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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:
Step1: <a id="ref0"></a>
Step2: Use the following function to calculate accuracy
Step3: <a id="ref1"></a>
Step4: Define a function to train the mode... | <ASSISTANT_TASK:>
Python Code:
!conda install -y torchvision
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
def plot_decision_regions_2class(model,data_set):
cmap_light = ListedColormap(['#FFAAAA',... |
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Description:
Step1: original output
Step2: Recreate validation set and sample
Step3: Examine the driver_imgs_list.csv
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Python Code:
#Create directories
%cd $DATA_HOME_DIR
# did this once
%mkdir valid
%mkdir results
%mkdir -p sample/train
%mkdir -p sample/test
%mkdir -p sample/valid
%mkdir -p sample/results
%mkdir -p test/unknown
# Create subdirectories
for c in categories:
%mkdir -p valid/{c}
%mkdir -p sample/... |
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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:
Step1: Plot histogram of average ratings by revision
Step2: Selected harassing and aggressive revisions by quartile
Step3: Inter-Annotator Agreement
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Python Code:
%matplotlib inline
from __future__ import division
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.set_option('display.max_colwidth', 1000)
# Download data from google drive (Respect Eng / Wiki Collab): wikipdia data/v0_annotated
dat = pd.read_csv('../data/experi... |
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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:
Step1: 1. Model-based parametric regression
Step2: 2.2. Summary
Step3: Fit a Bayesian linear regression model assuming ${\bf z}={\bf x}$ and
Step4: ... | <ASSISTANT_TASK:>
Python Code:
# Import some libraries that will be necessary for working with data and displaying plots
# To visualize plots in the notebook
%matplotlib inline
from IPython import display
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.io # To read matlab files
... |
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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:
Step1: Tensorflow Lattice์ ํ์ ์ ์ฝ ์กฐ๊ฑด
Step2: ํ์ ํจํค์ง ๊ฐ์ ธ์ค๊ธฐ
Step3: ์ด ๊ฐ์ด๋์์ ์ฌ์ฉ๋๋ ๊ธฐ๋ณธ๊ฐ
Step4: ๋ ์คํ ๋ ์์ ์ง์ ์ ์ํ ํ๋ จ ๋ฐ์ดํฐ์ธํธ
Step6: ์ด CTR ํจ์์ ๋ฑ๊ณ ์ ๋๋ฅผ ์ดํด๋ณด๊ฒ ์ต๋๋ค.
Step7... | <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... |
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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:
Step1: 10.1. Spatiocyte Lattice-based Method
Step2: There is a distinct difference in the second argument for spatiocyte.World. This is called voxel r... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from ecell4.prelude import *
with species_attributes():
A | B | C | {'D': 1}
with reaction_rules():
A + B == C | (0.01, 0.3)
m = get_model()
w = spatiocyte.World(ones(), 0.005) # The second argument is 'voxel_radius'.
w.bind_to(m)
w.add_molecules(Species('C'),... |
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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:
Step1: Load in house sales data
Step2: Create new features
Step3: Squaring bedrooms will increase the separation between not many bedrooms (e.g. 1) a... | <ASSISTANT_TASK:>
Python Code:
import graphlab
graphlab.product_key.set_product_key("C0C2-04B4-D94B-70F6-8771-86F9-C6E1-E122")
sales = graphlab.SFrame('kc_house_data.gl/kc_house_data.gl')
from math import log, sqrt
sales['sqft_living_sqrt'] = sales['sqft_living'].apply(sqrt)
sales['sqft_lot_sqrt'] = sales['sqft_lot']... |
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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:
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Python Code:
import numpy as np
import pandas as pd
import torch
A, B = load_data()
cnt_not_equal = int((A[int(len(A) / 2):] != B[int(len(A) / 2):]).sum())
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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:
Step1: The global collection of tide gauge records at the PSMSL is used to access the data. The other way to access the data is to ask the service desk... | <ASSISTANT_TASK:>
Python Code:
# this is a list of packages that are used in this notebook
# these come with python
import io
import zipfile
import functools
# you can install these packages using pip or anaconda
# (requests numpy pandas bokeh pyproj statsmodels)
# for downloading
import requests
# computation librarie... |
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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:
Step1: The code above also defines a stardard conformal $\mathbb{R}^{N+1,1}$ layout without this new basis vector. This is primarily to support renderi... | <ASSISTANT_TASK:>
Python Code:
from clifford import ConformalLayout, BasisVectorIds, MultiVector, transformations
class OurCustomLayout(ConformalLayout):
def __init__(self, ndims):
self.ndims = ndims
euclidean_vectors = [str(i + 1) for i in range(ndims)]
conformal_vectors = ['m2', '... |
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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:
Step1: Load the database webserver.
Step2: Import the web interface and initialize it.
Step3: The instructions below assume that these three steps ha... | <ASSISTANT_TASK:>
Python Code:
# you must specify the length of the time series when loading the database
ts_length = 100
# when running from the terminal
# python go_server_persistent.py --ts_length 100 --db_name 'demo'
# here we load the server as a subprocess for demonstration purposes
server = subprocess.Popen(['py... |
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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:
Step2: 1.4.2 Code Management with Git
Step4: 1.5.6 SDSS DR7 Quasar Catalog
Step5: Access BOSS spectra and metadata
Step6: Read the DR12 quasar catal... | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import astroML
print astroML.__version__
SDSS Spectrum Example
---------------------
Figure 1.2.
