Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
|---|---|---|
14,200 | <ASSISTANT_TASK:>
Python Code:
data_in_shape = (3, 6)
rnn = GRU(4, activation='tanh', recurrent_activation='hard_sigmoid')
layer_0 = Input(shape=data_in_shape)
layer_1 = rnn(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for i, w in enumerate(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: [recurrent.GRU.1] units=5, activation='sigmoid', recurrent_activation='sigmoid'
Step2: [recurrent.GRU.2] units=4, activation='tanh', recurrent_... |
14,201 | <ASSISTANT_TASK:>
Python Code:
if os.path.exists('dress_sf_processed.sf'):
reference_sf = graphlab.SFrame('dress_sf_processed.sf')
else:
reference_sf = graphlab.SFrame('https://static.turi.com/datasets/dress_sf_processed.sf')
reference_sf.save('dress_sf_processed.sf')
if os.path.exists('dress_nn_model'):
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2. Create a Predictive Service (One time) <a id='create'></a>
Step2: Load an already created service
Step3: Query the model <a id='query'></a>... |
14,202 | <ASSISTANT_TASK:>
Python Code:
import re
format_pat= re.compile(
r"(?P<host>[\d\.]+)\s"
r"(?P<identity>\S*)\s"
r"(?P<user>\S*)\s"
r"\[(?P<time>.*?)\]\s"
r'"(?P<request>.*?)"\s'
r"(?P<status>\d+)\s"
r"(?P<bytes>\S*)\s"
r'"(?P<referer>.*?)"\s'
r'"(?P<user_agent>.*?)"\s*'
)
logPath = "... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Here's the full path to the log file I'm analyzing; change this if you want to run this stuff yourself
Step2: Now we'll whip up a little script... |
14,203 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table, vstack
from astropy.io import fits
import multiprocessing
nproc = multiprocessing.cpu_count() // 2
from desispec.io.util import write_bintable
from desitarget
from desiutil.log import get_logger... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Establish the I/O path, random seed, and path to the dust maps and desired healpixel.
Step2: All or none of the output files can be overwritten... |
14,204 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
%matplotlib inline
df = pd.read_csv('stroopdata.csv')
df['diff'] = df['Incongruent'] - df['Congruent']
df
df.describe()
df.plot.scatter(x='Congruent',y='Incongruent');
(df.Incongruent - df.Congruent).plot.hist();
%%R
n = 24
mu = 7.964792
s = 4.864827
CL = 0.95
n =... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The experiment takes participants with two test, congruent task and incongruent task. Congruent task is word with agreeing text and font color, ... |
14,205 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os
import numpy as np
import psycopg2
import psycopg2.extras
from itertools import chain
from collections import Counter, defaultdict
import requests
import imageio
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image, ImageDraw
from io import... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The fetch_image function below fetches an image from wikipedia given a wikipedia id for that image
Step2: The code here relies on wikipedia hav... |
14,206 | <ASSISTANT_TASK:>
Python Code:
#import pandas and numpy libraries
import pandas as pd
import numpy as np
import sys #sys needed only for python version
#import gaussian naive bayes from scikit-learn
import sklearn as sk
#seaborn for pretty plots
import seaborn as sns
#display versions of python and packages
print('\npy... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The dataset doesn't include column names, and the values are text characters
Step2: Added column names from the UCI documentation
Step3: The d... |
14,207 | <ASSISTANT_TASK:>
Python Code:
names_df = pd.read_csv("./IMA_mineral_names.txt", sep=',', header=None, names=['names'])
names_df['names'] = names_df['names'].str.strip().str.lower()
names_df['len'] = names_df['names'].str.len()
names_df['tuple'] = names_df['names'].apply(lambda x: tuple(sorted(set(x))))
names_df['setle... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Remove duplicates
Step2: Create letter table
Step3: Find argmin in the letter distribution
Step4: Recursion
Step5: The effective ratio crite... |
14,208 | <ASSISTANT_TASK:>
Python Code:
!pip install tensorflow-gpu
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=0,1
import os
print(os.environ["CUDA_DEVICE_ORDER"])
print(os.environ["CUDA_VISIBLE_DEVICES"])
import tensorflow as tf
from tensorflow.python.client import device_lib
device_lib.list_local_devices()
tf... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: From https
Step2: From https
|
14,209 | <ASSISTANT_TASK:>
Python Code:
for st_type, ways in abq_st_types.iteritems():
for name in ways:
better_name = update_name(name, mapping)
if name != better_name:
print name, "=>", better_name
Honolulu:
Kalakaua Ave => Kalakaua Avenue
Lusitania St. => Lusitania Street
...
Albuquerque:
Vall... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <hr>
Step2: <hr>
Step3: Number of documents
Step4: Number of node nodes.
Step5: Number of way nodes.
Step6: Total Number of contributors.
