Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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14,800 | <ASSISTANT_TASK:>
Python Code:
factors(689)
max_seq_len = 682
#full_train_size = 55820
#train_size = 55800
#small_train_size = 6000 #just because of performance reasons, no statistics behind this decision
#test_size = 6200
data_path = '../../../../Dropbox/data'
phae_path = data_path + '/price_hist_autoencoder'
csv_in =... | <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: Step 1 - collect data
Step2: Step 2 - Build model
Step3: targets
Step4: Quick test run
Step5: Step 3 training the network
Step6: Conclusion... |
14,801 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
# All the imports
from __future__ import print_function, division
import pom3_ga, sys
import pickle
# TODO 1: Enter your unity ID here
__author__ = "pwang13"
def normalize(problem, points):
Normalize all the objectives
in each point and return them
meta = ... | <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: To compute most measures, data(i.e objectives) is normalized. Normalization is scaling the data between 0 and 1. Why do we normalize?
Step10: D... |
14,802 | <ASSISTANT_TASK:>
Python Code:
class Stack:
def __init__(self):
self.items = []
def isEmpty(self):
return self.items == []
def push(self, item):
self.items.append(item)
def pop(self):
return self.items.pop()
def peek(self):
return self.items... | <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: cf. 3 Stacks and Queues, Cracking the Coding Interview, 6th Ed., McDowell, stack uses LIFO - as in a stack of dinner plates, the most recent ite... |
14,803 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
sys.path.append(module_path + '/rl_coach')
from typing import Union
import numpy as np
from rl_coach.agents.ddpg_agent import DDPGAgent, DDPGAge... | <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: Now let's define the HAC algorithm and agent parameters.
Step2: Now we'll define the agent itself - HACDDPGAgent - which subclasses the DDPG ag... |
14,804 | <ASSISTANT_TASK:>
Python Code::
from sklearn.model_selection import GridSearchCV
import xgboost as xgb
# create a dictionary containing the hyperparameters
# to tune and the range of values to try
PARAMETERS = {"subsample":[0.75, 1],
"colsample_bytree":[0.75, 1],
"max_depth":[2, 6],
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
14,805 | <ASSISTANT_TASK:>
Python Code:
#Import libraries
import sys, os
import pandas as pd
import numpy as np
#Get file names; these files are created by the CreateUsageTable.py and CreateSupplyTable.py respectively
dataDir = '../../Data'
tidyuseFN = dataDir + os.sep + "UsageDataTidy.csv"
tidysupplyFN = dataDir + os.sep + "Su... | <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: Summarize USE table by county
Step2: Import and summarize supply table by county
Step3: Join Use and Supply Tables on Year and FIPS
Step4: Su... |
14,806 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from __future__ import print_function # only necessary if using Python 2.x
import numpy as np
from pyshtools import SHCoeffs
lmax = 30
coeffs = SHCoeffs.from_zeros(lmax)
coeffs.set_coeffs(values=[1], ls=[10], ms=[0])
grid = coeffs.expand()
fig, ax = grid.plot3d(elevati... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: To plot the data, we first expand it on a grid, and then use the method plot3d()
Step2: Let's try a somewhat more complicated function. Here we... |
14,807 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
# if using a Jupyter notebook, includue:
%matplotlib inline
mu = 80
sigma = 7
x = np.random.normal(mu, sigma, size=200)
plt.hist(x, 20,
density=True,
histtype='bar',
facecolor='b',
alpha=0.5)
plt.show... | <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: For our dataset, let's define a mean (average) mu = 80 and a standard deviation (spread) sigma = 7. Then we'll use numpy's np.random.normal() fu... |
14,808 | <ASSISTANT_TASK:>
Python Code:
# server = subprocess.Popen(['python', '../go_persistent_server.py'])
# time.sleep(3)
# web = subprocess.Popen(['python', '../go_web.py'])
# time.sleep(3)
web_interface = WebInterface()
results = web_interface.add_trigger(
'junk', 'insert_ts', None, 'db:one:ts')
assert results... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step8: Having set up the triggers, now insert the time series, and upsert the metadata
|
14,809 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
dftrain = pd.read_csv('data/bike_sharing_train.csv')
dfval = pd.read_csv('data/bike_sharing_val.csv')
dftest = pd.read_csv('data/bike_sharing_test.csv')
ntrain = len(dftrain)
nval = len(dftrain) + len(dfval)
df = pd.concat([dftrain,dfval,dftest])
pri... | <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: b) Entrene un árbol de regresión para resolver el problema usando parámetros por defecto. Con este fin, construya una matriz $X_{train}$ de form... |
14,810 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
train = pd.read_csv('titanic_train.csv')
train.head()
sns.heatmap(train.isnull(),yticklabels=False,cbar=False,cmap='viridis')
sns.set_style('whitegrid')
sns.countplot(x='Surv... | <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 Data
Step2: Exploratory Data Analysis
Step3: Roughly 20 percent of the Age data is missing. The proportion of Age missing is likely small ... |
14,811 | <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 wr... | <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: Adversarial regularization for image classification
Step2: Import libraries. We abbreviate neural_structured_learning to nsl.