An example of an SDSS spectrum (the specific flux plotted as a function of
wavelength) loaded from the SDSS SQL server in real time using Python tools
provided here (this spectr... |
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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:
Step1: Set up default parameters. We use 32 subjects, which is the median sample size of the set of fMRI studies published between 2011 and 2015 that ... | <ASSISTANT_TASK:>
Python Code:
import numpy
import nibabel
import os
import nilearn.plotting
import matplotlib.pyplot as plt
from statsmodels.regression.linear_model import OLS
import nipype.interfaces.fsl as fsl
import scipy.stats
if not 'FSLDIR' in os.environ.keys():
raise Exception('This notebook requires that F... |
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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:
Step1: ้ใฌใฆในๅ่ฆณๆธฌใซใใ STS ใขใใซใฎ่ฟไผผๆจ่ซ
Step2: ๅๆใใผใฟ
Step3: ใขใใซ
Step4: ใใฎใขใใซใฏใ่ฆณๆธฌใใใๆ็ณปๅใงๆผ็ฎใใไปฃใใใซใ่ฆณๆธฌใ็ฎก็ใใใใฏใฝใณๅใฌใผใใใฉใกใผใฟใฎ็ณปๅใงๆผ็ฎใใพใใ
Step5: ้ใฌใฆในๅ่ฆณๆธฌใขใใซใซ่ฟไผผๆจ่ซใไฝฟ็จใใใใ... | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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 l... |
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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:
Step1: Configure environment settings
Step2: Importing the dataset into BigQuery
Step3: Explore the Covertype dataset
Step4: Create training and val... | <ASSISTANT_TASK:>
Python Code:
import json
import os
import pickle
import tempfile
import time
import uuid
from typing import NamedTuple
import numpy as np
import pandas as pd
from google.cloud import bigquery
from googleapiclient import discovery, errors
from jinja2 import Template
from kfp.components import func_to_c... |
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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:
Step1: Let's load and examine the titanic data with pandas first.
Step2: So we have 891 training examples with 10 information columns given. Of course... | <ASSISTANT_TASK:>
Python Code:
#import all the needed package
import numpy as np
import scipy as sp
import re
import pandas as pd
import sklearn
from sklearn.cross_validation import train_test_split,cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
import matplotlib
from matp... |
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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:
Step1: Import data from Google Clod Storage
Step2: Prepare data for ARIMA
Step3: Let's create a column for weekly returns. Take the log to of the ret... | <ASSISTANT_TASK:>
Python Code:
!pip install --user statsmodels
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime
%config InlineBackend.figure_format = 'retina'
df = pd.read_csv('gs://cloud-training/ai4f/AAPL10Y.csv')
df['date'] = pd.to_datetime(df['date'])
df.sor... |
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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:
Step1: We are going to use a simple cnn network as our encoder and decoder. In decoder, we use SpatialFullConvolution (aka deconvolution or convolution... | <ASSISTANT_TASK:>
Python Code:
# a bit of setup
import numpy as np
from bigdl.nn.criterion import *
from bigdl.dataset import mnist
from zoo.pipeline.api.keras.layers import *
from zoo.pipeline.api.keras.models import Model
from zoo.pipeline.api.keras.utils import *
import datetime as dt
IMAGE_SIZE = 784
IMAGE_ROWS = 2... |
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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:
Step1: ๅฐฑๅฏไปฅไธ่ฝฝๅฐ๏ผ็ถๅๅฏไปฅๆพๅฐไธไธชๅซๅmeetingminitus.pdf็ๆไปถใ่ฟไธชๅฐฑๆฏๆไปฌๆฅไธๆฅ่ฆ็จ็
Step2: ๅฅฝใ็ฐๅจๆฅ่ฏ่ฏๆ่ชๅทฑ็จlatexๅ็pdfๆๆกฃใ
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Python Code:
wget https://nostarch.com/download/Automate_the_Boring_Stuff_onlinematerials_v.2.zip
import PyPDF2
with open('./automate_online-materials/meetingminutes.pdf', 'rb') as f:
pdfreader = PyPDF2.PdfFileReader(f)
print(pdfreader.numPages)
page0 = pdfreader.getPage(0)
page0... |
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Description:
Step3: Part 0
Step4: In this lab we will be examining subsets of the tuples we create (e.g., the top rated movies by users). Whenever we examine only ... | <ASSISTANT_TASK:>
Python Code:
import sys
import os
from test_helper import Test
baseDir = os.path.join('data')
inputPath = os.path.join('cs100', 'lab4', 'small')
ratingsFilename = os.path.join(baseDir, inputPath, 'ratings.dat.gz')
moviesFilename = os.path.join(baseDir, inputPath, 'movies.dat')
numPartitions = 2
rawRa... |
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Description:
Step1: We need to import here the data, modify them if needed and proceed
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Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from simmit import smartplus as sim
import os
dir = os.path.dirname(os.path.realpath('__file__'))
umat_name = 'MIPLN' #This is the 5 character code for the periodic homogenization for multlayered co... |
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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:
Step1: SBML
Step2: There are other dialects of SBML prior to FBC 2 which have previously been use to encode COBRA models. The primary ones is the "COB... | <ASSISTANT_TASK:>
Python Code:
import cobra.test
import os
from os.path import join
data_dir = cobra.test.data_directory
print("mini test files: ")
print(", ".join(i for i in os.listdir(data_dir)
if i.startswith("mini")))
textbook_model = cobra.test.create_test_model("textbook")
ecoli_model = cobra.test... |
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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:
Step1: Now let's pretend that we feel there are too many features, and many are probably useless. We only want the top 100 for making our model.