S... |
14,210 | <ASSISTANT_TASK:>
Python Code:
#%matplotlib notebook
# imports
from importlib import reload
import numpy as np
import os
from pkg_resources import resource_filename
from matplotlib import pyplot as plt
from scipy import interpolate
from astropy import units
from astropy.table import Table
from astropy.cosmology import ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: DM -- Piece by piece (as coded)
Step2: Cumulative plot
|
14,211 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import ipyvolume as ipv
V = np.zeros((128,128,128)) # our 3d array
# outer box
V[30:-30,30:-30,30:-30] = 0.75
V[35:-35,35:-35,35:-35] = 0.0
# inner box
V[50:-50,50:-50,50:-50] = 0.25
V[55:-55,55:-55,55:-55] = 0.0
ipv.figure()
ipv.volshow(V, level=[0.25, 0.75], opacity=0... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Visualizating a scan of a male head
|
14,212 | <ASSISTANT_TASK:>
Python Code:
%pylab
%matplotlib inline
%run jupyter_helpers
%run yc_framework
figure_width = 16
eval_date = create_date('2017-01-03')
def generate_pricing_curvemap(eval_date):
random.seed(0)
pricing_curvemap = CurveMap()
t = linspace(eval_date+0, eval_date+365*80, 7)
def createCurve(n... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Pricing Curve Map
Step2: Interpolation Modes
Step3: Curve Builder
Step4: Instrument Repricing
Step5: Display price ladder for a specific cur... |
14,213 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import sys
sys.path.insert(0, "../..")
from insights.combiners.httpd_conf import get_tree
from insights.parsr.query import *
conf = get_tree()
conf["Alias"]
conf["Directory"]
conf["Directory"]["Options"]
conf["Directory", "/"]
conf["Directory", "/"... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: conf now contains the consolidated httpd configuration tree from my machine. The API that follows is exactly the same for nginx, multipath, logr... |
14,214 | <ASSISTANT_TASK:>
Python Code:
mktcaps = {'AAPL':538.7,'GOOG':68.7,'IONS':4.6}# Dictionary wird initialisiert
print(type(mktcaps))
print(mktcaps)
print(mktcaps.values())
print(mktcaps.keys())
print(mktcaps.items())
c=mktcaps.items()
print c[0]
mktcaps['AAPL'] #Gibt den Wert zurück der mit dem Schlüssel "AAPL" verknüpft... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <h3>Beispiel
Step2: <h3>Beispiel
Step3: <h2>Beispiel Studenten - mit dictionary</h2>
Step4: <h2>Schrittweiser Aufbau eines Studentenverezichn... |
14,215 | <ASSISTANT_TASK:>
Python Code:
try:
import cirq
except ImportError:
print("installing cirq...")
!pip install --quiet cirq --pre
print("installed cirq.")
from typing import Iterable, List, Optional, Sequence
import matplotlib.pyplot as plt
import numpy as np
import os
import cirq
import cirq_google as c... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Note
Step2: Defining the circuit
Step3: This line is now broken up into a number of segments of a specified length (number of qubits).
Step4: ... |
14,216 | <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
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Description:
Step1: Text classification with TensorFlow Lite Model Maker
Step2: Import the required packages.
Step3: Get the data path
Step4: You can also upload... |
14,217 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os
import sys
# Modify the path
sys.path.append("..")
import pandas as pd
import yellowbrick as yb
import matplotlib.pyplot as plt
from yellowbrick.classifier import ROCAUC
from sklearn.model_selection import train_test_split
occupancy = pd.read_csv('data/occu... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Binary Classification with 1D Coefficients or Feature Importances
Step2: Looks good; everything works!
Step3: Some of these generate the Index... |
14,218 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.random.seed(3908544)
# Generate two random datasets.
data1 = np.random.normal(loc = 0, scale = 58, size = 1000)
data2 = 200 * np.random.random(1000) - 100
# What are their means and variances?
print("Dataset 1 :: {:.2f} (avg) :: {:.2f} (std)".format(data1.mean(), dat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Both datasets contain 1000 random numbers. Both datasets have very nearly the same mean and same standard deviation.
Step2: Behold
Step3: If y... |
14,219 | <ASSISTANT_TASK:>
Python Code:
a_df = pd.DataFrame([
{
"Name": "A 회사 직원 (1)",
"Age": 30,
},
{
"Name": "A 회사 직원 (2)",
"Age": 29,
}
])
b_df = pd.DataFrame([
{
"Name": "B 회사 직원 (1)",
"Age": 33,
},
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: A와 B 회사가 합병해서 하나의 DF으로 만들자.
Step2: append 2. concat 이 있어.
Step3: pd.concat
Step4: 카카오(A)와 다음(B) 합병 했는데 누가 어디 출신인지 알고 싶다.
Step5: df[STR] =>... |
14,220 | <ASSISTANT_TASK:>
Python Code:
from simulator import Simulator, Map, Agent
import numpy as np
class config:
metadata = {
'render.modes': ['human', 'rgb_array'],
'video.frames_per_second': 10,
'world_width': 300,
'world_height': 300,
'screen_width': 600,
'screen_height': 600,
'dt': 1.0 /... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: In bugbot simulation, either map or agents are subclass of Geom2d which is a wrapper over Geom defined in OpenAI Gym. You can define a Geom2d us... |
14,221 | <ASSISTANT_TASK:>
Python Code:
from helpers import load_data
# load dataset
x, y = load_data()
def build_k_indices(y, k_fold, seed):
build k indices for k-fold.
num_row = y.shape[0]
interval = int(num_row / k_fold)
np.random.seed(seed)
indices = np.random.permutation(num_row)
k_indices = [indice... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Cross-Validation and Bias-Variance decomposition
Step5: Bias-Variance Decomposition
|
14,222 | <ASSISTANT_TASK:>
Python Code:
% matplotlib inline
import os, sys, time
import math, random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from joblib import Parallel, delayed
%run 'ssvm.ipynb'
check_protocol = True
traj_group_test = dict()
test_ratio = 0.3
for key in sor... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Run notebook ssvm.ipynb
Step2: Sanity check for evaluation protocol
|
14,223 | <ASSISTANT_TASK:>
Python Code:
import kfp
import kfp.gcp as gcp
import kfp.dsl as dsl
import kfp.compiler as compiler
import kfp.components as comp
import datetime
import kubernetes as k8s
# Required Parameters
PROJECT_ID='<ADD GCP PROJECT HERE>'
GCS_BUCKET='gs://<ADD STORAGE LOCATION HERE>'
# Optional Parameters, but... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Create client
Step2: Build reusable components
Step3: Create a Docker container
Step4: Build docker image
Step5: If you want to use docker t... |
14,224 | <ASSISTANT_TASK:>
Python Code:
# Planet class definition at the end of Part 1
# code to make sure constructors and get methods all work
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Test your code to make sure that the class definition worked.
