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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. Eksplorasi Awal Data (10 poin)
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Python Code:
from __future__ import print_function, division # Gunakan print(...) dan bukan print ...
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.datasets import load_digits
from sklearn.cluster import KMeans
from sklearn.metrics import acc... |
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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 Description
Step2: From the above info(),We can see columns Age, Cabin and Embarked have missing values.
Step3: lets decide should we rem... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from IPython.display import display
%matplotlib inline
# Load the dataset
files = 'titanic_data.csv'
data_titanic = pd.read_csv(files)
display(data_titanic.head())
data =data_titanic
# Show the dataset
display(data.head())
data.info()
#exculding 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: monuments_all_se-bbr_(sv)
Step2: Let's check If we have extracted the two always occuring fields 'name' and 'buildId' for all objects
Step3: H... | <ASSISTANT_TASK:>
Python Code:
cursor.execute("SELECT * FROM monuments_all WHERE country='se-bbr'")
all_bbr = pd.io.sql.read_sql('select * from monuments_all WHERE country="se-bbr"', conn)
all_bbr.shape
table_name = "se_bbr" # I've renamed monuments_se-bbr_(se) to 'se_bbr' in local database, change to correct name
se_... |
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Description:
Step1: 훈련 후 동적 범위 양자화
Step2: TensorFlow 모델 훈련하기
Step3: 예를 들어, 단일 epoch에 대해서만 모델을 훈련했기 때문에 최대 96%의 정확성으로만 훈련됩니다.
Step4: tflite 파일에 작성합니다.
Step5: 내보낼... | <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: Create or load data set
Step2: Data set characteristics
Step3: Unknown parameters
Step4: Estimate beta
Step5: Alternatively, estimate beta b... | <ASSISTANT_TASK:>
Python Code:
import random
import operator as op
import optunity.metrics
import semisup_metrics as ss
import numpy as np
from matplotlib import pyplot as plt
import pickle
import csv
import util
%matplotlib inline
# fraction of positives/negatives that are known
# known_neg_frac == 0 implies PU learn... |
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Description:
Step1: ¿Cómo?
Step2: ¿Cómo?
Step3: Ejemplo 1
Step4: Ejemplo 1
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Python Code:
from matplotlib import pyplot as plt
import numpy as np
x = np.linspace(1E-8,np.pi/6,1000)
y = x*np.sin(1./x)
plt.plot(x, y)
plt.plot(x, x)
plt.plot(x, -x)
plt.show()
from mat281_code import black_box
black_box.iplot()
# Calculando el promedio
x = np.array([1.2, 2.2, 2.6, 3.1, 3.1, 3.2,... |
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Description:
Step2: The strategy, unlike our first attempt, requires a real train/test split in the dataset because we're going to fit an actual model (although a t... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import cPickle as pickle
from copy import deepcopy
%matplotlib inline
plt.style.use("fivethirtyeight")
sns.set()
all_graphs = pickle.load(open("train-freq-graphs.pkl",'r'))
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: 迁移学习和微调
Step2: 数据预处理
Step3: 显示训练集中的前九个图像和标签：
Step4: 由于原始数据集不包含测试集，因此您需要创建一个。为此，请使用 tf.data.experimental.cardinality 确定验证集中有多少批次的数据，然后将其中的 20%... | <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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Description:
Step1: The actual data that we will use comes from the GIANT consortium
Step2: Next, we will read in the data for two groups.
Step3: Lastly, we run t... | <ASSISTANT_TASK:>
Python Code:
# from assocplots.misc import mock_data_generation
# data_m, data_w = mock_data_generation(M=100000, seed=42)
# data_m['pval'] /= 500000.*np.exp(-(data_m['pos']-10000.)**2/50000.0) * (data_m['chr']=='4') * np.random.rand(len(data_m)) + 1.