Step3: Hyperpara... |
14,812 | <ASSISTANT_TASK:>
Python Code:
PROJECT_ID = "YOUR PROJECT ID"
BUCKET_NAME = "gs://YOUR BUCKET NAME"
REGION = "YOUR REGION"
SERVICE_ACCOUNT = "YOUR SERVICE ACCOUNT"
content_name = "pt-img-cls-gpu-cust-cont-torchserve"
gcs_output_uri_prefix = f"{BUCKET_NAME}/{content_name}"
! gsutil ls $gcs_output_uri_prefix
! curl -O ... | <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 Artifact
Step2: Vertex Prediction using Custom TorchServe Container
Step3: Model Archive for TorchServe
Step4: Option
Step5: Initia... |
14,813 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import itertools
import urllib2
import scipy.stats as stats
%matplotlib inline
np.set_printoptions(precision=3, threshold=1000000, suppress=True)
np.random.seed(1)
alpha = .025
url = ('https://raw.githubusercontent... | <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: Step 4a
Step2: Step 4b
Step3: Step 5
Step4: Apply to data
|
14,814 | <ASSISTANT_TASK:>
Python Code:
# setup
from pyrise import products as prod
obsid = prod.OBSERVATION_ID('PSP_003072_0985')
# test orbit number
assert obsid.orbit == '003072'
# test setting orbit property
obsid.orbit = 4080
assert obsid.orbit == '004080'
# test repr
assert obsid.__repr__() == 'PSP_004080_0985'
# test ta... | <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: PRODUCT_ID
Step2: SOURCE_PRODUCT_ID
Step3: http
Step4: HiRISE_URL
Step5: others
|
14,815 | <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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<USER_TASK:>
Description:
Step1: 使用 tf.data 加载文本数据
Step2: 三个版本的翻译分别来自于
Step3: 将文本加载到数据集中
Step4: 将这些标记的数据集合并到一个数据集中，然后对其进行随机化操作。
Step5: 你可以使用 tf.data.Dataset.take 与 print 来查看... |
14,816 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from SimPEG import Mesh
import matplotlib.pyplot as plt
%matplotlib inline
plt.set_cmap(plt.get_cmap('viridis')) # use a nice colormap!
# define a 1D mesh
mesh1D = Mesh.TensorMesh([5]) # with 5 cells
fig, ax = plt.subplots(1,1, figsize=(12,2))
ax.plot(mesh1D.gridN, np.... | <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: Over a single cell, the divergence is
Step2: Doing it as a for loop is easy to program for the first time,
Step3: the above is still a loop.... |
14,817 | <ASSISTANT_TASK:>
Python Code:
from beakerx import *
import pandas as pd
tableRows = pd.read_csv('../resources/data/interest-rates.csv')
Plot(title="Title",
xLabel="Horizontal",
yLabel="Vertical",
initWidth=500,
initHeight=200)
x = [1, 4, 6, 8, 10]
y = [3, 6, 4, 5, 9]
pp = Plot(title='Bars, Lines, ... | <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: Plot items
Step2: Lines, Points with Pandas
Step3: Areas, Stems and Crosshair
Step4: Constant Lines, Constant Bands
Step5: TimePlot
Step6: ... |
14,818 | <ASSISTANT_TASK:>
Python Code:
# Run some setup code for this notebook.
import sys
import os
sys.path.append('..')
import graphlab
products = graphlab.SFrame('datasets/')
products['sentiment']
products.head(10)['name']
print '# of positive reviews =', len(products[products['sentiment']==1])
print '# of negative revi... | <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: Load review dataset
Step2: One column of this dataset is 'sentiment', corresponding to the class label with +1 indicating a review with positiv... |
14,819 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
def find_peaks(x):
a = np.array(x)
l = []
for i in range(len(a)):
if i == 0 and a[i] > a[i+1]:
l.append(i)
elif i == len(a)-1 and a[i]> a[i-1]:
... | <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: Peak finding
Step2: Here is a string with the first 10000 digits of $\pi$ (after the decimal). Write code to perform the following
|
14,820 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from mpl_toolkits.basemap import Basemap
from shapely.geometry import Point, Polygon, MultiPoint, MultiPolygon
from shapely.prepared import prep
import fiona
from matplotlib.collections import PatchCollection
from desc... | <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: Data Wrangling
Step2: data manipulation
Step3: calculate speed during trips (in km/hr)
Step4: Make a new dataframe containing the difference ... |
14,821 | <ASSISTANT_TASK:>
Python Code:
# RUN THIS CELL FIRST!!!
import time
from pydrill.client import PyDrill
import psycopg2
import pandas as pd
drill = PyDrill(host='128.206.116.250', port=8048)
if not drill.is_active():
raise ImproperlyConfigured('Please run Drill first')
# Start the Timer
start = time.perf_counter()
... | <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: Pydrill
Step3: Postgres
Step5: Simple enough. Just as a reminder, the % operator in SQL matches any or no characters.