|
14,225 | <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.display import display
def random_line(m, b, sigma, size=10):
Create a line y = m*x + b + N(0,sigma**2) between x=[-1.0,1.0]
Param... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step2: Line with Gaussian noise
Step5: Write a function named plot_random_line that takes the same arguments as random_line and creates a random line ... |
14,226 | <ASSISTANT_TASK:>
Python Code:
import vcsn
%%automaton a1
context = "lal_char(abc), z"
$ -> 0
0 -> 1 <2>a
0 -> 2 <3>a
1 -> 1 a
1 -> 3 <4>a
2 -> 2 a
2 -> 4 a
3 -> $
4 -> $
a1.minimize()
%%automaton a
context = "lal_char, z"
$ -> 0
$ -> 1 <2>
0 -> 0 a
0 -> 1 b
0 -> 2 <3>a,b
0 -> 3 b
1 -> 1 a, b
1 -> 2 a, <2>b
1 -> 3 <2... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Weighted
Step2: The following example is taken from lombardy.2005.tcs, Fig. 4.
Step3: Signature
Step4: Moore
Step5: Brzozowski
Step6: Hopcr... |
14,227 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import os
import os.path
import re
import astropy.table
import astropy.units as u
from astropy.io import fits
from astropy import wcs
def read_raytracing(num_sections=14, num_groups=12):
# Initialize the result arrays.
wavelength = np.zeros(num_sections)
ba... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Preprocess Ray Tracing Results
Step2: Reproduce the plot on the geometric_blur tab of the DESI-0347-v13 spreadsheet. For reference, the fiber d... |
14,228 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import quantecon as qe
# matplotlib settings
plt.rcParams['axes.xmargin'] = 0
plt.rcParams['axes.ymargin'] = 0
def approx_lq(s_star, x_star, f_star, Df_star, DDf_star, g_star, Dg_star, discount):
Return an approximating LQ insta... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step2: We consider a dynamic maximization problem with
Step3: Optimal Economic Growth
Step4: Function definitions
Step5: Steady state
Step6: (s_sta... |
14,229 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
?plt.scatter()
from matplotlib import markers
markers.MarkerStyle.markers.keys()
x = np.random.rand(100)
y = np.random.rand(100)
plt.scatter(x, y, label = 'The Dots', c = u'r', marker = u'o')
plt.grid(True)
plt.box(Fal... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Scatter plots
Step2: Histogram
|
14,230 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
g = 9.81 # m/s^2
l = 0.5 # length of pendulum, in meters
tmax = 50. # seconds
t = np.linspace(0, tmax, int(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Damped, driven nonlinear pendulum
Step4: Write a function derivs for usage with scipy.integrate.odeint that computes the derivatives for the da... |
14,231 | <ASSISTANT_TASK:>
Python Code:
# Import libraries
import numpy as np
import pandas as pd
# Read student data
student_data = pd.read_csv("student-data.csv")
print "Student data read successfully!"
# Note: The last column 'passed' is the target/label, all other are feature columns
# TODO: Compute desired values - replac... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Now, can you find out the following facts about the dataset?
Step2: 3. Preparing the Data
Step3: Preprocess feature columns
Step4: Split data... |
14,232 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
queries, documents = load_data()
assert type(queries) == list
assert type(documents) == list
tfidf = TfidfVectorizer()
tfidf.fit_transform(documents)
from sklearn.metrics.pairwise import cos... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
14,233 | <ASSISTANT_TASK:>
Python Code:
import pints
import pints.toy as toy
import pints.plot
import numpy as np
import matplotlib.pyplot as plt
# Use the toy logistic model
model = toy.LogisticModel()
real_parameters = [0.015, 500]
times = np.linspace(0, 1000, 100)
org_values = model.simulate(real_parameters, times)
# Add ind... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Visualisation of the data
Step2: Plotting autocorrelation of the residuals
Step3: The figure shows no significant autocorrelation in the resid... |
14,234 | <ASSISTANT_TASK:>
Python Code:
import time
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline
# config directory must have "__init__.py" file
# from the 'config' directory, import the following classes:
from config import Motor, ASI_Controller, Autosipper
from config import 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: Autosipper
Step2: Manifold
Step3: Micromanager
Step4: Preset
Step5: ACQUISITION
Step6: MM Get info
Step7: Video
Step8: SNAP CV2
Step9: E... |
14,235 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import *
SVG('three_receiver_cal/pics/vnaBlockDiagramForwardRotated.svg')
ls three_receiver_cal/data/
import skrf as rf
%matplotlib inline
from pylab import *
rf.stylely()
raw = rf.read_all_networks('three_receiver_cal/data/')
# list the raw measurements
raw.keys... | <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 fully correct an arbitrary two-port, the device must be measured in two orientations, call these forward and reverse. Because there is no sw... |
14,236 | <ASSISTANT_TASK:>
Python Code:
fertility_df, life_expectancy_df, population_df_size, regions_df, years, regions_list = process_data()
sources = {}
region_name = regions_df.Group
region_name.name = 'region'
for year in years:
fertility = fertility_df[year]
fertility.name = 'fertility'
life = life_expectancy_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: sources looks like this
Step2: Build the plot
Step3: Build the axes
Step4: Add the background year text