Step2:... | <ASSISTANT_TASK:>
Python Code:
npoints = 100
nfeatures = int(5e4)
rng = np.random.default_rng()
X = rng.random(size=(npoints, nfeatures));
Y = rng.random(size=(npoints,)) > 0.5
def select_best_features(X, Y, n=100):
corrs = np.zeros(X.shape[1])
for ii in range(X.shape[1]):
corrs[ii] = np.corrcoef(X[:, ... |
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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:
Step1: Transforming an input to a known output
Step2: relation between input and output is linear
Step3: Defining the model to train
Step5: Defining... | <ASSISTANT_TASK:>
Python Code:
!pip install -q tf-nightly-gpu-2.0-preview
import tensorflow as tf
print(tf.__version__)
# a small sanity check, does tf seem to work ok?
hello = tf.constant('Hello TF!')
print("This works: {}".format(hello))
# this should return True even on Colab
tf.test.is_gpu_available()
tf.test.is_bu... |
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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:
Step1: If you look above you can see that we are accessing a csv file that has the locations of the podcasts, taking the html data and parsing it, to l... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import urllib2
import re
import wget
import os
os.chdir('/Users/sheldon/git/springboard_capstone/acquire_podcasts')
df = pd.read_csv('top100_pcasts_locations.csv')
urls = df.url.tolist()
urls = filter(lambda string: 'feeds.' in string or 'feed.' in s... |
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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:
Step1: Note that a similar transformation can be applied with compute_ems
| <ASSISTANT_TASK:>
Python Code:
# Author: Denis Engemann <denis.engemann@gmail.com>
# Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io, EvokedArray
from mne.datasets import sample
from mne.decoding import EMS, co... |
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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:
Step1: Load the Data
Step2: Create the Model
Step3: Training the Model
Step4: Store the Model
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Python Code:
# ใฐใฉใใๆ็ซ ไธญใซ่กจ็คบใใใใใใซใใใใพใใชใ
%matplotlib inline
# autoreload module
%load_ext autoreload
%autoreload 2
# load local package
import sys
import os
current_path = os.getcwd()
sys.path.append(os.path.join(current_path, "../../")) # load project root
def read_data(path, ignore_columns):
imp... |
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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:
Step1: 1 Introduction
Step2: Ou importer le module matplotlib.pyplot avec l'identifiant plt. Plus correct pour รฉviter de charger tous les objets
Step3... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from pylab import *
# import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from pylab import *
x = np.linspace(0, 5, 10)
y = x ** 2
figure()
plot(x, y, 'r')
xlabel('x')
ylabel('y')
title('titre')
show()
subplot(1,2,1)
plot(x, y, 'r--')
subplot(1,2,... |
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Description:
Step1: Define an objective function
Step2: Let's begin by plotting $f$.
Step3: Setting a Gaussian Process prior
Step4: The following helper function... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import torch
import torch.autograd as autograd
import torch.optim as optim
from torch.distributions import constraints, transform_to
import pyro
import pyro.contrib.gp as gp
assert pyro.__version__.startswith('1.7.0')
... |
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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:
Step1: Ideal Responses
Step2: Modeling the Offset Transmission Line
Step3: The broadcasting feature in numpy is used here. The quantities
Step4: At ... | <ASSISTANT_TASK:>
Python Code:
import skrf
from skrf.media import DefinedGammaZ0
import numpy as np
freq = skrf.Frequency(1, 9000, 1001, "MHz")
ideal_medium = DefinedGammaZ0(frequency=freq, z0=50)
ideal_open = ideal_medium.open()
ideal_short = ideal_medium.short()
ideal_load = ideal_medium.match()
ideal_thru = idea... |
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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:
Step1: Data
Step2: Exercise 1
Step3: Since we find a pvalue for the Levene test of less than our $\alpha$ level (0.05), we can reject the null hypot... | <ASSISTANT_TASK:>
Python Code:
# Useful functions
def normal_test(X):
z, pval = stats.normaltest(X)
if pval < 0.05:
print 'Values are not normally distributed.'
else:
print 'Values are normally distributed.'
return
# Useful Libraries
import numpy as np
import matplotlib.pyplot as plt
fr... |
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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:
Step1: Overview <a class="anchor" id="Overview"></a>
Step2: However under the enumeration interpretation, the same sample site will return a fully enu... | <ASSISTANT_TASK:>
Python Code:
import os
import torch
import pyro
import pyro.distributions as dist
from torch.distributions import constraints
from pyro import poutine
from pyro.infer import SVI, Trace_ELBO, TraceEnum_ELBO, config_enumerate, infer_discrete
from pyro.infer.autoguide import AutoNormal
from pyro.ops.inde... |
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Description:
Step1: Create dataframe
Step2: Make plot
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Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
'pre_score': [4, 24, 31, 2, 3],
'mid_score': [25, 94, 57, 62, 70],
'post_score': [5, 43, 23, 23, 51]}
df ... |
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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:
Step1: Train the model
Step2: Some crude attempts at sentiment analysis
Step3: Proof of principal - ish
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Python Code:
import Load_Text_Set as l_data
import run_Word2Vec as w2v
words = l_data.text_8(200000)
embeddings = w2v.run_embeddings()
import numpy as np
import regex as re
joy_words = ['happy','joy','pleasure','glee']
sad_words = ['sad','unhappy','gloomy']
scary_words = ['scary','frightening','terr... |
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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:
Step1: Comparing the Errors
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Python Code:
def compute_value_dct(theta_lst, features):
return [{s: np.dot(theta, x) for s, x in features.items()} for theta in theta_lst]
def compute_values(theta_lst, X):
return [np.dot(X, theta) for theta in theta_lst]
def compute_errors(value_lst, error_func):
return [error_func(v) fo... |
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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:
Step1: Let's look at the tables and columns we have for analysis.
Step2: Price History
Step3: Compare google to S&P
Step4: Learning objective 2
Step... | <ASSISTANT_TASK:>
Python Code:
PROJECT = 'qwiklabs-gcp-ml-489e10787faf' # Change to your project.
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from google.cloud import bigquery
from IPython.core.magic import register_cell_magic
from IPython import get_ipython
bq = b... |
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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:
Step1: 1. Gnerate x = a sequence of points, y = sin(x)+a where a is a small random error.
Step2: 2. Draw a scatter plot of x and y.