# Load standard libraries
import numpy as np
from... |
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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 error is actually reassuring
Step2: What should range_overlap do in this case
Step3: Do two segments that touch at their endpoints overlap... | <ASSISTANT_TASK:>
Python Code:
assert range_overlap([ (0.0, 1.0) ]) == (0.0, 1.0)
assert range_overlap([ (2.0, 3.0), (2.0, 4.0) ]) == (2.0, 3.0)
assert range_overlap([ (0.0, 1.0), (0.0, 2.0), (-1.0, 1.0) ]) == (0.0, 1.0)
assert range_overlap([ (0.0, 1.0), (5.0, 6.0) ]) == ???
assert range_overlap([ (0.0, 1.0), (1.0, ... |
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Description:
Step1: Load the Data into an SMPS object
Step2: Explore the SMPS Object
Step3: SMPS.bins and SMPS.midpoints
Step4: SMPS.histogram and SMPS.raw
Step5... | <ASSISTANT_TASK:>
Python Code:
import smps
import seaborn as sns
import os
import matplotlib
import matplotlib.pyplot as plt
import json
%matplotlib inline
# You can use seaborn to easily control how your plots appear
sns.set('notebook', style='ticks', font_scale=1.5, palette='dark')
smps.set()
print ("smps v{}".format... |
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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: Looking at Predicted Time of Onset
Step2: Interestingly a lot of the paitents off the diagnonal in the recently diagnosed group have detectable... | <ASSISTANT_TASK:>
Python Code:
import NotebookImport
from IPython.display import clear_output
from HIV_Age_Advancement import *
from Setup.DX_Imports import *
import statsmodels.api as sm
import seaborn as sns
sns.set_context("paper", font_scale=1.7, rc={"lines.linewidth": 2.5})
sns.set_style("white")
fig, ax = subplo... |
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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: <div class="alert alert-info"><h4>Note</h4><p>An uninitialized matrix is declared,
Step2: Construct a randomly initialized matrix
Step3: Const... | <ASSISTANT_TASK:>
Python Code:
import torch
x = torch.empty(5, 3)
print(x)
type(x)
x = torch.rand(5, 3)
print(x)
x = torch.zeros(5, 3, dtype=torch.long)
print(x)
x = torch.tensor([5.5, 3])
print(x)
x = x.new_ones(5, 3, dtype=torch.double) # new_* methods take in sizes
print(x)
x = torch.randn_like(x, dtype=to... |
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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: So what is going on? Well, Python variables are reference variables. You could say "the variable a (b) is assigned to a list" rather than "the... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import HTML # Allows us to embed HTML into our notebook.
HTML('<iframe width="800" height="400" frameborder="0" src="http://pythontutor.com/iframe-embed.html#code=a%20%3D%20%5B1,%203,%205%5D%0Ab%20%3D%20a%0Aprint%28%22a%20%3D%20%7B0%7D%20and%20has%20id%20%7B1%7D%22.fo... |
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-2', 'atmos')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "... |
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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: There are some infeasibilities without line extensions.
Step2: Performing security-constrained linear OPF
Step3: For the PF, set the P to the ... | <ASSISTANT_TASK:>
Python Code:
import pypsa, os
import numpy as np
network = pypsa.examples.scigrid_de(from_master=True)
for line_name in ["316", "527", "602"]:
network.lines.loc[line_name, "s_nom"] = 1200
now = network.snapshots[0]
branch_outages = network.lines.index[:15]
network.sclopf(now, branch_outages=bran... |
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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: For this simple circuit the questions are
Step2: Components
Step3: Explore and explain the results
Step4: We also good visual diagnostics in... | <ASSISTANT_TASK:>
Python Code:
Image("res4.gif")
import numpy as np
import matplotlib.pyplot as plt
import pymc3 as pm
import pandas as pd
import seaborn as sns
sns.set()
%matplotlib inline
## setup the model
# these are the values and precision of each
Datasheets = {'R1':(6.0, 0.01),
'R2':(8.0, 0.01)... |
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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: Toolkit
Step2: Binary Classification
Step3: Confusion Matrix
Step4: The above two confusion matrixes show the same network. The bottom (norm... | <ASSISTANT_TASK:>
Python Code:
from sklearn import preprocessing
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Encode text values to dummy variables(i.e. [1,0,0],[0,1,0],[0,0,1] for red,green,blue)
def encode_text_dummy(df,name):
dummies = pd.get_dummies(df[name])
for x in dummies.col... |
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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 and deploy the model
Step2: We one-hot encode the label...
Step3: ...and create a train/test split.