Step5: Add the bubbles and hover
Ste... |
14,237 | <ASSISTANT_TASK:>
Python Code:
#import sys
#sys.path.insert(0,'/path/to/pydensecrf/')
import pydensecrf.densecrf as dcrf
from pydensecrf.utils import unary_from_softmax, create_pairwise_bilateral
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['image.interpolation'] = 'nearest'
plt.rc... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Unary Potential
Step2: Run inference with unary potential
Step3: Pairwise terms
Step4: Run inference of complete DenseCRF
|
14,238 | <ASSISTANT_TASK:>
Python Code:
# Author: Jussi Nurminen (jnu@iki.fi)
#
# License: BSD (3-clause)
import mne
import os
from mne.datasets import multimodal
fname_raw = os.path.join(multimodal.data_path(), 'multimodal_raw.fif')
print(__doc__)
raw = mne.io.read_raw_fif(fname_raw)
print(raw.acqparser)
cond = raw.acqparse... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Read raw file
Step2: Check DACQ defined averaging categories and other info
Step3: Extract epochs corresponding to a category
Step4: Get epoc... |
14,239 | <ASSISTANT_TASK:>
Python Code:
d = pq('<span><p class="hello">Hi</p><p>Bye</p></span>')
for each in d.children():
print each.text
r = requests.get(sampleurl1)
r.raise_for_status()
r.content
blurb = pq(r.content)
for detail in blurb('dt'):
print detail.text
#blurb().text()
r = requests.get(sampleurl2)
r.raise_fo... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Sample
Step2: again
Step3: pyqyery get Details sample
|
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14,241 | <ASSISTANT_TASK:>
Python Code:
import pickle
import sys
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit
data_dict = pickle.load(open("../final_project/final_project_dataset.pkl", "r") )
features_list = ["poi", "salary"]
data = featureFormat(data_dict, features_list)
labels, fea... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Training a decision tree on this starter data
Step2: Counts of actual and predicted values
Step3: Which turn out to match up very poorly. No t... |
14,242 | <ASSISTANT_TASK:>
Python Code:
# For use in Quantopian Research, exploring interactively
from quantopian.interactive.data.quandl import cboe_vvix as dataset
# import data operations
from odo import odo
# import other libraries we will use
import pandas as pd
# Let's use blaze to understand the data a bit using Blaze ds... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Let's go over the columns
Step2: <a id='pipeline'></a>
Step3: Now that we've imported the data, let's take a look at which fields are availabl... |
14,243 | <ASSISTANT_TASK:>
Python Code:
help('learning_lab.03_interface_properties')
from importlib import import_module
script = import_module('learning_lab.03_interface_properties')
from inspect import getsource
print(getsource(script.main))
print(getsource(script.demonstrate))
run ../learning_lab/03_interface_properties.py... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Implementation
Step2: Execution
Step3: HTTP
|
14,244 | <ASSISTANT_TASK:>
Python Code:
enunciado = list([r'\frac{7!}{6!}',r'\frac{{8!}}{{9!}}',r'\frac{{9!}}{{5!\cdot 4!}}',r'\frac{{m!}}{{(m - 1)!}}', r'\frac{{( {m + 1} )!}}{{( {m - 1} )!}}'])
enunciado
enunciado = list([r'\frac{7!}{6!}',r'\frac{{8!}}{{9!}}',r'\frac{{9!}}{{5!\cdot 4!}}',r'\frac{{m!}}{{(m - 1)!}}', r'\frac{{(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Ejercicio
|
14,245 | <ASSISTANT_TASK:>
Python Code:
__author__ = 'shivam_gaur'
import requests
from bs4 import BeautifulSoup
import re
from pymongo import MongoClient
# Global Config Variables
client_key = '&key=<insert_your_39_character_api_key_here>'
_URL_ = 'https://maps.googleapis.com/maps/api/geocode/xml?address='
count = 0
# same he... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Declaring the important helper functions and global variables
Step2: Connecting to the Mongo DB client running on the same machine.
Step3: Hel... |
14,246 | <ASSISTANT_TASK:>
Python Code:
# setup SymPy
from sympy import *
x, y, z, t = symbols('x y z t')
init_printing()
# define the matrices A and B, and the vecs v and w
A = Matrix([[1,3],
[4,5]])
B = Matrix([[-1,0],
[ 3,3]])
v = Matrix([[1,2]]).T # the .T makes v a column vector
w = Matrix([[-3... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Definitions
|
14,247 | <ASSISTANT_TASK:>
Python Code:
def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].uppe... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Note
Step2: Lesson
Step3: Project 1
Step4: We'll create three Counter objects, one for words from positive reviews, one for words from negati... |
14,248 | <ASSISTANT_TASK:>
Python Code:
# importando el modulo de regex de python
import re
# compilando la regex
patron = re.compile(r'\bfoo\b') # busca la palabra foo
# texto de entrada
texto = bar foo bar
foo barbarfoo
foofoo foo bar
# match nos devuelve None porque no hubo coincidencia al comienzo del texto
print(patr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Buscando coincidencias
Step3: Ahora que ya tenemos el objeto de expresión regular compilado podemos utilizar alguno de los siguientes métodos p... |
14,249 | <ASSISTANT_TASK:>
Python Code:
import magma as m
from mantle import DFF
class Register(m.Generator):
Generate an n-bit register
Interface
---------
I : In(Bits[width]), O : Out(Bits[width])
@staticmethod
def generate(width: int):
T = m.Bits[width]
class _Register(m... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Register - col and join
Step3: fork
Step5: There is a lot going on in this function.