Step7: Postgres
Step9: ... |
14,822 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
#export
from exp.nb_00 import *
import operator
def test(a,b,cmp,cname=None):
if cname is None: cname=cmp.__name__
assert cmp(a,b),f"{cname}:\n{a}\n{b}"
def test_eq(a,b): test(a,b,operator.eq,'==')
test_eq(TEST,'test')
# To ru... | <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: Jump_to lesson 8 video
Step2: Get data
Step3: Initial python model
Step4: Matrix multiplication
Step5: This is kinda slow - what if we could... |
14,823 | <ASSISTANT_TASK:>
Python Code:
# enable plotting in notebook
%matplotlib notebook
from simulation_results import example_simulations
import physical_validation
simulation_nvt_vrescale_low = example_simulations.get(
"900 water molecules, NVT at 298K with v-rescale thermostat"
)
num_molecules = 900
simulation_data_... | <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: The results imported here are the time series of kinetic and potential energy from example simulations, which are
Step2: Check NVT simulations
... |
14,824 | <ASSISTANT_TASK:>
Python Code:
# -- inputs
X_test[0]
# -- predicted output (using Keras)
yhat[0]
from tensorflow.core.framework import graph_pb2
# -- read in the graph
f = open("models/graph.pb", "rb")
graph_def = graph_pb2.GraphDef()
graph_def.ParseFromString(f.read())
import tensorflow as tf
# -- actually import the... | <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: Inspect the protobuf containing the model's architecture and logic
|
14,825 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
view_sentence_range = (0, 10)
DON'T MODIFY AN... | <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: Language Translation
Step3: Explore the Data
Step6: Implement Preprocessing Function
Step8: Preprocess all the data and save it
Step10: Chec... |
14,826 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from pylab import *
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
# Now moving on to the SFR-M*-Size analysis
%run ~/Dropbox/pythonCode/LCSanalyzeblue.py
# using John Moustakas's stellar mass estimates
figure()
plot(s.s.ABSMAG[:,4][s.blueflag2],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: As of 1/6/16, need to make one more pass through the sample and remove galaxies that are blended with nearby companion. Not sure if people thin... |
14,827 | <ASSISTANT_TASK:>
Python Code:
# Import py_entitymatching package
import py_entitymatching as em
import os
import pandas as pd
# Get the datasets directory
datasets_dir = em.get_install_path() + os.sep + 'datasets'
# Get the paths of the input tables
path_A = datasets_dir + os.sep + 'person_table_A.csv'
path_B = datas... | <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: Then, read the (sample) input tables for blocking purposes
Step2: Removing Features from Feature Table
|
14,828 | <ASSISTANT_TASK:>
Python Code:
# Import the needed packages, SymPy
import sympy as sp
from sympy import init_printing
init_printing()
# Define the variables
# Complex variable
s = sp.symbols('s')
# FOTD Coeffficients
T1,T2,T3,T4 = sp.symbols('T_1 T_2 T_3 T_4')
K1,K2,K3,K4 = sp.symbols('K_1 K_2 K_3 K_4')
# Time Delay Co... | <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: Interpretation
Step2: We now have a system of 4 Equations we can set to zero. We have to solve for four variables, the parameter of the decoupl... |
14,829 | <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
from IPython.display import SVG
s =
<svg width="100" height="100">
<circle cx="50" cy="50" r="20" fill="aquamarine" /... | <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: Interact with SVG display
Step5: Write a function named draw_circle that draws a circle using SVG. Your function should take the parameters of ... |
14,830 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import interp1d
with np.load('trajectory.npz') as data:
t = data['t']
x = data['x']
y = data['y']
print(t,x,y)
assert isinstance(x, np.ndarray) and len(x)==40
assert isinstance(y, 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: 2D trajectory interpolation
Step2: Use these arrays to create interpolated functions $x(t)$ and $y(t)$. Then use those functions to create the ... |
14,831 | <ASSISTANT_TASK:>
Python Code:
%run "../src/start_session.py"
%run "../src/recurrences.py"
%run "../src/sums.py"
from oeis import oeis_search, ListData
import knowledge
sys.setrecursionlimit(10000000)
s = oeis_search(id=45)
s(data_only=True, data_representation=ListData(upper_limit=20))
with bind(IndexedBase('f'), si... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: OEIS
Step2: Recurrence
Step3: Unfolding
Step4: Involution
Step5: Subsuming
Step6: We can abstract the following conjecture
Step7: Instanti... |
14,832 | <ASSISTANT_TASK:>
Python Code:
crisisInfo = {
"boston": {
"name": "Boston Marathon Bombing",
"time": 1366051740, # Timestamp in seconds since 1/1/1970, UTC
# 15 April 2013, 14:49 EDT -> 18:49 UTC
"directory": "boston",
"keywords": ["boston", "exploision", ... | <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: Choose Your Crisis
Step2: <hr>
Step3: <hr>
Step4: Top Twitter Users
Step5: Many of these tweets are not relevant to the event at hand.
Step6... |
14,833 | <ASSISTANT_TASK:>
Python Code:
b = phoebe.default_binary()
# set parameter values
b.set_value('q', value = 0.6)
b.set_value('incl', component='binary', value = 84.5)
b.set_value('ecc', 0.2)
b.set_value('per0', 63.7)
b.set_value('sma', component='binary', value= 7.3)
b.set_value('vgamma', value= -32.84)
# add an rv data... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Initialize the bundle
Step2: rv_geometry
Step3: The rv_geometry estimator is meant to provide an efficient starting point for q, vgamma, asini... |
14,834 | <ASSISTANT_TASK:>
Python Code:
!ls | grep "mo"
!wc -l anonymous-msweb-preprocessed.data && echo
!head anonymous-msweb-preprocessed.data
!cp anonymous-msweb-preprocessed.data log.txt
!cat mostFrequentVisitors.txt | cut -f 1,2 -d',' > urls.txt
!wc -l urls.txt && echo
!head urls.txt
%%writefile join.py
from mrjob.job i... | <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: Count lines in log dataset. View the first 10 lines. Rename data to log.txt
Step2: Convert the output of 4.4 to be just url and url_id. Save as... |
14,835 | <ASSISTANT_TASK:>
Python Code:
from time import time
import numpy as np
import matplotlib.pyplot as plt
from collections import deque
import random
%matplotlib inline
def benchmark(counts):
def times(f):
def ret():
timings = []
for c in counts:
start = time()
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Let's start with the obvious
Step2: Looks pretty linear to me. Apparently string += runs in O(1). That was honestly a shocking discovery to me.... |
14,836 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import sys
sys.path.append('../..')