Step4: Swivel Model
Step5: The bui... | <ASSISTANT_TASK:>
Python Code:
# change these to try this notebook out
PROJECT = 'munn-sandbox'
BUCKET = 'munn-sandbox'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['TFVERSION'] = '2.1'
import shutil
import pandas as pd
import tensorflow as tf
from google.cloud import bigquery
from... |
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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: Creating Evoked objects from Epochs
Step2: Basic visualization of Evoked objects
Step3: Like the plot() methods for
Step4: To select based o... | <ASSISTANT_TASK:>
Python Code:
import os
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_data_raw_file, verbose=False)
events = mne.find... |
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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 reset the notebook's session kernel! Since we're no longer using Cloud Dataflow, we'll be using the python3 kernel from here on out so don't... | <ASSISTANT_TASK:>
Python Code:
!pip3 install tensorflow_hub
%%bash
pip install --upgrade tensorflow
# Import helpful libraries and setup our project, bucket, and region
import os
import tensorflow as tf
import tensorflow_hub as hub
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT ID
BUCKET = "cloud-trainin... |
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Description:
Step1: Introduction
Step2: Setup the operators, Hamiltonian, and initial state
Step4: Below, we define the terms specific to the Bloch-Redfield solve... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import numpy as np
import itertools
from qutip import *
from numpy import *
n_Pi = 13 # 8 pi pulse area
Om_list = np.linspace(0.001, n_Pi, 80) # dr... |
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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: Classical expressions
Step2: Or, using the diffs function we defined above
Step3: Weighted Expressions
Step4: And this is tightly connected w... | <ASSISTANT_TASK:>
Python Code:
import vcsn
from IPython.display import Latex
def diffs(r, ss):
eqs = []
for s in ss:
eqs.append(r'\frac{{\partial}}{{\partial {0}}} {1}& = {2}'
.format(s,
r.format('latex'),
r.derivation(s).format('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: Local Training with Vertex Local Mode and Auto Packaging
Step2: Vertex Training using Vertex SDK and Vertex Local Mode Container
Step3: Initia... | <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 = "tf-keras-txt-cls-dist-single-worker-gpus-local-mode-cont"
BASE_IMAGE_URI = "us-docker.pkg.dev/vertex-ai/training/tf-gpu.2-5:latest"
SCRIPT_... |
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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 find the peak at Pb-212 ~ 238 keV
Step2: This is good enough for now but we can fix l8tr if needed
Step3: expected
Step4: Expected
Ste... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
csv = np.genfromtxt('thorium_test_2019-02-19_D3S.csv', delimiter= ",").T
summed = np.sum(csv[:-1], axis=1) # gets rid of last value
plt.plot(summed)
plt.yscale('log')
plt.show()
Pb_shift = 250
Pb_rang... |
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Description:
Step1: Comment. When you run the code cell above, its output appears below it.
Step2: Create dataframes to play with
Step3: Comment. In the previ... | <ASSISTANT_TASK:>
Python Code:
import sys # system module
import pandas as pd # data package
import matplotlib as mpl # graphics package
import matplotlib.pyplot as plt # graphics module
import datetime as dt # date and time module
... |
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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: Overfit and underfit
Step2: The Higgs dataset
Step3: The tf.data.experimental.CsvDataset class can be used to read csv records directly from a... | <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: Some Formal Background (Skip if you just want code examples)
Step2: Apart from its apealling form, this curve has the nice property of given ri... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
# import all shogun classes
from shogun import *
import random
import numpy as np
import matplotlib.pyplot as plt
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from math import exp
# plot likelihood for three different noise lebels $\sigma$ (w... |
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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 defaults for plotting
Step2: Dataframe and bins initialization
Step3: Plotting O2 respiration rate and mitochondrial volume ratio as a fun... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os
import os.path as op
import cPickle as pickle
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
plt.close('all')
datadir = op.join(os.getcwd(), 'data')
#Oxygen consumption data
with open(op.join(datadir, 'o2data.pkl')... |
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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: In our case, we only want to check the modularization of our software for Java production code. So we just leave the files that are belonging to... | <ASSISTANT_TASK:>
Python Code:
from lib.ozapfdis.git_tc import log_numstat
GIT_REPO_DIR = "../../dropover_git/"
git_log = log_numstat(GIT_REPO_DIR)[['sha', 'file', 'author']]
git_log.head()
prod_code = git_log.copy()
prod_code = prod_code[prod_code.file.str.endswith(".java")]
prod_code = prod_code[prod_code.file.str.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: Data preprocessing
Step2: Encoding the words
Step3: Encoding the labels
Step4: Okay, a couple issues here. We seem to have one review with ze... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import tensorflow as tf