Step7: scan
|
14,250 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
DO NOT MODIFY THIS CELL
def fully_connected(prev_layer, num_units):
Create a fully connectd layer with the given layer... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step3: Batch Normalization using tf.layers.batch_normalization<a id="example_1"></a>
Step6: We'll use the following function to create convolutional l... |
14,251 | <ASSISTANT_TASK:>
Python Code:
# TODO: You Must Change the setting bellow
MYSQL = {
'user': 'root',
'passwd': '',
'db': 'coupon_purchase',
'host': '127.0.0.1',
'port': 3306,
'local_infile': True,
'charset': 'utf8',
}
DATA_DIR = '/home/nasuno/recruit_kaggle_datasets' # ディレクトリの名前に日本語(マルチバイト文... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: モジュールのimportや変数の初期化
Step2: 2. データベースへのデータの格納
Step3: 次に、データのインサートです。
Step4: テーブルの作成に利用したCREATE TABLE文には、
Step5: 実行すると、それぞれのレコードでWarningが発生します... |
14,252 | <ASSISTANT_TASK:>
Python Code:
aapl = data.DataReader('AAPL', 'yahoo', '2000-01-01')
print(aapl.head())
plt.plot(aapl.Close)
ibm = data.DataReader('AAPl', 'yahoo', '2000-1-1')
print(ibm['Adj Close'].head())
%matplotlib inline
ibm['Adj Close'].plot(figsize=(10,6))
plt.ylabel('price')
plt.xlabel('year')
plt.title('Pric... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: $\Rightarrow$ various different price series
Step2: $\Longrightarrow$ There was a stock split 7
Step3: Define new financial instruments
Step4:... |
14,253 | <ASSISTANT_TASK:>
Python Code:
import os
from urllib.request import urlretrieve
import numpy as np
import matplotlib.pyplot as plt
from SeisCL import SeisCL
url = "http://sw3d.cz/software/marmousi/little.bin/velocity.h@"
if not os.path.isfile("velocity.h@"):
urlretrieve(url, filename="velocity.h@")
vel = np.fromfil... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: For inversion, we often want a coaser grid. We must also pad the model for the absorbing boundary and create the vs and rho paramters.
Step2: W... |
14,254 | <ASSISTANT_TASK:>
Python Code:
# import requirements
import pandas as pd
import nltk
#import gensim
import spacy
# New York Times data
## read subset of data from csv file into panadas dataframe
df = pd.read_csv('1_100.csv')
## for now, chosing one article to illustrate preprocessing
article = df['full_text'][939]
# 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: <h2>Data</h2>
Step2: Let's take a peek at the raw text of this article to see what we are dealing with!
Step3: <h2>Preprocessing Text</h2>
Ste... |
14,255 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Audio,Image, YouTubeVideo
YouTubeVideo('S5SG9km2f_A', height=450, width=900)
%matplotlib inline
import warnings
warnings.simplefilter('ignore')
import numpy as np
import matplotlib.pyplot as plt
import pandas
import geopandas
from pygridgen import Gridgen
from... | <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: Main Tutorial
Step2: Loading and plotting the boundary data
Step3: Generating a grid with pygridgen, plotting with pygridtools
Step4: Interac... |
14,256 | <ASSISTANT_TASK:>
Python Code:
running_id = 0
output = [[0]]
with open("E:/output.txt") as file_open:
for row in file_open.read().split("\n"):
cols = row.split(",")
if cols[0] == output[-1][0]:
output[-1].append(cols[1])
output[-1].append(True)
else:
outpu... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Step1: Problems
Step2: Problems
Step3: Problems
Step4: If we want to look at covariates, we need a new approach.
Step5: Once we've fit the data, ... |
14,257 | <ASSISTANT_TASK:>
Python Code:
%tensorflow_version 1.x
!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import deepchem_installer
%time deepchem_installer.install(version='2.3.0')
!wget https://raw.githubusercontent.com/deepchem/deepchem/master/dataset... | <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 need to load a dataset of estimated aqueous solubility measurements [1] into deepchem. The data is in CSV format and contains SMILES strings,... |
14,258 | <ASSISTANT_TASK:>
Python Code:
import LFPy
import MEAutility as mu
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
cellParameters = {
'morphology' : 'morphologies/L5_Mainen96_LFPy.hoc',
'tstart' : -50, # ignore startup transients
'tstop' : 100,
'dt' : 2**-4,
... | <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 some dictionarys with parameters for cell
Step2: Create an helper function to instantiate a cell object given a set of parameters
Step3:... |
14,259 | <ASSISTANT_TASK:>
Python Code:
#If you haven't already, make sure you install the `dfcx-scrapi` library
!pip install dfcx-scrapi
from dfcx_scrapi.tools.copy_util import CopyUtil
creds_path = '<YOUR_CREDS_FILE>'
agent_id = '<YOUR_AGENT_ID>'
source_flow = 'Default Start Flow'
target_flow = 'My Target Flow'
cu = CopyUt... | <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: Imports
Step2: User Inputs
Step3: Get Flows Map from Agent
Step4: Get All Pages from Source Flow
Step5: Extract Subset of Pages To Copy
Step... |
14,260 | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
# TODO 1: Read in the advertising.csv file and set it to a data frame called ad_data.
# TODO: Your ... | <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 Dataset
Step2: Check the head of ad_data
Step3: Use info and describe() on ad_data
Step4: Let's check for any null values.
