b['rpole@secondary'] = 0.96
b['teff@primary'] = 10000
b['grav... | <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: Let's make our system so ... |
7,952 | <ASSISTANT_TASK:>
Python Code:
probe_x_offset = 5 # i.e. probe is 5 mm to the right of the nozzle
probe_y_offset = -31 # i.e. probe is 31mm "down" from the nozzle
probe_z_offset = -22.5 # i.e. probe clicks with nozzle 22.5mm above the bed
min_y = 41
min_x = 5
max_y = 147 # Moving beyond this value after homing will cr... | <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: Display results
Step4: Need to remove the tilt from this. Get the best fit orthoganal distance regression plane using approach here
Step5: Wha... |
7,953 | <ASSISTANT_TASK:>
Python Code:
from random import choices
lnct_few_friends = ["Jyoti Pancholi", "Amit Shrivastava", "Mukesh Bansal", "Preeti Saraswat", "Manish Nandle"]
list_of_prob = [0.2, 0.1, 0.3, 0.2, 0.2]
lnct_few_friends = choices(lnct_few_friends, weights=list_of_prob, k=200)
for name in set(population):
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: Lets try some graphs on them
Step2: In the above graph, you can see that everytime, "X" were the lowest and "Mukesh" & "Manish" were the highes... |
7,954 | <ASSISTANT_TASK:>
Python Code:
# Make sure the base overlay is loaded
from pynq.overlays.base import BaseOverlay
base = BaseOverlay("base.bit")
from pynq.lib.arduino import Arduino_Analog
from pynq.lib.arduino import ARDUINO_GROVE_A1
from pynq.lib.arduino import ARDUINO_GROVE_A4
analog1 = Arduino_Analog(base.ARDUINO,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: 1. Instantiate individual analog controller
Step2: 2. Read voltage value out
Step3: 3. Read raw value out
Step4: 4. Logging multiple sample v... |
7,955 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
Image(url='http://www.phdcomics.com/comics/archive/phd101212s.gif')
%%bash
git status
<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: If that hasn't convinced you, here are some other benefits
|
7,956 | <ASSISTANT_TASK:>
Python Code:
from QGL import *
cl = ChannelLibrary("example")
q1 = cl["q1"]
# Repeat similar configuration for q2
q2 = cl.new_qubit("q2")
aps2_3 = cl.new_APS2("BBNAPS3", address="192.168.5.103")
aps2_4 = cl.new_APS2("BBNAPS4", address="192.168.5.104")
dig_2 = cl.new_X6("X6_2", address=0)
cl.set_con... | <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: See Auspex example notebooks on how to configure a channel library.
Step2: One can define simultaneous operations on qubits using the * operato... |
7,957 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from empiricaldist import Pmf
from utils import decorate
# set the random seed so we get the same results every time
np.random.seed(17)
# make the directory for the figures
import os
if not os.pat... | <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: Class size
Step3: I generate a sample from this distribution, assuming a uniform distribution in each range and an upper bound of 300.
Step4: ... |
7,958 | <ASSISTANT_TASK:>
Python Code:
from pyoptools.all import *
from numpy import pi
P1=Plane(shape=Circular(radius=(25)))
Plot3D(P1,center=(0,0,0),size=(60,60),rot=[(0,0,0)],scale=6)
P2=Plane(shape=Rectangular(size=(50,50)))
Plot3D(P2,center=(0,0,0),size=(60,60),rot=[(0,0,0)],scale=6)
P3=Plane(shape=Triangular(coord=((0,2... | <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: Plane Surface
Step2: Spherical Surface
Step3: Cylindrical Surface
Step4: The second class is the Cylindrical.