Step3: 3. Use lin... | <ASSISTANT_TASK:>
Python Code:
%%javascript
$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')
# import the necessary package at the very beginning
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import sklearn
## Type Your Answer Below... |
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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:
Step1: As always, let's do imports and initialize a logger and a new bundle. See Building a System for more details.
Step2: Adding Spots
Step3: Spot... | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.0,<2.1"
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_star()
b.add_spot(radius=30, colat=80, long=0, relteff=0.9)
print b['spot']
times = np.linspace(0, 10, 11)
b.set... |
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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:
Step1: Now import the version in bruges for comparison
Step2: Plot the two together
Step3: We can use the inspect module ot compare the source code
S... | <ASSISTANT_TASK:>
Python Code:
def r(l,d,f):import numpy as n;t=n.arange(-l/2,l/2,d);k=(n.pi*f*t)**2;return t,(1-2*k)/n.exp(k)
from bruges.filters import ricker
import matplotlib.pyplot as plt
t, w = r(0.128, 0.004, 25)
plt.figure(figsize=(10,3))
plt.plot(t, ricker(0.128, 0.004, 25), 'o') # Compare to bruges.
plt.pl... |
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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:
Step1: Multiplying Numpy Arrays
Step2: LAB CHALLENGE
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Python Code:
import numpy as np
one_dimensional = np.array([1,1,1,2,3,3,3,3,3])
one_dimensional
one_dimensional.shape # not yet rows & columns
one_dimensional.reshape((9,-1)) # let numpy figure out how many columns
one_dimensional # still the same
one_dimensional.ndim
two_dimensional = one_dimension... |
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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:
Step1: If you did not already have the training set, download and unpack the tarball.
Step2: Problem 1b
Step3: Problem 1c
Step4: Problem 1d
Step5: ... | <ASSISTANT_TASK:>
Python Code:
def lc_plot(t, m, m_unc, period=0.0):
if period == 0.0:
fig, ax = plt.subplots()
ax.errorbar(t, m, m_unc,
fmt='o', color='MediumAquaMarine',
mec="0.2",mew=0.5)
ax.set_xlabel('HJD (d)')
ax.set_ylabel(r'$V_\mathrm{... |
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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:
Step1: Download & Process Security Dataset
Step2: Analytic I
Step3: Analytic II
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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_powerview_ldap_ntsecuritydescriptor.zip"
registerMordorSQLTable(spark, sd_file, "sdTable")
df = spark.sql... |
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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:
Step1: Input data
Step2: Create a plot function for a visual comparison of the time series
Step3: Plot an example series - in this case the temperatu... | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
import copy
import os
import pandas as pd
import matplotlib.pyplot as plt
import tsam.timeseriesaggregation as tsam
import tsam.hyperparametertuning as tune
import tqdm
%matplotlib inline
raw = pd.read_csv('testdata.csv', index_col = 0)
raw=raw.rename(c... |
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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:
Step1: Verify that models.OneParameterLogisticModel can recover parameters. We would only expect this to be possible when USING_2PL = False.
Step2: Ve... | <ASSISTANT_TASK:>
Python Code:
num_students = 2000
num_assessments = 3000
num_ixns_per_student = 1000
USING_2PL = False # False => using 1PL
proficiencies = np.random.normal(0, 1, num_students)
difficulties = np.random.normal(0, 1, num_assessments)
if USING_2PL:
discriminabilities = np.random.normal(0, 1, num_asses... |
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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:
Step1: Each variable holds data, a gradient, and information about the function that created it.
Step2: Compare the above computations to the below gr... | <ASSISTANT_TASK:>
Python Code:
x = torch.Tensor(5, 3)
print(x)
x.zero_()
torch.Tensor([[1, 2, 3], # rank 2 tensor
[4, 5, 6],
[7, 8, 9]])
x.size()
x = torch.rand(5, 3)
print(x)
npy = np.random.rand(5, 3)
y = torch.from_numpy(npy)
print(y)
z = x + y #can we do this addition?
x.type(), y.type... |
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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:
Step1: Parameterization within SQL queries
Step2: This defined a SQL query with a string parameter named endpoint, which can be filled when executing ... | <ASSISTANT_TASK:>
Python Code:
%%bq query -n logs_query
SELECT * FROM `cloud-datalab-samples.httplogs.logs_20140615`
%bq sample -q logs_query --count 10
%%bq query
SELECT endpoint FROM `cloud-datalab-samples.httplogs.logs_20140615` GROUP BY endpoint
%%bq query -n endpoint_stats
SELECT *
FROM `cloud-datalab-samples.htt... |
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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:
Step1: Estimating the angle of rotation
Step2: The function can be applied as follows.
Step3: Estimating the angle of rotation and the translation
St... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import sys,os
ia898path = os.path.abspath('../../')
if ia898path not in sys.path:
sys.path.append(ia898path)
import ia898.src as ia
%matplotlib inline
import matplotlib.image as mpimg
#f = ia.normalize(ia.gaussian((151,151), [[75],[75]], [[800,0],[0,800]]), [0,200])... |
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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:
Step1: Now we plot the two classes as a scatter plot!