with open('reviews.txt', 'r') as f:
reviews = f.read()
with open('labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
from string import punctuation
all_text = ''.join([c for c in reviews if c not in punctuation])
reviews = all_text... |
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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: Aufgabe 2
Step2: Aufgabe 3
Step3: Groundtruth-Label anpassen
Step4: Aufgabe 4
Step5: Aufgabe 5
Step6: Aufgabe 6
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Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pprint as pp
hdfs = pd.HDFStore("../../data/raw/henrik/TestMessungen_NEU.hdf")
hdfs.keys
df1 = hdfs.get('/x1/t1/trx_1_2')
df1.head(5)
# Little function to retrieve sende... |
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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 FIF file and display the projections present in the file. Here the
Step2: Display the projections one by one
Step3: Use the function ... | <ASSISTANT_TASK:>
Python Code:
# Author: Joan Massich <mailsik@gmail.com>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import read_proj
from mne.io import read_raw_fif
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
... |
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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: These features are
Step2: Derived Features
Step3: Here is a broad description of the keys and what they mean
Step4: We clearly want to discar... | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_iris
iris = load_iris()
print(iris.data.shape)
measurements = [
{'city': 'Dubai', 'temperature': 33.},
{'city': 'London', 'temperature': 12.},
{'city': 'San Francisco', 'temperature': 18.},
]
from sklearn.feature_extraction import DictVectori... |
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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: How to build a simple text classifier with TF-Hub
Step2: More detailed information about installing Tensorflow can be found at https
Step3: Ge... | <ASSISTANT_TASK:>
Python Code:
# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... |
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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: Filling in Missing Values
Step2: Generating Features
Step3: Cross Validation Method
Step4: Iteration 1
Step5: Iteration 2
Step6: Iteration ... | <ASSISTANT_TASK:>
Python Code:
import py_entitymatching as em
import os
import pandas as pd
# specify filepaths for tables A and B.
path_A = 'newTableA.csv'
path_B = 'tableB.csv'
# read table A; table A has 'ID' as the key attribute
A = em.read_csv_metadata(path_A, key='id')
# read table B; table B has 'ID' as the key... |
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Description:
Step1: Parameter Selection with Preprocessing
Step2: Building Pipelines
Step3: Using Pipelines in Grid-searches
Step4: The General Pipeline Interfac... | <ASSISTANT_TASK:>
Python Code:
from sklearn.svm import SVC
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
# load and split the data
cancer = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(
... |
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Description:
Step1: Data Exploration
Step2: Question 1 - Feature Observation
Step3: LSTAT
Step4: PTRATIO
Step6: Developing a Model
Step7: Question 2 - Goodness... | <ASSISTANT_TASK:>
Python Code:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
from sklearn.cross_validation import ShuffleSplit
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the Boston housing dat... |
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Description:
Step1: Definitions
Step2: Problem Statement
Step3: Plotting the T-s diagram of the cycle,
Step4: Summarizing the states
Step5: <div class="alert al... | <ASSISTANT_TASK:>
Python Code:
from thermostate import State, Q_, units, SystemInternational as SI
from thermostate.plotting import IdealGas, VaporDome
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
substance = 'water'
T_1 = Q_(560.0, 'degC')
p_1 = Q_(16.0, 'MPa')
mdot_1 = Q_(120.0, 'kg/s')
p_2 ... |
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Description:
Step1: 通过 Keras 模型创建 Estimator
Step2: 创建一个简单的 Keras 模型。
Step3: 编译模型并获取摘要。
Step4: 创建输入函数
Step5: 测试您的 input_fn
Step6: 通过 tf.keras 模型创建 Estimator。
St... | <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: An existing MODFLOW6 model is in the directory freyberg_mf6. Lets check it out
Step2: You can see that all the input array and list data for t... | <ASSISTANT_TASK:>
Python Code:
import os
import shutil
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pyemu
import flopy
org_model_ws = os.path.join('freyberg_mf6')
os.listdir(org_model_ws)
id_arr = np.loadtxt(os.path.join(org_model_ws,"freyberg6.dis_idomain_layer3.txt"))
top_arr = np.l... |
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Description:
Step1: We can select the prices for the available time periods of Dubai crude oil by using the [] operator
Step2: We can also pass a list of columns t... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
SpotCrudePrices_2013_Data= { 'U.K. Brent' :
{'2013-Q1':112.9, '2013-Q2':103.0,
'2013-Q3':110.1, '2013-Q4':109.4},
'Dubai':
{'2013-Q1':108.1, '2013-Q2':10... |
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Description:
Step1: 可能小伙伴们会觉得设计模式这块的东西略微有些复杂，完全不用感到灰心，如果不是想要将软件开发作为自己的职业的话，可能一辈子也不需要了解，或者不经意间用到也不知道。但是这部分内容可以用来复习类的概念知识。
Step2: 每次函数都要输出一个print语句告知用户当前在哪个函数中，这样的操作... | <ASSISTANT_TASK:>
Python Code:
import os
class Dog(object):
def __init__(self):
self.name = "Dog"
def bark(self):
return "woof!"