Step5: Aspherical Surface
|
7,959 | <ASSISTANT_TASK:>
Python Code:
sushi_order = ['unagi', 'hamachi', 'otoro']
prices = [6.50, 5.50, 15.75]
print(sushi_order)
print(prices)
print(sushi_order[0])
print(sushi_order[2])
print(len(sushi_order))
print(sushi_order[-3])
everyones_order = [['california roll'], ['unagi', 'dragon roll'], sushi_order]
print(eve... | <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 can access a single element in a list by indexing in using brackets. List indexing starts at 0 so to get the first element, you use 0, the s... |
7,960 | <ASSISTANT_TASK:>
Python Code:
import sys
import numpy as np
# the following line is not required if BatchFlow is installed as a python package.
sys.path.append("../..")
from batchflow import Dataset, DatasetIndex, Batch
# number of items in the dataset
NUM_ITEMS = 10
# number of items in a batch when iterating
BATCH_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: Create a dataset
Step2: The dataset index
Step3: drop_last=True skips the last batch if it contains fewer than BATCH_SIZE items
Step4: shuffl... |
7,961 | <ASSISTANT_TASK:>
Python Code:
from sklearn.model_selection import cross_val_score, KFold
from sklearn.neighbors import KNeighborsRegressor
# generate toy dataset:
x = np.linspace(-3, 3, 100)
rng = np.random.RandomState(42)
y = np.sin(4 * x) + x + rng.normal(size=len(x))
X = x[:, np.newaxis]
cv = KFold(shuffle=True)
# ... | <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 is a function in scikit-learn, called validation_plot to reproduce the cartoon figure above. It plots one parameter, such as the number of... |
7,962 | <ASSISTANT_TASK:>
Python Code:
from sympy import *
init_printing()
x = symbols('x')
x**2
eq = Eq(x + 3, 2, evaluate=False)
eq
Eq(x**2 + 3*x -1, 0, evaluate = False)
solve(eq, [x], dict=True)
eq = Eq(3*x, -2, evaluate=False)
eq
solve(eq, [x], dict=True)
eq = Eq(x**2, 3, evaluate=False)
eq
tentativi = list(map(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: $\mathbb{N}$
Step2: Quando leggi la scrittura matematica $x + 3 = 2$, questa significa la seguente cosa
Step3: puoi leggere la scrittura matem... |
7,963 | <ASSISTANT_TASK:>
Python Code:
import sys
import numpy
def main():
script = sys.argv[0]
filename = sys.argv[1]
data = numpy.loadtxt(filename, delimiter=',')
for m in data.mean(axis=1):
print(m)
import sys
import numpy
def main():
script = sys.argv[0]
filename = sys.argv[1]
data = nu... | <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 function gets the name of the script from sys.argv[0], because that’s where it’s always put, and the name of the file to process from sys.a... |
7,964 | <ASSISTANT_TASK:>
Python Code:
letters_map = {'2':'ABC', '3':'DEF', '4':'GHI', '5':'JKL',
'6':'MNO', '7':'PQRS', '8':'TUV', '9':'WXYZ'}
def printWords(number, ):
#number is phone number
def printWordsUtil(numb, curr_digit, output, n):
if curr_digit == n:
print('%s ' % output)
... | <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: Print Longest Common Subsequence
Step2: Time Travelling dictionary
Step3: Alien Dictionary
Step4: Binary Search
|
7,965 | <ASSISTANT_TASK:>
Python Code:
import ga4gh.client as client
c = client.HttpClient("http://1kgenomes.ga4gh.org")
dataset = c.search_datasets().next()
print(dataset)
reference_set = c.search_reference_sets().next()
print(reference_set)
references = [r for r in c.search_references(reference_set_id=reference_set.id)]
... | <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 continue to refer to this client object for accessing the remote server.