Step5: E... |
14,261 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'sandbox-2', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "ema... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
14,262 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import string as str
train_data = pd.read_csv('train_data.csv',header = 0)
y = train_data["Target"]
y = y.values #convert to ndarray
train_data = train_data.drop("Target",1)
x = train_data.values
x = np.c_[np.ones(x.shape[0]),x]
def calculateCost (H... | <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: x ==> Array of Feature Values
Step2: If anyone's wondering why I didn't transpose my Parameters array, one dimensional arrays do not need to be... |
14,263 | <ASSISTANT_TASK:>
Python Code:
from fretbursts import *
sns = init_notebook()
import lmfit; lmfit.__version__
import phconvert; phconvert.__version__
url = 'http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5'
download_file(url, save_dir='./data')
filename = "./data/12d_New_30p_320mW_steer_3.hdf5"
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:
Step1: Downloading the sample data file
Step2: Selecting a data file
Step3: Data load and Burst search
Step4: For convenience we can set the correct... |
14,264 | <ASSISTANT_TASK:>
Python Code:
from cave.cavefacade import CAVE
#cave = CAVE(["workflow-result"], "test_jupyter", ["."], file_format='BOHB')
cave = CAVE(folders=["./smac3/example_output/run_1", "./smac3/example_output/run_2"],
output_dir="cave_on_jupyter",
ta_exec_dir=["./smac3"],
ve... | <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 use the CAVE-object to generate general information in tables
Step2: Performance Analysis
Step3: Only available for instances, CAVE can... |
14,265 | <ASSISTANT_TASK:>
Python Code:
# %reload_ext autoreload
# %autoreload 2
%matplotlib inline
import torch
import torchvision
import numpy as np
# import mnist_loader
# train, valid, test = mnist_loader.load_data(path='data/mnist/')
# torchvision datasets are PIL.Image images of range [0,1]. Must trsfm them
# to Tensors... | <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. Data Loading
Step2: 2. Network Definition
Step3: 3. Loss Function & Optimizer Definitions
Step4: 4. Training
Step5:
Step6: NOTE
Step7: ... |
14,266 | <ASSISTANT_TASK:>
Python Code:
# import math lib
from math import pi
# import Qiskit
from qiskit import Aer, IBMQ, execute
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
# import basic plot tools
from qiskit.tools.visualization import plot_histogram
# To use local qasm simulator
backend = Aer.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: In this section, we first judge the version of Python and import the packages of qiskit, math to implement the following code. We show our algor... |
14,267 | <ASSISTANT_TASK:>
Python Code:
truth = "This is some text.\nMore text, but on a different line!\nInsert your favorite meme here.\n"
pred = read_file_contents("q1data/file1.txt")
assert truth == pred
retval = -1
try:
retval = read_file_contents("nonexistent/path.txt")
except:
assert False
else:
assert retval... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: B
Step2: C
Step3: D
|
14,268 | <ASSISTANT_TASK:>
Python Code:
!pip install --upgrade pymongo
from pprint import pprint as pp
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
matplotlib.style.use('ggplot')
%%bash
sudo apt-get update
sudo apt-get install -y mongodb-clients
%%bash
cat << END | mongo --host mongo... | <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: Usaremos la librería pymongo para python. La cargamos a continuación.
Step2: La conexión se inicia con MongoClient en el host descrito en el fi... |
14,269 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
dta = sm.datasets.macrodata.load_pandas().data
index = pd.Index(sm.tsa.datetools.dates_from_range("1959Q1", "2009Q3"))
print(index)
dta.index = index
del dta["year"]
del dta["quarter"]
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: Hodrick-Prescott Filter
Step2: Baxter-King approximate band-pass filter
Step3: We lose K observations on both ends. It is suggested to use K=1... |
14,270 | <ASSISTANT_TASK:>
Python Code:
# Make a dictionary with {} and : to signify a key and a value
my_dict = {'key1':'value1','key2':'value2'}
# Call values by their key
my_dict['key2']
my_dict = {'key1':123,'key2':[12,23,33],'key3':['item0','item1','item2']}
#Lets call items from the dictionary
my_dict['key3']
# Can call ... | <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: Its important to note that dictionaries are very flexible in the data types they can hold. For example
Step2: We can effect the values of a key... |
14,271 | <ASSISTANT_TASK:>
Python Code:
import sys, os
from adaptivemd import Project, Event, FunctionalEvent, Trajectory
project = Project('tutorial')
print project.tasks
print project.trajectories
print project.models
engine = project.generators['openmm']
modeller = project.generators['pyemma']
pdb_file = project.files['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: Let's open our test project by its name. If you completed the previous example this should all work out of the box.