from matplotlib import pylab
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
pylab.rcParams['figure.figsize'] = 12, 10
import functools
import numpy
import scipy
import scipy.special
from crocodile.clean 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: Generate baseline coordinates for an observation with a hypothetical north-pole VLA over 6 hours, with a visibility recorded every 10 minutes. T... |
14,837 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
tf.__version__
from tensorflow.contrib import keras
from keras.datasets import cifar100
(X_train, Y_train), (X_test, Y_test) = cifar100.load_data(label_mode='fine')
from keras import backend as K
img_rows, img_cols = 32, 32
if K.image_data_format() == 'channels_fi... | <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: Tensorboard Integration
Step2: TensorBoard Callback
Step4: ```python
Step5: Runing Tensorboard
Step6: tf.Queue integration with Keras
|
14,838 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
names = ["Name_2MASS","Name_alt","Spectral_Type","T_eff","AJ","L_bol","IMF"]
tbl6 = pd.read_csv("http://iopscience.iop.org/0067-0049/173/1/104/fulltext/71585.tb6.txt",
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Table 6 - Derived Properties for Members of Chamaeleon I
Step2: Custom analysis
Step3: Save data tables locally.
|
14,839 | <ASSISTANT_TASK:>
Python Code:
!python3 -m pip install pip --upgrade --quiet --user
!python3 -m pip install kfp --upgrade --quiet --user
!python3 -m pip install tfx==0.21.2 --quiet --user
# Set `PATH` to include user python binary directory and a directory containing `skaffold`.
PATH=%env PATH
%env PATH={PATH}:/home/j... | <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: In this example we'll need TFX SDK later than 0.21 to leverage the RuntimeParameter feature.
Step3: TFX Components
|
14,840 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set() # setting seaborn default for plots
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
train.head()
train.shape
train.describe()
train.describe(inclu... | <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: Loading Datasets
Step2: Looking into the training dataset
Step3: Below is a brief information about each columns of the dataset
Step4: We can... |
14,841 | <ASSISTANT_TASK:>
Python Code:
__author__ = "kyubyong. kbpark.linguist@gmail.com"
import numpy as np
np.__version__
x = np.arange(4).reshape((2, 2))
print("x=\n", x)
print("ans=\n", np.amin(x, 1))
x = np.arange(4).reshape((2, 2))
print("x=\n", x)
print("ans=\n", np.amax(x, 1, keepdims=True))
x = np.arange(10).reshap... | <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: Order statistics
Step2: Q2. Return the maximum value of x along the second axis. Reduce the second axis to the dimension with size one.
Step3: ... |
14,842 | <ASSISTANT_TASK:>
Python Code:
# Import required libraries
from tpot import TPOTClassifier
from sklearn.cross_validation import train_test_split
import pandas as pd
import numpy as np
#Load the data
Marketing=pd.read_csv('Data_FinalProject.csv')
Marketing.head(5)
Marketing.groupby('loan').y.value_counts()
Marketing.g... | <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 Exploration
Step2: Data Munging
Step3: At present, TPOT requires all the data to be in numerical format. As we can see below, our data se... |
14,843 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-10, 10, 201)
def f(x):
return x**2
y = f(x)
fig, ax = plt.subplots(1, figsize=(8,4))
ax.plot(x,y, 'g', label='line')
ax.fill_between(x,y, color='blue', alpha=0.3, label='area under graph')
ax.grid(True)
ax.legend()
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
Step2: <a id='Fundamental_Theorem_of_Calculus'></a>
|
14,844 | <ASSISTANT_TASK:>
Python Code:
# The kernel for this notebook is running Python 3, but we'll see:
from __future__ import print_function
import mxnet as mx
from mxnet import nd, autograd, gluon
mx.random.seed(1)
data_ctx = mx.cpu()
model_ctx = mx.cpu()
num_inputs = 2
num_outputs = 1
num_examples = 10000
def real_fn(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: Set the context
Step2: Linear regression
Step3: Notice that each row in X consists of a 2-dimensional data point and that each row in y consis... |
14,845 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
from scipy.io import loadmat
from sklearn.preprocessing import OneHotEncoder
data = loadmat('../data/andrew_ml_ex33507/ex3data1.mat')
data
X = data['X']
y = data['y']
X.shape, y.shape#看下维度
# 目前考虑输入是图片... | <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: 模型展示
Step2: 反向传播
Step3: 初始话参数
Step4: 反向传播
Step5: 梯度检验
|
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Python Code:
# -*- coding: utf-8 -*-
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from pyfme.aircrafts import Cessna310
from pyfme.environment.environment import Environment
from pyfme.environment.atmosphere import ISA1976
from pyfme.env... | <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: Initialize variables
Step2: Initial conditions
|
14,847 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (15.0, 8.0)
# First, we need to know what's in the data file.
!head -15 R11ceph.dat
class Cepheids(object):
def __init__(self,filename):
... | <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 Look at Each Host Galaxy's Cepheids
Step2: OK, now we are all set up! Let's plot one of the datasets.
Step3: Q
Step4: Q
Step5: Now, let's ... |
14,848 | <ASSISTANT_TASK:>
Python Code:
import graphlab
sales = graphlab.SFrame('kc_house_data.gl/')
import numpy as np # note this allows us to refer to numpy as np instead
def get_numpy_data(data_sframe, features, output):
data_sframe['constant'] = 1 # this is how you add a constant column to an SFrame
# add the 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: Load in house sales data
Step2: If we want to do any "feature engineering" like creating new features or adjusting existing ones we should do t... |
14,849 | <ASSISTANT_TASK:>
Python Code:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
import collections
import os
from google.colab import auth
auth.authenticate_user()
#@title Choices about the dataset you want to load.
#... | <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: If you choose chain_length 3 the data will look like this
Step3: Load the data.
Step4: Looking at what we loaded.