Step2: Access the reference set
Step3: With the reference set... |
7,966 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from pandas_datareader.data import DataReader
cpi_apparel = DataReader('CPIAPPNS', 'fred', start='1986')
cpi_apparel.index = pd.DatetimeIndex(cpi_apparel.index, freq='MS... | <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: Although most operations related to state space models rely on the Kalman filtering recursions, in some special cases one can use a separate met... |
7,967 | <ASSISTANT_TASK:>
Python Code:
from IPython.lib.display import YouTubeVideo
YouTubeVideo('6O43gOxtaWo', start=14)
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Import Users Data
unames = ['user_id','gender','age','occupation','zip']
users = pd.read_table('data/users.dat', ... | <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: TEAM Members
Step2: Compute some Summary Statistics for the data
Step3: How many movies have an average rating over 4.5 overall?
Step4: How m... |
7,968 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
from bokeh.charts import TimeSeries, output_notebook, show
# Get data
df = pd.read_csv('data/Land_Ocean_Monthly_Anomaly_Average.csv')
# Process data
df['datetime'] = pd.to_datetime(df['datetime'])
df = df[['anomaly','datetime']]
# Output option
output_notebook()
# Crea... | <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: Exercise
Step2: Exercise
|
7,969 | <ASSISTANT_TASK:>
Python Code:
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator
print(__doc__)
data_path = sample.data_path()
fname = data_path
fname += '/MEG/sample/sample_audvis-meg-oct... | <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: Show result on 3D source space
|
7,970 | <ASSISTANT_TASK:>
Python Code:
from pextant.mesh.abstractmesh import NpDataset
import numpy as np
xx,yy= np.mgrid[0:5,0:5]
basic_terrain = NpDataset(0.1*(xx**2+yy**2), resolution=1)
basic_terrain
basic_terrain[1,1]
basic_terrain.get_datapoint(np.array(([1,1],[1.5,1.5])))
from pextant.EnvironmentalModel import GridMe... | <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 dataset is wrapped around numpy so we can access can easily access entries
Step2: Or access several entries, and even interpolate
Step3: ... |
7,971 | <ASSISTANT_TASK:>
Python Code:
## Interactive magics
%matplotlib inline
import sys
import warnings
warnings.filterwarnings('ignore')
import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import patsy as pt
from scipy import optimize
# pymc3 libraries
import pymc3 as pm
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: Local Functions
Step2: Generate Data
Step3: View means of the various combinations (poisson mean values)
Step4: Briefly Describe Dataset
Step... |
7,972 | <ASSISTANT_TASK:>
Python Code:
count = 1
for elem in range(1, 3 + 1):
count *= elem
print(count)
from math import factorial as f
f(3)
def n_max():
inpt = eval(input("Please enter some values: "))
maximum = max_val(inpt)
print("The largest value is", maximum)
def max_val(ints):
Input: col... | <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: 3. Extend your program to n objects. How many different combinations do I have for 5 objects? How about 15? What is the max number of objects I ... |
7,973 | <ASSISTANT_TASK:>
Python Code:
import os
import matplotlib.pyplot as plt
import pandas as pd
import torch
from torch.nn import Parameter
import pyro
import pyro.contrib.gp as gp
import pyro.distributions as dist
import pyro.ops.stats as stats
smoke_test = ('CI' in os.environ) # ignore; used to check code integrity 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: Dataset
Step2: Modelling
Step3: Now comes the most interesting part. We know that the observed data $y$ has latent structure
Step4: We will u... |
7,974 | <ASSISTANT_TASK:>
Python Code:
import sys
print('Python version:', sys.version)
import IPython
print('IPython:', IPython.__version__)
import numpy
print('numpy:', numpy.__version__)
import scipy
print('scipy:', scipy.__version__)
import matplotlib
print('matplotlib:', matplotlib.__version__)
import pandas
print('pandas... | <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. Python Overview
Step2: (If you're typing this into an IPython notebook, or otherwise using notebook file, you hit shift-Enter to evaluate a ... |
7,975 | <ASSISTANT_TASK:>
Python Code:
!pip install meterstick
!git clone https://github.com/google/meterstick.git
import sys, os
sys.path.append(os.getcwd())
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from meterstick import *
np.random.seed(42)
platform = ('Desktop', 'Mobile', 'Tablet')
exprs = (... | <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: or from GitHub for the latest version.