Step2: Now we want to classify this synthetic data using the perceptron model which will be trai... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
# now we genrate the data
N = 30
x = np.zeros(N, dtype=np.float64)
y = np.zeros(N, dtype=np.float64)
for k in range(N):
x[k], y[k] = [np.random.uniform(-1,1) for i in range(2)]
a = np.random.... |
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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:
Step2: Lorenz system
Step4: Write a function solve_lorenz that solves the Lorenz system above for a particular initial condition $[x(0),y(0),z(0)]$. Y... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
def lorentz_derivs(yvec, t, sigma, rho, beta):
Compute the the derivatives for the Lorentz system at yvec(t).
x,y,z = yvec[0],... |
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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:
Step1: Client-efficient large-model federated learning via federated_select and sparse aggregation
Step2: Each client will federated_select the rows o... | <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... |
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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:
Step1: Constrained problem
Step2: Modeling and joint acquisition function
Step3: Initial belief
Step4: Running Bayesian Optimizer
Step5: Results
St... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import gpflow
import gpflowopt
import numpy as np
# Objective & constraint
def townsend(X):
return -(np.cos((X[:,0]-0.1)*X[:,1])**2 + X[:,0] * np.sin(3*X[:,0]+X[:,1]))[:,None]
def constraint(X):
return -(-np.cos(1.5*X[:,0]+np.pi)... |
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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:
Step1: Curve data
Step2: You can also access curves by index (remembering that the first index is zero but the first curve is the depth).
Step3: All ... | <ASSISTANT_TASK:>
Python Code:
import os
import lasio
l = lasio.read(os.path.join("..", "tests", "examples", "6038187_v1.2.las"))
print(type(l))
print(l._text)
l.keys()
l['NEUT']
print(l['GAMN'])
print(l[2])
print(l["COND"])
print(l[-1])
print(l.data.shape)
print(l.data)
from lasio.las import HeaderItem
example = H... |
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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:
Step1: Python Basics (2016-09-09)
Step2: Data Types
Step3: What will be the data type of a+b?
Step4: It's a float. But why? If it was an integer, in... | <ASSISTANT_TASK:>
Python Code:
# this is a single line comment
this is a
multi line
comment
a = 5.1
print 'a', type(a)
b = 3
print 'b', type(b)
print a+b, type(a+b)
print '3/2', 3/2 # integer divided by integer
print '3./2', 3./2 # float divided by integer
c = 'g'
print 'c', type(c)
d = 'stuff'
print 'd', type(d... |
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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:
Step3: Jupyter notebooks
Step4: Some common terminology
Step5: Vandermonde matrices
Step6: This type of matrix is very common; we can also create it... | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import numpy
from matplotlib import pyplot
def matmult1(A, x):
Entries of y are dot products of rows of A with x
y = numpy.zeros_like(A[:,0])
for i in range(len(A)):
row = A[i,:]
for j in range(len(row)):
y[i] += row[j] * x[j]
... |
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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:
Step6: The pipeline uses a mix of custom and pre-build components.
Step7: The custom components execute in a container image defined in base_image/Doc... | <ASSISTANT_TASK:>
Python Code:
!grep 'BASE_IMAGE =' -A 5 pipeline/covertype_training_pipeline.py
%%writefile ./pipeline/covertype_training_pipeline.py
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You... |
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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:
Step1: Exercise 1
Step2: Exercise 2
Step3: Exercise 3
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Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%pylab inline
#Your code here...
file1 = pd.read_json('../../data/data1.json')
file2 = pd.read_json('../../data/data2.json')
file2 = pd.read_json('../../data/data2.json')
file3 = pd.read_json('... |
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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:
Step1: Prepare non-python dependencies by calling "nbinstall"
Step2: Set default options by calling "set_defaults" (optional)
Step3: Demo 1 - Renderi... | <ASSISTANT_TASK:>
Python Code:
import qgrid
qgrid.nbinstall(overwrite=True) # copiesย javascript dependencies to your /nbextensions folder
qgrid.set_defaults(remote_js=True, precision=4)
import pandas as pd
import numpy as np
randn = np.random.randn
pd.set_option('display.max_rows', 8)
from pandas.io.data import ge... |
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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:
Step1: Jax Model Conversion For TFLite
Step3: Data Preparation
Step4: Build the MNIST model with Jax
Step5: Train & Evaluate the model
Step6: Conve... | <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... |
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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:
Step1: Download and prepare the dataset
Step2: Limit the size of the dataset to experiment faster (optional)
Step3: Create a tf.data dataset
Step4: ... | <ASSISTANT_TASK:>
Python Code:
from __future__ import absolute_import, division, print_function
# Import TensorFlow >= 1.10 and enable eager execution
import tensorflow as tf
tf.enable_eager_execution()
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import unicodedata
import re
imp... |
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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:
Step1: This is just a simple sample to show how Bollinger Bands work.
Step2: What is a 'Bollinger Bandยฎ'
Step4: In this example of Bollinger Bandsยฎ, ... | <ASSISTANT_TASK:>
Python Code:
import quantiacsToolbox
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
F_AD = pd.read_csv('./tickerData/F_AD.txt')
CLOSE = np.array(F_AD.loc[:252-1, [' CLOSE']])
plt.plot(CLOSE)
def bollingerBands(a, n=20):
sma = np.nansum(a[-n:]) / n
s... |
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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:
Step1: Creation of Dictionary
Step3: Recalibration of Dictionary
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Python Code:
file_path = '../data/2011.0.00419.S/sg_ouss_id/group_ouss_id/member_ouss_2013-03-06_id/product/IRAS16547-4247_Jet_CH3OH7-6.clean.fits'
noise_pixel = (15, 4)
train_pixels = [(133, 135),(134, 135),(133, 136),(134, 136)]
img = fits.open(file_path)
meta = img[0].data
hdr = img[0].header
# V a... |
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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:
Step1: Delta hedging
Step2: Construct efficient frontier based on return in infinitesimal future time $\mathrm{d}t$
Step3: The most risk averse por... | <ASSISTANT_TASK:>
Python Code:
from scipy.stats import norm
import numpy as np
def bs_put(df, fwd, t, vol, k):
log_fwd_moneyness = np.log(fwd/k)
d1 = (log_fwd_moneyness + vol**2 /2.0 * t) / vol / np.sqrt(t)
d2 = d1 - vol * np.sqrt(t)
return df * (-norm.cdf(-d1) * fwd + norm.cdf(-d2) * k), norm.cdf(-d1)... |
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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:
Step1: quickly add lots of similar procedures
Step2: Error handling
Step3: Advance usage
| <ASSISTANT_TASK:>
Python Code:
import os
import sys
import ruamel.yaml as yaml
from ddf_utils.chef.api import Chef
# at the beginning, initial the chef
chef = Chef()
# adding configs and metadata(to the info section)
(chef.add_config(ddf_dir=os.path.expanduser('~/src/work/Gapminder/datasets'))
.add_metadata(id='te... |
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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:
Step1: Make a grid and set boundary conditions.