Step2: Open all connections... |
14,272 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import plotly.plotly as py
import plotly.graph_objs as go
df = pd.read_csv('./asset/sydney_housing_market.txt', sep='\t')
df.head()
pd.pivot_table(df, index=['type'])
pd.pivot_table(df, index=['type'], aggfunc={'distance_to_CBD':np.mean, 'sold':np... | <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 data
Step2: Pivot Table
Step3: Note that the default aggregation function is np.mean. We can specify the aggregation function in the ag... |
14,273 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ncar', 'sandbox-3', 'toplevel')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "e... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
14,274 | <ASSISTANT_TASK:>
Python Code:
!which howdoi
!howdoi --help #注意我这里是在jupyter notebook里面直接使用的，所以需要加感叹号。如果是在terminal上，不需要加叹号。
!howdoi --num-answers 3 python lambda function list comprehension
!howdoi --num-answer 3 python numpy array create
!ls /Users/ywfang/FANG/git/howdoi_ywfang/howdoi
!sed -n '70,120p' /Users/ywfa... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 通过帮助文档，我们可以了解到HowDoI大概的工作模式以及它的一些功能，例如可以colorize the output，get multiple answers，
Step2: Read HowDoI's code
Step3: 通过浏览howdoi.py，我们发现这里面定义了很多新... |
14,275 | <ASSISTANT_TASK:>
Python Code:
from array import array
import reprlib
import math
import numbers
import functools
import operator
import itertools
class Vector:
typecode = 'd'
def __init__(self, components):
self._components = array(self.typecode, components)
def __iter__(self):
r... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 因为 Vector 实例是可迭代对象，而且 Vector.__init__ 的参数是可迭代对象，所以我们的 __neg__ 和 _pos__ 的实现短小精悍
Step2: 虽然每个 +one_third 表达式都会使用 one_third 的值创建一个新的 Decimal 实例，但是会... |
14,276 | <ASSISTANT_TASK:>
Python Code:
import yaml
# Set `PATH` to include the directory containing TFX CLI and skaffold.
PATH = %env PATH
%env PATH=/home/jupyter/.local/bin:{PATH}
!python -c "import tfx; print('TFX version: {}'.format(tfx.__version__))"
!python -c "import kfp; print('KFP version: {}'.format(kfp.__version__))... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Validate lab package version installation
Step2: Note
Step3: Note
Step4: The config.py module configures the default values for the environme... |
14,277 | <ASSISTANT_TASK:>
Python Code:
def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].uppe... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Lesson
Step2: Project 1
|
14,278 | <ASSISTANT_TASK:>
Python Code:
from pandas import DataFrame, read_csv
from estnltk import Text
from estnltk.taggers import EventTagger
event_vocabulary = DataFrame([['Harv', 'sagedus'],
['tugev peavalu', 'sümptom']],
columns=['term', 'type'])
eve... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Example 1
Step2: or file event vocabulary.csv in csv format
Step3: or list of dicts
Step4: There must be one key (column) called term in even... |
14,279 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn import datasets
from sklearn import svm
iris = datasets.load_iris()
# Split the iris data into train/test data sets with 40% reserved for testing
X_train, X_test, y_train, y_test = train... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: A single train/test split is made easy with the train_test_split function in the cross_validation library
Step2: K-Fold cross validation is jus... |
14,280 | <ASSISTANT_TASK:>
Python Code:
# run h2o Kmeans
# Import h2o library
import h2o
from h2o.estimators import H2OKMeansEstimator
# init h2o cluster
h2o.init(strict_version_check=False, url="http://192.168.59.147:54321")
# load data
import pandas as pd
data = pd.read_csv("../../smalldata/chicago/chicagoAllWeather.csv")
da... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Data - Chicago Weather dataset
Step2: Traditional K-means
Step3: Constrained K-means reduced data using Aggregator - changed size 1/2 of origi... |
14,281 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
p, X = load_data()
assert type(X) == np.ndarray
km = KMeans()
km.fit(X)
d = km.transform(X)[:, p]
indexes = np.argsort(d)[::][:100]
closest_100_samples = X[indexes]
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
14,282 | <ASSISTANT_TASK:>
Python Code:
import h5py
import numpy as np
h5file = h5py.File("/Users/users/breddels/src/vaex/data/helmi-dezeeuw-2000-10p.hdf5", "r")
FeH = h5file["/data/FeH"]
# FeH is your regular numpy array (with some extras)
print("mean FeH", np.mean(FeH), "length", len(FeH))
print(FeH.attrs["ucd"], FeH.attrs["... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: More information about a column can be found using
|
14,283 | <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: 텐서플로 허브와 전이학습
Step2: ImageNet 분류기
Step3: 싱글 이미지 실행시키기
Step4: 차원 배치를 추가하세요, 그리고 이미지를 모델에 통과시키세요.
Step5: 그 결과는 로지트의 1001 요소 벡터입니다. 이는 이미지에 대한 ... |
14,284 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from IPython.display import HTML
import ipywidgets as widgets
from IPython.display import display
L = 200
dx = .5
U = lambda x:(0.*x)
buttonrunsim = widgets.Button(description="Simulate")
buttongen... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Código que controla las simulaciones
Step2: Rutinas que generan inicializan y generan la simulación
Step3: Rutinas que conectan los controles ... |
14,285 | <ASSISTANT_TASK:>
Python Code:
import graphlab
graphlab.canvas.set_target('ipynb')
loans = graphlab.SFrame('lending-club-data.gl/')
loans.column_names()
loans['grade'].show()
loans['home_ownership'].show()
# safe_loans = 1 => safe
# safe_loans = -1 => risky
loans['safe_loans'] = loans['bad_loans'].apply(lambda x ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load LendingClub dataset
Step2: Exploring some features
Step3: Here, we see that we have some feature columns that have to do with grade of th... |
14,286 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from pennies.market.interpolate import CubicSplineWithNodeSens
def f(x):
return np.sin(x)
x = 0.5 * np.arange(10)
y = f(x)
for i in range(len(x)):
print('({}, {}'.format(x[i],y[i]))
cs_sens = CubicSplineWithN... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: y = sin(x)
Step2: Sensitivity to nodes
|