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Python Code:
# When not running on Kaggle, comment out this import
from kaggle_datasets import KaggleDatasets
# When not running on Kaggle, set a fixed GCS path here
GCS_PATH = KaggleDatasets().get_gcs_path('jigsaw-multilingual-toxic-comment-classification')
print(GCS_PATH)
import os, time, logging
i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Overview
Step2: TPU or GPU detection
Step3: Configuration
Step5: Model
Step6: Dataset
Step8: Set up our data pipelines for training and eva... |
14,851 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
%matplotlib inline
# 如果不知道函数名是什么，可以只敲击函数前几个，然后按tab键，就会有下拉框提示
titanic = pd.read_csv('train.csv')
titanic.head()
titanic.info()
# 把所有数值类型的数据做一个简单的统计
titanic.describe()
# 统计None值个数
titanic.isnull().sum()
# 可以填充整个datafram里边的空值, 可以取消注释，实验一下
# titanic.fillna(0)
# 单独选择一列进... | <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: 导入数据
Step2: 快速预览
Step3: | 单词 | 翻译
Step4: 处理空值
Step5: 尝试从性别进行分析
Step6: 通过上面图片可以看出：性别特征对是否生还的影响还是挺大的
Step7: 分析票价
Step8: 可以看出低票价的人的生还率比较低
S... |
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Python Code:
import rebound
sim = rebound.Simulation()
sim.add(m=1)
sim.add(m=0.1, e=0.041, a=0.4, inc=0.2, f=0.43, Omega=0.82, omega=2.98)
sim.add(m=1e-3, e=0.24, a=1.0, pomega=2.14)
sim.add(m=1e-3, e=0.24, a=1.5, omega=1.14, l=2.1)
sim.add(a=-2.7, e=1.4, f=-1.5,omega=-0.7) # hyperbolic orbit
%matpl... | <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: To plot these initial orbits in the $xy$-plane, we can simply call the OrbitPlot function and give it the simulation as an argument.
Step2: Not... |
14,853 | <ASSISTANT_TASK:>
Python Code:
import graphlab
sales = graphlab.SFrame('kc_house_data.gl/')
import numpy as np # note this allows us to refer to numpy as np instead
def get_numpy_data(data_sframe, features, output):
data_sframe['constant'] = 1 # this is how you add a constant column to an SFrame
# add the co... | <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: Load in house sales data
Step2: If we want to do any "feature engineering" like creating new features or adjusting existing ones we should do t... |
14,854 | <ASSISTANT_TASK:>
Python Code:
plot = ChristmasPlot('Fake', n_dataset=3, methods=['yass', 'kilosort', 'spyking circus'], logit_y=True, eval_type="Accuracy")
for method in plot.methods:
for i in range(plot.n_dataset):
x = (np.random.rand(30) - 0.5) * 10
y = 1 / (1 + np.exp(-x + np.random.rand()))
... | <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: Generate SNR vs Metric
Step2: Generate the curve plots
|
14,855 | <ASSISTANT_TASK:>
Python Code:
'\\'.join(['folder1','folder2','folder3','file.png']) # join all elements using the escaped (literal) '\' string
import os # contains many file path related functions
print(os.path.join('folder1','folder2','folder3','file.png')) # takes string arguments and returns OS-appropriate path
pr... | <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: But this string only works on Windows; to create an OS insensitive path, using the os module
Step2: If no explicit path is specified, Python wi... |
14,856 | <ASSISTANT_TASK:>
Python Code:
# Example Dataset Review Entry
__ = {
'beer/ABV': 7.2,
'beer/beerId': '59261',
'beer/brewerId': '67',
'beer/name': 'Sierra Nevada Torpedo Extra IPA',
'beer/style': 'India Pale Ale (IPA)',
'review/appearance': 1.0,
'review/aroma': 0.8,
'review/overal... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step5: User Level Results
Step7: High-level Feature Trends
Step8: Review Counts
Step9: Average Number of Beer Styles Reviewed
Step10: Average Overa... |
14,857 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 12, 10
import random
x = np.array([i*np.pi/180 for i in range(60,300,4)])
np.random.seed(10) #Setting seed for reproducability
y =... | <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: Creating the data
Step2: Fit simple linear regression
Step3: Determining overfitting
Step4: Fit a Linear regression model on the 15 powers
St... |
14,858 | <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: データ増強
Step2: データセットをダウンロードする
Step3: 花のデータセットには 5 つのクラスがあります。
Step4: データセットから画像を取得し、それを使用してデータ増強を実演してみましょう。
Step5: Keras 前処理レイヤーを使用する
Step6: ... |
14,859 | <ASSISTANT_TASK:>
Python Code:
# Imports
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
%matplotlib inline
## use `%matplotlib notebook` for interactive figures
# plt.style.use('ggplot')
import sklearn
import tigramite
from tigramite import data_processing as pp
from tigramite.toymodels ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 1. Structural causal processes with contemporaneous and lagged dependencies
Step2: The true graph $\mathcal{G}$ here has shape (N, N, 2+1) sinc... |
14,860 | <ASSISTANT_TASK:>
Python Code:
class MyClass:
def __init__(self, val):
self.set_val(val)
def get_val(self):
return self._val
def set_val(self, val):
if val > 0:
self._val = val
else:
raise ValueError('val must be greater 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: Wie wir sehen, ist die Eigenschaft _val durchaus von außerhalb verfügbar. Allerdings signalisiert das Underline, dass vom Programmierer der Klas... |
14,861 | <ASSISTANT_TASK:>
Python Code:
# Versão da Linguagem Python
from platform import python_version
print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
# Imports
import time
import numpy as np
import pandas as pd
import matplotlib as mat
from matplotlib import pyplot as plt
from sklearn.dat... | <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: Exercício
Step2: Extração e Transformação de Dados
Step3: Exploração de Dados
Step4: Plot
|
14,862 | <ASSISTANT_TASK:>
Python Code:
# AR parameters
p = 4
a = 1.0 * np.random.rand(p) - 0.5
print "Original AR parameters:\n", a
# Time series data
N = 1000
n = np.arange(0, N)
# Input white noise
eparam = (0, 1.0)
e = np.sqrt(eparam[1]) * np.random.randn(N) + eparam[0]