Step2: Here we make the initial grid of elevation of zeros with a very small amount of noise to make a... | <ASSISTANT_TASK:>
Python Code:
# Code block 1
import copy
import numpy as np
from matplotlib import pyplot as plt
from landlab import RasterModelGrid, imshow_grid
from landlab.components import (
ChannelProfiler,
ChiFinder,
FlowAccumulator,
SteepnessFinder,
StreamPowerEroder,
)
from landlab.io impor... |
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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:
Step1: Working with ndarray
Step2: Examining ndrray
Step3: Why to use numpy?
Step4: Basic Operation
Step5: Most Common Functions
Step6: Multi Dime... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.arange(10)
np.arange(1,10)
np.arange(1,10, 0.5)
np.arange(1,10, 3)
np.arange(1,10, 2, dtype=np.float64)
ds = np.arange(1,10,2)
ds.ndim
ds.shape
ds.size
ds.dtype
ds.itemsize
x=ds.data
list(x)
ds
# Memory Usage
ds.size * ds.itemsize
%%capture timeit_results
# Regula... |
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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:
Step1: ๊ทธ๋๋์ธํธ ๋ฐ ์๋ ๋ฏธ๋ถ ์๊ฐ
Step2: ๊ทธ๋๋์ธํธ ๊ณ์ฐํ๊ธฐ
Step3: ์ผ๋ถ ์ฐ์ฐ์ ๊ธฐ๋กํ ํ์๋ GradientTape.gradient(target, sources)๋ฅผ ์ฌ์ฉํ์ฌ ์ผ๋ถ ์์ค(์ข
์ข
๋ชจ๋ธ ๋ณ์)์ ์๋์ ์ธ ์ผ๋ถ ๋์(์ข
์ข
์์ค)์ ๊ทธ๋๋์ธ... | <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... |
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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:
Step1: Now we must generate a dictionary which maps vocabulary into a number
| <ASSISTANT_TASK:>
Python Code:
from itertools import chain
from collections import OrderedDict
reviews_merged = OrderedDict()
# Flatten the reviews, so each review is just a single list of words.
n_reviews = -1
for bus_id in set(review.business_id.values[:n_reviews]):
# This horrible line first collapses each revie... |
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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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<USER_TASK:>
Description:
Step1: Step 1
Step3: Include an exploratory visualization of the dataset
Step7: Histogram of the data shows that the trainign data is unevenly distri... | <ASSISTANT_TASK:>
Python Code:
# Import all the relevant modules.
import cv2
import csv
import matplotlib.image as mpimg
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pickle
from random import randint
import seaborn as sns
from sklearn.utils import shuffle
... |
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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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<USER_TASK:>
Description:
Step1: If you want the confidence intervals
| <ASSISTANT_TASK:>
Python Code:
from lmfit.models import GaussianModel
# initialize the gaussian model
gm = GaussianModel()
# take a look at the parameter names
print gm.param_names
# I get RuntimeError since my numpy version is a little old
# guess parameters
par_guess = gm.guess(n,x=xpos)
# fit data
result = gm.fit(n,... |
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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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<USER_TASK:>
Description:
Step1: Building the policy network
Step2: Loss function and updates
Step5: Computing cumulative rewards
Step7: Playing the game
Step9: Results & vi... | <ASSISTANT_TASK:>
Python Code:
# This code creates a virtual display to draw game images on.
# If you are running locally, just ignore it
import os
if type(os.environ.get("DISPLAY")) is not str or len(os.environ.get("DISPLAY")) == 0:
!bash ../xvfb start
os.environ['DISPLAY'] = ':1'
import gym
import numpy as n... |
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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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<USER_TASK:>
Description:
Step1: A univariate example
Step2: We create a bimodal distribution
Step3: The simplest non-parametric technique for density estimation is the histog... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.distributions.mixture_rvs import mixture_rvs
np.random.seed(12345) # Seed the random number generator for reproducible results
# Location, scale an... |
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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:
Step1: ... or call all the methods individually
| <ASSISTANT_TASK:>
Python Code:
# NOTE: This will take several minutes depending on the performance of your machine
audio_features = audioAnalyzer.analyze(audio_filename)
# plot the features
plt.rcParams['figure.figsize'] = [20, 8]
audioAnalyzer.plot(audio_features)
plt.show()
# audio metadata extraction
metadata = aud... |
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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:
Step1: Load Data
Step2: Create Classifier Pipeline
Step3: Cross Validation
Step4: Evaluate Model
Step5: To get an good measure of the model's accur... | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_iris
from sklearn.pipeline import make_pipeline
from sklearn import preprocessing
from sklearn import cross_validation
from sklearn import svm
# Load the iris test data
iris = load_iris()
# View the iris data features for the first three rows
iris.data[0... |
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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:
Step2: Make a function for importing the station info for each line
Step3: Import the data for each line
Step4: Turn list of dict's into dic with lin... | <ASSISTANT_TASK:>
Python Code:
import http.client, urllib.request, urllib.parse, urllib.error, base64
import urllib
import json
import pickle
def metro_line_import(line):
Import station data from WMATA API and format it as a dictionary with staion name as the key and the
[latitude, longitude] as the values
... |
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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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<USER_TASK:>
Description:
Step2: Problem 1) An (oversimplified) 1-D Model
Step3: Problem 1b
Step4: Problem 1c
Step5: Problem 2) Add Noise
Step6: Problem 2b
Step7: Problem 3... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
def phi(x, mu, fwhm):
Evalute the 1d PSF N(mu, sigma^2) along x
sigma = fwhm/2.3548
flux = 1/np.sqrt(2*np.pi*sigma**2)*np.exp(-(x - mu)**2/(2*sigma**2))
return flux
x = np.linspace(0,20,21)
plt.plot(... |
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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:
Step1: We are using TensorFlow-GPU 0.12.1 on Python 3.5.2, running on Windows 10 with Cuda 8.0.