Step2: Demo Starts
Step3: Simple Metrics
Step4: Count
Step5: Dot (inner product)
Step6: It can also ... |
7,976 | <ASSISTANT_TASK:>
Python Code:
# Import data
import math
# Create list of values
data = [3,2,3,4,2,3,5,2,2,33,3,5,2,2,5,6,62,2,2,3,6,6,2,23,3,2,3]
# Calculate n
n = len(data)
# Calculate the mean
mean = sum(data)/len(data)
# Create a list of all deviations from the mean
all_deviations_from_mean_squared = []
# For eac... | <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 Data
Step2: Calculate Population Variance
Step3: Calculate Population Standard Deviation
|
7,977 | <ASSISTANT_TASK:>
Python Code:
import seaborn
import pandas as pd
import pylab as pl
import yaml
%pylab inline
df = pd.read_pickle("../yelp-challenge/data_urbana_champaign/business_urbana_champaign.p")
df.reset_index(drop=True, inplace=True)
print df.shape
print df.columns.values
len(df.business_id.unique())
df.head(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: geo
Step2: 1. 'city'
Step3: we only consider Champaign and Urbana as our target in this dataset
Step4: 2. 'is_open'
Step5: we only consider ... |
7,978 | <ASSISTANT_TASK:>
Python Code:
from indicnlp.morph import unsupervised_morph
morph = unsupervised_morph.UnsupervisedMorphAnalyzer("bn")
text = u\
করা করেছিলাম করেছি করতে করেছিল হয়েছে হয়েছিল হয় হওয়ার হবে আবিষ্কৃত আবিষ্কার অভিষিক্ত অভিষেক অভিষেকের আমি আমার আমাদের তুমি তোমার তোমাদের বসা বসেছিল বসে বসি বসেছিলাম বস বসার\
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: Using Indic NLP Library
Step2: Transliteration
Step3: Using Silpa
Step4: Using BengaliStemmer
Step5: Using BanglaStemmer
Step6: Using Avro
|
7,979 | <ASSISTANT_TASK:>
Python Code:
import tataaq
YOUR_API_KEY_HERE = ""
api = tataaq.TataAQ(apikey=YOUR_API_KEY_HERE)
# Ping the server to see if we have valid auth credentials
resp = api.ping()
print (resp.status_code)
import pandas as pd
import feather
# Request decice information for EBAM001
resp = api.device("EBAM001... | <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 Things
Step2: Retrieve Information about a Device
Step3: Access the status of the previous request
Step4: Access the header informatio... |
7,980 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
# from fastai.core
def even_mults(start:float, stop:float, n:int)->np.ndarray:
"Build evenly stepped schedule from `star` to `stop` in `n` steps."
mult = stop/start
step = mult**(1/(n-1))
return np.array([start*(step**i) for i in range(n)])
layer_groups... | <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 say for a hypothetical network with 3 layer groups (conv_group_1, conv_group_2, linear_group).