# Generate AR time series.
y = genARProcess(p, a, epar... | <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: Estimate AR parameters using the entire dataset
Step2: Whiten the AR process output using the $\hat{a}$
Step3: Non-stationary AR process with... |
14,863 | <ASSISTANT_TASK:>
Python Code:
from openhunt.mordorutils import *
spark = get_spark()
sd_file = "https://raw.githubusercontent.com/OTRF/Security-Datasets/master/datasets/atomic/windows/defense_evasion/host/empire_psinject_PEinjection.zip"
registerMordorSQLTable(spark, sd_file, "sdTable")
df = spark.sql(
'''
SELECT `@... | <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: Download & Process Security Dataset
Step2: Analytic I
|
14,864 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
import time
from matplotlib import rcParams
import matplotlib.pyplot as plt
%matplotlib inline
rcParams['figure.figsize'] = (13, 6)
plt.style.use('ggplot')
from nilmtk import DataSet, TimeFrame, MeterGroup, HDFDataStore
from nilmtk.disaggreg... | <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: Dividing data into train and test set
Step2: Let us use building 1 for demo purposes
Step3: Let's split data at April 30th
Step4: REDD data s... |
14,865 | <ASSISTANT_TASK:>
Python Code:
import os
from copy import deepcopy
import numpy as np
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_dat... | <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: Marking bad channels
Step2: Here you can see that the
Step3: We can do the same thing for the bad MEG channel (MEG 2443). Since we
Step4: No... |
14,866 | <ASSISTANT_TASK:>
Python Code:
from pandas import DataFrame
import sqlite3
query =
CREATE TABLE test
(a VARCHAR(20), b VARCHAR(20),
c REAL, d INTEGER
);
con = sqlite3.connect(':memory:')
con.execute(query)
con.commit()
data = [('Atlanta', 'Georgia', 1.25, 6),
('Tallahassee', 'Florida', 2.6, 3),
... | <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: Loading data from SQL into a DataFrame is fairly straightforward, and pandas has some functions to simplify the process. As an example, I’ll use... |
14,867 | <ASSISTANT_TASK:>
Python Code:
#general imports
import pygslib
#get the data in gslib format into a pandas Dataframe
cluster= pygslib.gslib.read_gslib_file('../datasets/cluster.dat')
true= pygslib.gslib.read_gslib_file('../datasets/true.dat')
true['Declustering Weight'] = 1
npoints = len(cluster['Primary'])
tru... | <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: Getting the data ready for work
Step2: QQ-Plot
|
14,868 | <ASSISTANT_TASK:>
Python Code:
import os.path as op
import numpy as np
import mne
data_path = mne.datasets.opm.data_path()
subject = 'OPM_sample'
subjects_dir = op.join(data_path, 'subjects')
raw_fname = op.join(data_path, 'MEG', 'OPM', 'OPM_SEF_raw.fif')
bem_fname = op.join(subjects_dir, subject, 'bem',
... | <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: Prepare data for localization
Step2: Examine our coordinate alignment for source localization and compute a
Step3: Perform dipole fitting
Step... |
14,869 | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.0,<2.1"
%matplotlib inline
import phoebe
from phoebe import u
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary(contact_binary=True)
b.add_dataset('lc', times=np.linspace(0,1,101), dataset='lc01')
b.add_datas... | <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: As always, let's do imports and initialize a logger and a new bundle. See Building a System for more details.
Step2: Adding Datasets and Compu... |
14,870 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
! grep "multipv 1" log4.txt | grep -v lowerbound | grep -v upperbound > log4_g.txt
def parse_info(l):
D = {}
k = l.split()
i = 0
assert k[i] == "info"
i += 1
while i < len(k):
if k[i] == "depth":
D[k[i]] = int(k[i+1])
... | <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: The Speed of Search
Step2: So nodes per second is roughly constant
Step3: The hashtable usage is at full capacity
Step4: Number of nodes need... |
14,871 | <ASSISTANT_TASK:>
Python Code:
import jax.numpy as jnp
from jax import grad, jit, vmap
from jax import random
# A helper function to randomly initialize weights and biases
# for a dense neural network layer
def random_layer_params(m, n, key, scale=1e-2):
w_key, b_key = random.split(key)
return scale * random.norma... | <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: Hyperparameters
Step2: Auto-batching predictions
Step3: Let's check that our prediction function only works on single images.