Step2: Definitions
Step3: Data load
Step4: Model def... | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
# We don't really need to import TensorFlow here since it's handled by Keras,
# but we do it in order to output the version we are using.
tf.__version__
from IPython.display import Image
from util import Util
u = Util()
import numpy as np
# Explicit random seed ... |
<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: ไปฅไธใๆฐๅค่จ็ฎใ่กใใใญใฐใฉใ ใงใใ๏ผ
Step2: 3. ๅฏ่ฆๅ
Step3: animfuncใๅฎ่กใใใใจใง๏ผpendulum.mp4ใฎใใใชใขใใกใผใทใงใณๅ็ปใไฟๅญใใใ๏ผ
Step4: <video width="432" height="288" controls>
| <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy.integrate import odeint
from math import sin
''' constants '''
m = 1 # mass of the pendulum [kg]
l = 1 # length of the pendulum [m]
g = 10 # Gravitational acceleration [m/s^2]
''' time setting '''
t_end = 10 # simulation time [s]
t_fps = 50 # frame per second... |
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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:
Step1: Parameters are given as follows. D and radius mean a diffusion constant and a radius of molecules, respectively. Dimensions of length and time a... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from ecell4.prelude import *
D = 1 # 0.01
radius = 0.005
N = 20 # a number of samples
y0 = {} # {'A': 60}
duration = 3
V = 8
with species_attributes():
A | {'radius': radius, 'D': D}
with reaction_rules():
~A > A | 45.0
A > ~A | 1.5
m = get_model()
ret1... |
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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:
Step1: Pregunta 2
Step2: Pregunta 4
| <ASSISTANT_TASK:>
Python Code:
##escriba la funciรณn aqui##
horaValida('13:00:00')
rut = input("ingrese su rut: ")
##su cรณdigo va aqui##
import random
random.seed(int(rut))
##su cรณdigo va aqui##
<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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Description:
Step3: STOCK PICKING 3.1 - FORECASTING ARIMAs
Step4: ARIMA
Step5: Massive Predictions
Step6: Reloading full results
Step7: Stock picking
Step8: No... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_datareader.data as web
import datetime
def get_data(symbols,
add_ref=True,
data_source='yahoo',
price='Adj Close',
start='1/21/2010',
en... |
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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:
Step1: Exercise 1
Step2: Exercise 2
Step3: Exercise 3
Step4: b. Confidence Intervals.
Step5: Exercise 4
| <ASSISTANT_TASK:>
Python Code:
# Useful Functions
class DiscreteRandomVariable:
def __init__(self, a=0, b=1):
self.variableType = ""
self.low = a
self.high = b
return
def draw(self, numberOfSamples):
samples = np.random.randint(self.low, self.high, numberOfSamples)
... |
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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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<USER_TASK:>
Description:
Step1: Request Data
Step3: Now that we have a function, we can run a query like this
Step4: Pandas Dataframes
Step5: Additional operations
Step6: O... | <ASSISTANT_TASK:>
Python Code:
import datetime
import pandas as pd
import numpy as np
import linecache, bisect
import os
spath = os.getcwd()
path = spath.split("/")
date = path[len(path)-1]
def makeGraphqlRequest(query, variables):
return GraphQLClient.request(query, variables)
suspicious_query = query($date:Spot... |
<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: As always, let's do imports and initialize a logger and a new Bundle. See Building a System for more details.
Step2: Dataset Parameters
Step3:... | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.1,<2.2"
%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_dataset('orb')
print b.filter(kind='orb')
print b['times']
print b['compute']
... |
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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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<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bnu', 'sandbox-1', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "ema... |
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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:
Step1: As with previous notebooks we will use an action selection function to give us an exploration strategy. Feel free to change this function and pl... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tempfile
import base64
import pprint
import random
import json
import sys
import gym
import io
from gym import wrappers
from collections import deque
from subprocess import check_output
from IPython.display impo... |
<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: Initial set-up
Step2: Combine model and experiments to produce
Step3: Set up prior ranges for each parameter in the model.
Step4: Run ABC cal... | <ASSISTANT_TASK:>
Python Code:
import os, tempfile
import logging
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from ionchannelABC import theoretical_population_size
from ionchannelABC import IonChannelDistance, EfficientMultivariateNormalTransition, IonChannelAccepto... |
<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 a survey plan
Step3: Transient model
Step4: Transient Generator
Step5: Number of injections, you can fix the number of generated trans... | <ASSISTANT_TASK:>
Python Code:
import os
home_dir = os.getcwd()
# Please enter the path to where you have placed the Schlegel, Finkbeiner & Davis (1998) dust map files
# You can also set the environment variable SFD_DIR to this path (in that case the variable below should be None)
sfd98_dir = os.path.join(home_dir, 'da... |
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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:
Step2: ALS implementation using DML
Step3: Running the Algorithm
Step5: Predictions
Step6: Just for Fun!