Step2: Interesting, so if you have multipl... |
7,981 | <ASSISTANT_TASK:>
Python Code:
psource(Chart)
chart = Chart(E0)
print(chart.parses('the stench is in 2 2'))
chart_trace = Chart(nlp.E0, trace=True)
chart_trace.parses('the stench is in 2 2')
print(chart.parses('the stench 2 2'))
import os, sys
sys.path = [os.path.abspath("../../")] + sys.path
from nlp4e import *
f... | <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: And then we simply call the parses function
Step3: You can see which edges get added by setting the optional initialization arg... |
7,982 | <ASSISTANT_TASK:>
Python Code:
%pylab notebook
%precision 4
Zline = 38.2 + 140.0j # [Ohm]
Zeq = 0.10 + 0.4j # [Ohm]
V_high = 14e3 # [V]
V_low = 2.4e3 # [V]
Pout = 90e3 # [W] load
PF = 0.8 # lagging
VS = 2.3e3 # [V] secondary voltage
a = V_high / V_low
a
Z_line = (1/a)**2 * Zline
print('Z_line = ... | <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: Description
Step2: (a)
Step3: The feeder’s impedance referred to the secondary side is
Step4: The secondary current $I_S$ is given by
Step5:... |
7,983 | <ASSISTANT_TASK:>
Python Code:
# Loading data, dividing, modeling and EDA below
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
data = pd.read_csv('../input/new-york-city-taxi-fare-prediction/tr... | <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 following two cells may also be useful to understand the values in the training data
Step2: Question 1
Step3: Question 2
Step4: Uncomment... |
7,984 | <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: ... |
7,985 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
# read in the iris data
iris = load_iris()
X = iris.data
y = iris.target
for i in xrange(1,5):
print "random_stat... | <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: 上面的测试准确率可以看出，不同的训练集、测试集分割的方法导致其准确率不同，而交叉验证的基本思想是：将数据集进行一系列分割，生成一组不同的训练测试集，然后分别训练模型并计算测试准确率，最后对结果进行平均处理。这样来有效降低测试准确率的差异。
Step2: 3. 使用交叉验证的建议
Ste... |
7,986 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from pylab import *
N = 10**5
lambda_ = 2.0
########################################
# Supply the missing coefficient herein below
V1 = -1.0/lambda_
data = V1*log(rand(N))
########################################
m = mean(data)
v = va... | <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: 2) The discrete valued random variable $X$ follows a Poisson distribution if its probabilities depend on a parameter $\lambda$ and are such that... |
7,987 | <ASSISTANT_TASK:>
Python Code:
X = np.array([[7, 5],[5, 7],[7, 7],[4, 4],[4, 6],[1, 4],[0, 0],[2, 2],[8, 7],[6, 8],[5, 5],[3, 7]], dtype=float)
plt.scatter(X[:,0], X[:,1], s=100)
plt.show()
from sklearn.cluster import KMeans
model = KMeans(n_clusters=2, init="random", n_init=1, max_iter=1, random_state=1).fit(X)
c0, c1... | <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: K-Means++
Step2: 예
|
7,988 | <ASSISTANT_TASK:>
Python Code:
def printArr(arr , n ) :
arr . sort()
if(arr[0 ] == arr[n - 1 ] ) :
print("No ")
else :
print("Yes ")
for i in range(n ) :
print(arr[i ] , end = "▁ ")
print()
if __name__== ' __main __' :
arr =[1 , 2 , 2 , 1 , 3 , 1 ]
N = len(arr )
printArr(arr , 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:
|
7,989 | <ASSISTANT_TASK:>
Python Code:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import hashlib
import math
import os.path
import random
import re
import sys
import tarfile
import numpy as np
import librosa as rosa
from six.moves import urllib
from six.moves im... | <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: One shot keyword trigger
Step2: Wav MFCC loader
Step3: Conv Network
Step4: Siamese Network
|
7,990 | <ASSISTANT_TASK:>
Python Code:
import bqplot.pyplot as plt
# first, let's create two vectors x and y to plot using a Lines mark
import numpy as np
x = np.linspace(-10, 10, 100)
y = np.sin(x)
# 1. Create the figure object
fig = plt.figure(title="Simple Line Chart")
# 2. By default axes are created with basic defaults. 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:
Steps for building plots in pyplot
Step1: For creating other marks (like scatter, pie, bars, etc.), only step 2 needs to be changed. Lets look a simple... |
7,991 | <ASSISTANT_TASK:>
Python Code:
summaryDf = pd.DataFrame([extractSummaryLine(l) for l in open('../../data/learnedModel/anto/summary.txt').readlines()],
columns=['bidirectional', 'strict', 'clf', 'feature', 'post', 'precision', 'recall', 'f1'])
summaryDf.sort_values('f1', ascending=False)[:10]
!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: We can observe quite good f1-score on RandomForest with normalised projected cosine similarity.