14,287 | <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
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Description:
Step1: SavedModel 形式の使用
Step2: 実行例として、グレース・ホッパーの画像と Keras の次元トレーニング済み画像分類モデルを使用します（使いやすいため）。カスタムモデルも使用できますが、これについては後半で説明します。
Step3: この画像の予測トップは「軍服」です... |
14,288 | <ASSISTANT_TASK:>
Python Code:
import os, csv, lzma
import numpy as np
import open_cp.sources.chicago
import geopandas as gpd
import pyproj
import shapely.geometry
#datadir = os.path.join("/media", "OTHERDATA")
datadir = os.path.join("..", "..", "..", "..", "Data")
open_cp.sources.chicago.set_data_directory(datadir)
p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Get our favourite, the southside
Step2: Process the data
|
14,289 | <ASSISTANT_TASK:>
Python Code:
a = 10
print(a)
import time
time.sleep(10)
import sys
from ctypes import CDLL
# This will crash a Linux or Mac system
# equivalent calls can be made on Windows
# Uncomment these lines if you would like to see the segfault
# dll = 'dylib' if sys.platform == 'darwin' else 'so.6'
# libc = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: There are two other keyboard shortcuts for running code
Step2: If the Kernel dies you will be prompted to restart it. Here we call the low-leve... |
14,290 | <ASSISTANT_TASK:>
Python Code:
# Download example dataset
from msmbuilder.example_datasets import FsPeptide
fs_peptide = FsPeptide(verbose=False)
fs_peptide.cache()
# Work in a temporary directory
import tempfile
import os
os.chdir(tempfile.mkdtemp())
from msmbuilder.dataset import dataset
xyz = dataset(fs_peptide.dat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The dataset object
Step2: Featurization
Step3: Preprocessing
Step4: Intermediate kinetic model
Step5: tICA Histogram
Step6: Clustering
Step... |
14,291 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import openmc
uo2 = openmc.Material(1, "uo2")
print(uo2)
mat = openmc.Material()
print(mat)
help(uo2.add_nuclide)
# Add nuclides to uo2
uo2.add_nuclide('U235', 0.03)
uo2.add_nuclide('U238', 0.97)
uo2.add_nuclide('O16', 2.0)
uo2.set_density('g/cm3', 10.0)
zirconium... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Defining Materials
Step2: On the XML side, you have no choice but to supply an ID. However, in the Python API, if you don't give an ID, one wil... |
14,292 | <ASSISTANT_TASK:>
Python Code:
import zipfile
from io import BytesIO
import cv2
import gdown
import matplotlib.pyplot as plt
import numpy as np
import requests
import tensorflow as tf
import tensorflow_hub as hub
from PIL import Image
from sklearn.preprocessing import MinMaxScaler
from tensorflow import keras
RESOLUTI... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Constants
Step2: Data utilities
Step3: Load a test image and display it
Step4: Load a model
Step5: More about the model
Step6: attention_sc... |
14,293 | <ASSISTANT_TASK:>
Python Code:
m.layer_names
channel = m.monitor.channels["valid_y_nll"]
hl.Curve(zip(channel.epoch_record, channel.val_record),label="valid_y_nll")
channel = m.monitor.channels["valid_y_nll"]
plt.plot(channel.epoch_record, channel.val_record)
ch1 = m.monitor.channels["valid_y_nll"]
ch2 = m.monitor.cha... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Hard to see whether it is still learning...
|
14,294 | <ASSISTANT_TASK:>
Python Code:
# Authors: Teon Brooks <teon.brooks@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
from mne.report import Report
from mne.datasets import sample
from mne import read_evokeds
from matplotlib import pyplot as plt
data_path = sample.data_path()
meg_pa... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Do standard folder parsing (this can take a couple of minutes)
Step2: Add a custom section with an evoked slider
|
14,295 | <ASSISTANT_TASK:>
Python Code:
import pickle
import numpy as np
import matplotlib.pyplot as plt
import scipy.sparse.linalg as spsla
import tectosaur as tct
with open('wenchuan_mesh.pkl', 'rb') as f:
m = pickle.load(f)
m.n_tris(), m.n_tris('surf'), m.n_tris('fault')
plt.figure(figsize = (10,10))
plt.triplot(m.pts... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: And the premade mesh that we're going to use
Step2: m is now a CombinedMesh object which is a handy class for tracking different subsets of a m... |
14,296 | <ASSISTANT_TASK:>
Python Code:
from datetime import datetime
print "Notebook last modified/run: {}".format(datetime.now())
from math import factorial
kNumBits = 1.e12 # 'n' in the equation, above.
kBER = 1.e-12 # 'p-sub-A'.
def prob(m):
'Probabillity of observing m errors.'
return pow(kBER, m) * p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Contents <a id="contents"/>
Step2: I had to give up, after 5 hours, and interrupt the code, above. What happened?!
Step3: The problem is the r... |
14,297 | <ASSISTANT_TASK:>
Python Code:
import os
import shutil
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard
from tensorflow.keras.layers import Dense, Flatten, Softmax
print(tf.__version__)
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Exploring the data
Step2: Each image is 28 x 28 pixels and represents a digit from 0 to 9. These images are black and white, so each pixel is a... |
14,298 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
from os.path import join
from pylab import rcParams
import matplotlib.pyplot as plt
rcParams['figure.figsize'] = (13, 6)
plt.style.use('ggplot')
#import nilmtk
from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore
from ni... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: show versions for any diagnostics
Step2: Load dataset
Step3: Let us perform our analysis on selected 2 days
Step4: Hart Training
Step5: Hart... |
14,299 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import openpathsampling as paths
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from openpathsampling.visualize import PathTreeBuilder, PathTreeBuilder
from IPython.display import SVG, HTML
def ipynb_visualize(movevis):
Default settings to sh... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Single Replica TIS
Step2: Open the storage and load things from it.
Step3: One of the points of SRTIS is that we use a bias (which comes from ... |
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