| <ASSISTANT_TASK:>
Python Code:
from pyspark.sql import SparkSession
from pyspark.sql.types import *
from systemml import MLContext, dml
spark = SparkSession\
.builder\
.appName("als-example")\
.getOrCreate()
schema = StructType([StructField("movieId", IntegerType(), True),
S... |
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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:
Step1: Paths
Step2: Config parameters
Step3: Data Loading
Step4: experimenting with Fastai
Step5: Misc / Asides / Notes
| <ASSISTANT_TASK:>
Python Code:
import pathlib
import os
import torchtext
# from torchtext.data import Field
from torchtext import data
# import spacy
import pandas as pd
import numpy as np
# from torchtext.data import TabularDataset
data_path = pathlib.Path('../../data')
comp_path = pathlib.Path(data_path/'competition... |
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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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<USER_TASK:>
Description:
Step1: Load the MNIST database
Step2: Each digit is represented by a one hot vector where the index of the 1 represents the number
Step3: Load the sa... | <ASSISTANT_TASK:>
Python Code:
import nengo
import numpy as np
import cPickle
from nengo_extras.data import load_mnist
from nengo_extras.vision import Gabor, Mask
from matplotlib import pylab
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from scipy import linalg
# --- load the data
img_rows,... |
<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: Processing the text using TRIPS
Step2: We can pass the block of text defined above to the TRIPS processor
Step3: Here tp is a TripsProcessor o... | <ASSISTANT_TASK:>
Python Code:
model_text = \
'''
Active ATM phosphorylates ATM, and phosphorylated ATM is active.
Active ATM activates p53.
p53 is transcribed and active p53 transcribes MDM2.
MDM2 is degraded.
Active p53 activates Wip1.
Active Wip1 inactivates p53.
Active Wip1 dephosphorylates ATM.
MDM2 ubiquitinates ... |
<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: In the Data Processing Pipelines section we discovered how to declare a DynamicMap and control multiple processing steps with the use of custom ... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import holoviews as hv
from bokeh.sampledata import stocks
from holoviews.operation.timeseries import rolling, rolling_outlier_std
hv.extension('bokeh')
def load_symbol(symbol, variable='adj_close', **kwargs):
df = pd.DataFrame(getattr(stocks, symbol))
df['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: Enter Your Project and GCS Bucket
Step2: Set Your Application Name, Task Name, and Directories.
Step3: Set a GCS Prefix
Step7: Stack Overflow... | <ASSISTANT_TASK:>
Python Code:
!pip3 uninstall -y google-cloud-aiplatform
!pip3 install google-cloud-aiplatform
import IPython
app = IPython.Application.instance()
app.kernel.do_shutdown(True)
MY_PROJECT = "YOUR PROJECT ID"
MY_STAGING_BUCKET = "gs://YOUR BUCKET" # bucket should be in same region as ucaip
APP_NAME = ... |
<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: Symbolic differentiation
Step2: You can even ask sympy to give you a formula you can evaluate in a coding language
Step3: And sympy can evalua... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import sympy
from sympy.abc import x
f = sympy.cos(x**sympy.pi) * sympy.log(x)
f
sympy.diff(f, x)
sympy.ccode(f, 'y')
sympy.fcode(f, 'y')
f.evalf(40, subs={x: 1.9})
def g(x, m=np):
y = x
... |
<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: At this point, we have our stations and trips data loaded into memory.
Step2: Then, let's iterate over the stations DataFrame, and add in the ... | <ASSISTANT_TASK:>
Python Code:
# This block of code checks to make sure that a particular directory is present.
if "divvy_2013" not in os.listdir('datasets/'):
print('Unzip the divvy_2013.zip file in the datasets folder.')
stations = pd.read_csv('datasets/divvy_2013/Divvy_Stations_2013.csv', parse_dates=['online da... |
<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 house sales data
Step2: Split data into training and testing
Step3: Useful SFrame summary functions
Step4: As we see we get the same ans... | <ASSISTANT_TASK:>
Python Code:
import graphlab
sales = graphlab.SFrame('kc_house_data.gl/')
train_data,test_data = sales.random_split(.8,seed=0)
# 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 now an SA... |
<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: Exercรญcios - Mรฉtodos e Funรงรตes
| <ASSISTANT_TASK:>
Python Code:
# Versรฃo da Linguagem Python
from platform import python_version
print('Versรฃo da Linguagem Python Usada Neste Jupyter Notebook:', python_version())
# Exercรญcio 1 - Crie uma funรงรฃo que imprima a sequรชncia de nรบmeros pares entre 1 e 20 (a funรงรฃo nรฃo recebe parรขmetro) e
# depois faรงa uma ... |
<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: Plotting Histogram
Step2: By default, hist is using 10 equal bins to plot the data, we can increase this no by using bins=n
Step3: Another imp... | <ASSISTANT_TASK:>
Python Code:
# import
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# generating some data points
X = np.random.random_integers(20, 50, 1000)
Y = np.random.random_integers(20, 50, 1000)
plt.hist(X)
plt.xlabel("Value of X")
plt.ylabel("Freq")
gaussian_numbers = np.random.normal... |
<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:
| <ASSISTANT_TASK:>
Python Code::
# one hot encode target sequence
def encode_output(sequences, vocab_size):
ylist = list()
for sequence in sequences:
encoded = to_categorical(sequence, num_classes=vocab_size)
ylist.append(encoded)
y = array(ylist)
y = y.reshape(sequences.shape[0], sequences.shape[1], vocab_size)
... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Exercicio 2 - Juros Compostos
Step2: Exercicio 3 - Imposto de renda
Step3: Exercรญcio 4 - Somatรณria
Step4: Exercicio 5 - Palรญndromo
Step5: Ex... | <ASSISTANT_TASK:>
Python Code:
def CaixaEletronico(valor):
notas50 = valor // 50
valor = valor % 50
notas20 = valor // 20
valor = valor % 20
notas10 = valor // 10
valor = valor % 10
notas5 = valor // 5
valor = valor % 5
notas1 = valor // 1
return (notas50, notas20, notas10, notas... |
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