Step5: At this ... |
14,872 | <ASSISTANT_TASK:>
Python Code:
!pip install tensorflow==1.15 dm-sonnet==1.36 tensor2tensor==1.14
import time
import numpy as np
import tensorflow.compat.v1 as tf
tf.logging.set_verbosity(tf.logging.ERROR) # Hide TF deprecation messages
import matplotlib.pyplot as plt
%cd /tmp
%rm -rf /tmp/deepmind_research
!git clone ... | <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: Download pre-trained model weights from Google Cloud Storage
Step2: Pre-trained model config
Step3: Generate class-conditional samples
Step4: ... |
14,873 | <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: 在 Tensorflow 中训练提升树（Boosted Trees）模型
Step2: 数据集由训练集和验证集组成：
Step3: 训练集和评估集分别有 627 和 264 个样本。
Step4: 大多数乘客在 20 岁或 30 岁。
Step5: 男乘客大约是女乘客的两倍。
S... |
14,874 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import scipy as sp
from scipy import stats
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
exec(open('settings.py').read(), globals())
cell_numbers = pd.read_csv('../data/cell_number_data.csv')
outgrowth = pd.read_csv('../... | <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: Check for significant differences
Step2: ttest vs day0
Step3: Plot
|
14,875 | <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: pix2pix
Step2: Load the dataset
Step3: Each original image is of size 256 x 512 containing two 256 x 256 images
Step4: You need to separate r... |
14,876 | <ASSISTANT_TASK:>
Python Code:
def insertion_sort(unsorted_list):
x = ipytracer.List1DTracer(unsorted_list)
display(x)
for i in range(1, len(x)):
j = i - 1
key = x[i]
while x[j] > key and j >= 0:
x[j+1] = x[j]
j = j - 1
x[j+1] = key
return x.data
... | <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: work
Step2: Code2 - ChartTracer
Step3: work
|
14,877 | <ASSISTANT_TASK:>
Python Code:
# As usual, a bit of setup
from __future__ import print_function
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_... | <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: Fully-Connected Neural Nets
Step4: Affine layer
Step5: Affine layer
Step6: ReLU layer
Step7: ReLU layer
Step8: "Sandwich" layers
Step9: Lo... |
14,878 | <ASSISTANT_TASK:>
Python Code:
import graphlab
sales = graphlab.SFrame('kc_house_data.gl/kc_house_data.gl')
c = sales.random_split(.8,seed=0)
train_data=c
# Let's compute the mean of the House Prices in King County in 2 different ways.
prices = sales['price'] # extract the price column of the sales SFrame -- this is... | <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 house sales data
Step2: Split data into training and testing
Step3: Useful SFrame summary functions
Step4: As we see we get the same ans... |
14,879 | <ASSISTANT_TASK:>
Python Code:
import os
import re
import operator
import matplotlib.pyplot as plt
import warnings
import gensim
import numpy as np
warnings.filterwarnings('ignore') # Let's not pay heed to them right now
import nltk
nltk.download('stopwords') # Let's make sure the 'stopword' package is downloaded & up... | <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: Analysing our corpus.
Step4: Preprocessing our data. Remember
Step5: Finalising our dictionary and corpus
Step6: Topic modeling with LSI
Step... |
14,880 | <ASSISTANT_TASK:>
Python Code:
def countGreater(arr , n , k ) :
l = 0
r = n - 1
leftGreater = n
while(l <= r ) :
m = int(l +(r - l ) / 2 )
if(arr[m ] > k ) :
leftGreater = m
r = m - 1
else :
l = m + 1
return(n - leftGreater )
if __name__== ' __main __' :
arr =[3 , 3 , 4 , 7 , 7 , ... | <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:
|
14,881 | <ASSISTANT_TASK:>
Python Code:
import copy
import cPickle
import glob
import gzip
import os
import random
import shutil
import subprocess
import cdpybio as cpb
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import peer
import pybedtools as pbt
import scipy.stats as stats
import seaborn as sns
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: Cohort Information
Step2: Most were collected at passage 12-16 although a few are at later passages.
Step3: Kinship Matrix
Step4: LD prune 1K... |
14,882 | <ASSISTANT_TASK:>
Python Code:
data = pd.read_csv('../benchMarkingResult.txt',
header=None,
sep='\t',
names=('iteration',
'basic_result',
'efficient_result'))
x = data.iteration
y1 = data.basic_result
y2 = data... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Plot the Basic and Efficient data first
Step2: do a linear regression for the 2 lines and evaluate the r-squared value
Step3: both r-squared l... |
14,883 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (6.0, 6.0)
plt.rcParams['savefig.dpi'] = 100
from straightline_utils import *
(x,y,sigmay) = generate_data()
plo... | <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 Data
Step2: Characterizing the posterior PDF
Step3: And now to draw some samples
Step4: Looks reasonable
Step5: It looks like we made a ... |
14,884 | <ASSISTANT_TASK:>
Python Code:
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode,iplot
init_notebook_mode(connected=True)
import pandas as pd
df = pd.read_csv('./2014_World_Power_Consumption')
df.head()
data = {'type':'choropleth', 'locations':df['Country'],'locationmode':'country names',
... | <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 pandas and read the csv file
Step2: Referencing the lecture notes, create a Choropleth Plot of the Power Consumption for Countries using... |
14,885 | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.2,<2.3"
%matplotlib inline
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
b.add_constraint('semidetached', 'primary')
b['requiv@constraint@primary']
b['requiv... | <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: As always, let's do imports and initialize a logger and a new Bundle. See Building a System for more details.
Step2: Semi-Detached Systems
Ste... |
14,886 | <ASSISTANT_TASK:>
Python Code:
% matplotlib inline
import pandas as pd
import glob
import matplotlib.pyplot as plt
GRLM = "345_GRLM10.txt"; print GRLM
df_grlm = pd.read_csv(GRLM, skiprows=43, delim_whitespace=True, names="mission,cycle,date,hour,minute,lake_height,error,mean(decibels),IonoCorrection,TropCorrection".spl... | <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: GRLM Altimetry data from July 22 2008 to September 3, 2016
Step2: Interpolate the missing data points
Step3: Add time information to the dataf... |
14,887 | <ASSISTANT_TASK:>
Python Code:
a = 10
b = 20
c = "Hello"
print a, b, c
list_items = ["milk", "cereal", "banana", 22.5, [1,2,3]] ## A list can contain another list and items of different types
print list_items
print "3rd item in the list: ", list_items[2] # Zero based index starts from 0 so 3rd item will have index 2
... | <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: Lists
Step2: Sets
Step3: Dictionaries
Step4: Functions
Step5: Loading Data
Step6: Pandas
Step7: Filtering data
Step8: Titanic data
Step9... |
14,888 | <ASSISTANT_TASK:>
Python Code:
# initialize your CORDEX submission form template
from dkrz_forms import form_handler
from dkrz_forms import checks
my_email = "..." # example: sf.email = "Mr.Mitty@yahoo.com"
my_first_name = "..." # example: sf.first_name = "Harold"
my_last_name = "..." # example: sf.last_name ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: please provide information on the contact person for this CORDEX data submission request
Step2: Type of submission
Step3: Requested general in... |
14,889 | <ASSISTANT_TASK:>
Python Code:
import numpy as np # importing this way allows us to refer to numpy as np
mylist = [1., 2., 3., 4.]