|
7,992 | <ASSISTANT_TASK:>
Python Code:
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from astropy.time import Time
import astropy.units as u
from astroplan import Observer, FixedTarget
# Observe from Keck
obs = Observer.at_site("Keck")
# Observe these three stars
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: Make a list of constraints to compute
Step2: Now combine those constraint results with non-trivial boolean logic
Step3: Simple visualization o... |
7,993 | <ASSISTANT_TASK:>
Python Code:
from sklearn import datasets
import matplotlib.pyplot as plt
iris = datasets.load_iris()
def plot(dataset, ax, i, j):
ax.scatter(dataset.data[:,i], dataset.data[:,j], c=dataset.target, s=50)
ax.set_xlabel(dataset.feature_names[i], fontsize=20)
ax.set_ylabel(dataset.feature_nam... | <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: Clustering
Step2: k-means
Step3: Aplicación a datos
Step4: ¿Es necesario reinventar la rueda?
Step5: ¿Cómo seleccionar k?
|
7,994 | <ASSISTANT_TASK:>
Python Code:
import random, datetime
import numpy as np
import pylab as plt
import statsmodels.api as sm
from scipy.stats import norm
from scipy.stats.stats import pearsonr
# str, int, float
str(3)
# int
int('5')
# float
float('7.1')
range(10)
range(1, 10)
dir
dir(str)[-5:]
help(str)
x = ' Hello Wor... | <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: Variable Type
Step2: dir & help
Step3: type
Step4: Data Structure
Step5: 定义函数
Step6: For 循环
Step7: map
Step8: if elif else
Step9: while循... |
7,995 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
df = pd.read_csv("../kyphosis.csv")
df.head()
# TODO 1
sns.pairplot(df, hue="Kyphosis", palette="Set1")
from sklearn.model_selection import train_test_split
X = df.drop("Kyph... | <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: Get the Data
Step2: Exploratory Data Analysis
Step3: Train Test Split
Step4: Decision Trees
Step5: Prediction and Evaluation
Step6: Tree Vi... |
7,996 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import sqlalchemy
!pip install -U okpy
from client.api.notebook import Notebook
ok = Notebook('hw4.ok')
my_URI = "postgres://sam:@localhost:5432/fec"
%load_ext sql
%sql $my_URI... | <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 connect to your Postgres database. On your Heroku Postgres details,
Step2: Table Descriptions
Step3: For longer queries, you can sa... |
7,997 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import re
!ls -liLah ../../share/Data
# generate the raw_data from CSV
raw_data = pd.read_csv("../../share/Data/SPON_complete", delimiter=",", skipinitialspace=True)
# Stopwords are downloaded and ... | <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: Rohdaten
Step2: Stopwörter
Step3: Aufbereiten der Rohdaten
Step5: Zählen
Step6: Aufbereiten der Daten und schreiben in CSV-Dateien
|
7,998 | <ASSISTANT_TASK:>
Python Code:
%run ../linked_list/linked_list.py
%load ../linked_list/linked_list.py
class MyLinkedList(LinkedList):
def kth_to_last_elem(self, k):
# TODO: Implement me
pass
# %load test_kth_to_last_elem.py
from nose.tools import assert_equal
class Test(object):
def test_kth_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: Unit Test
|
7,999 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
%matplotlib inline
def dd(t1, t2, nu):
return -nu*(3*np.cos(t1)*np.cos(t2) - np.cos(t1-t2))
def anis(t1, t2, sigma):
return sigma*(np.sin(t1)**2 + np.sin(t2)**2)
def tot(t1, t2, nu, 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: Individual energy terms
Step2: The unnormalised probability of state $\theta_1\theta_2$
Step3: 2-dimensional Boltzmann distribution
Step4: An... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.