mynparray = np.array(mylist)
mynparray
one_vector = np.ones(4)
print one_vector # using print removes the array() portion
one2Darray = np.ones((2, 4)) # an 2D array with 2 "rows" and 4 "c... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Creating Numpy Arrays
Step2: You can initialize an array (of any dimension) of all ones or all zeroes with the ones() and zeros() functions
Ste... |
14,890 | <ASSISTANT_TASK:>
Python Code:
cadena_caracteres = "Hola mundo"
print dir(cadena_caracteres)
print 'Hola mundo'
print 'Pero el print también imprime un Enter al terminar la línea'
print 'Pero al imprimir con la coma al final',
print 'cambia el enter por un espacio'
print 'También puedo escribir lo mismo' ' en dos par... | <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: String formating
Step2: Pero si no queremos imprimir ese último Enter lo que tenemos que hacer es poner una coma al final de la línea
Step3: ¿... |
14,891 | <ASSISTANT_TASK:>
Python Code:
data_original = np.loadtxt('stanford_dl_ex/ex1/housing.data')
data = np.insert(data_original, 0, 1, axis=1)
np.random.shuffle(data)
train_X = data[:400, :-1]
train_y = data[:400, -1]
test_X = data[400:, :-1]
test_y = data[400:, -1]
m, n = train_X.shape
def cost_function(theta, X, y):
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Create train & test sets.
Step2: Define the cost function and how to compute the gradient.<br>
Step3: Run a timed optimization and store the i... |
14,892 | <ASSISTANT_TASK:>
Python Code:
from pynq import Overlay
from pynq.drivers import Audio
Overlay('base.bit').download()
pAudio = Audio()
pAudio.record(3)
pAudio.save("Recording_1.pdm")
pAudio.load("/home/xilinx/pynq/drivers/tests/pynq_welcome.pdm")
pAudio.play()
import time
import numpy as np
start = time.time()
af_ui... | <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: Record and play
Step2: Load and play
Step3: Play in notebook
Step4: Step 2
Step5: Step 3
Step6: Plotting PCM data
Step7: Frequency spectru... |
14,893 | <ASSISTANT_TASK:>
Python Code:
import requests
response = requests.get("https://api.forecast.io/forecast/e554f37a8164ce189acd210d00a452e0/47.4079,9.4647")
weather_data = response.json()
weather_data.keys()
print(weather_data['timezone'])
print("Longitude:", weather_data['longitude'], "Latitude", weather_data['latitude... | <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 time zone of Trogen is correct! This is where I live.
Step2: The longitude is mentioned first, and then the latitude. Usually, it is the ot... |
14,894 | <ASSISTANT_TASK:>
Python Code:
import coiled
cluster = coiled.Cluster(n_workers=10)
from dask.distributed import Client
client = Client(cluster)
print('Dashboard:', client.dashboard_link)
<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: Let's point the distributed client to the Dask cluster on Coiled and output the link to the dashboard
|
14,895 | <ASSISTANT_TASK:>
Python Code:
#Create references to important directories we will use over and over
import os, sys
DATA_HOME_DIR = '/home/nathan/olin/spring2017/line-follower/line-follower/data'
#import modules
import numpy as np
from glob import glob
from PIL import Image
from tqdm import tqdm
from scipy.ndimage impo... | <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 paths to data directories
Step7: Helper Functions
Step8: Data
Step9: Test the shape of the arrays
Step10: Visualize the training data... |
14,896 | <ASSISTANT_TASK:>
Python Code:
import deltascope as ds
import deltascope.alignment as ut
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import normalize
from scipy.optimize import minimize
import os
import tqdm
import json
import datetime
# ---------------------------... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Import raw data
Step2: We'll generate a list of pairs of stypes and channels for ease of use.
Step3: We can now read in all datafiles specifie... |
14,897 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from load import mnist
X_train, X_test, y_train2, y_test2 = mnist(onehot=True)
y_train = np.argmax(y_train2, axis=1)
y_test = np.argmax(y_test2, axis=1)
X_train[1].reshape((28, 28)).round(2)[:, 4:9].tolist()
from pylab import imshow, show, cm
import matplotlib.pylab 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: 12 - Introduction to Deep Learning
Step2: Naive model
Step3: Lets try an other example
Step4: Logistic Regression
Step5: ```
Step6: initia... |
14,898 | <ASSISTANT_TASK:>
Python Code:
import astropy.coordinates as coord
import astropy.table as at
from astropy.time import Time
import astropy.units as u
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
import corner
import pymc3 as pm
import pymc3_ext as pmx
import exoplanet as xo
import arviz as az
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: Here we will again load some pre-generated data meant to represent well-sampled, precise radial velocity observations of a single luminous sourc... |
14,899 | <ASSISTANT_TASK:>
Python Code:
# !pip install ray[tune]
!pip install scikit-optimize==0.8.1
!pip install sklearn==0.18.2
import time
from typing import Dict, Optional, Any
import ray
import skopt
from ray import tune
from ray.tune.suggest import ConcurrencyLimiter
from ray.tune.suggest.skopt import SkOptSearch
ray.ini... | <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: Click below to see all the imports we need for this example.
Step2: Let's start by defining a simple evaluation function. Again, an explicit ma... |
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