class Cat(object):
def __init__(self):
self.name = "Cat"
def meow(self):
return "meow!"
class Human(object):
def __init__(self):
se... |
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Description:
Step1: Figure 33.1
Step2: 33.3
Step3: 33.5/6
Step5: 33.9
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Python Code:
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
from scipy.stats import norm
from scipy.signal import convolve2d
import skimage.measure
x = np.arange(-5,5, .01)
pdf = norm.pdf(x)
data = np.random.randn(1000)
fig, ax = plt.subplots(1,2, sharex='all')
ax[0].plot(x, pd... |
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Description:
Step1: Solve system of ODE's by Scipy
Step2: Reproduction Ratio
Step3: Note
Step4: Conclusion
Step5: Ebola
Step6: Governed Differential Equations
... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from numpy import log
from scipy import integrate
from scipy.optimize import fsolve
import scipy.linalg as la
import scipy.integrate as spi
from IPython.html.widgets import interact, interactive, fixed
from IPython.html... |
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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 create two of the most common Fields, imagining we are preparing some data for a sentiment analysis model.
Step2: Once we've made our Fie... | <ASSISTANT_TASK:>
Python Code:
# This cell just makes sure the library paths are correct.
# You need to run this cell before you run the rest of this
# tutorial, but you can ignore the contents!
import os
import sys
module_path = os.path.abspath(os.path.join('../..'))
if module_path not in sys.path:
sys.path.appen... |
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Description:
Step1: %Set up useful MathJax (Latex) macros.
Step2: Notes
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Python Code:
%matplotlib nbagg
%config InlineBackend.figure_format='retina'
# import libraries
import numpy as np
import matplotlib as mp
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
from importlib import reload
from datetime import datetime
import laUtilities as ut
import s... |
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Description:
Step1: Data Preparation
Step2: We are getting a dataset of dataset_size sequences of integers of length seq_len between 0 and max_num. We use split*10... | <ASSISTANT_TASK:>
Python Code:
import random
import string
import mxnet as mx
from mxnet import gluon, nd
import numpy as np
max_num = 999
dataset_size = 60000
seq_len = 5
split = 0.8
batch_size = 512
ctx = mx.gpu() if len(mx.test_utils.list_gpus()) > 0 else mx.cpu()
X = mx.random.uniform(low=0, high=max_num, shape=(... |
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Description:
Step1: DQN C51/Rainbow
Step2: ハイパーパラメータ
Step3: 環境
Step4: エージェント
Step5: また、先ほど作成したネットワークをトレーニングするためのoptimizerと、ネットワークが更新された回数を追跡するためのtrain_step_coun... | <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: Basic animation
Step2: You can control which array to visualize, using the scatter.sequence_index property. Actually, the pylab.animate_glyphs ... | <ASSISTANT_TASK:>
Python Code:
import ipyvolume as ipv
import numpy as np
# only x is a sequence of arrays
x = np.array([[-1, -0.8], [1, -0.1], [0., 0.5]])
y = np.array([0.0, 0.0])
z = np.array([0.0, 0.0])
ipv.figure()
s = ipv.scatter(x, y, z, marker='sphere', size=10)
ipv.xyzlim(-1, 1)
ipv.animation_control(s) # show... |
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Description:
Step1: Player and odds data from 2010-2016 has beeen matched and stored. Retrieve, merge, and rename.
Step5: Get additional training data. We'll inc... | <ASSISTANT_TASK:>
Python Code:
import sqlalchemy # pandas-mysql interface library
import sqlalchemy.exc # exception handling
from sqlalchemy import create_engine # needed to define db interface
import sys # for defining behavior under errors
import numpy as np # numerical libraries
import scipy as sp
import pandas 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: All countries except for Denmark seem to follow a normal distribution
Step2: In this case we have run all the comparisons between countries. We... | <ASSISTANT_TASK:>
Python Code:
#Get info of the dataset once reduced to the trust variable
#Slice the dataframe to the people trust variable
ppltrust = raw_data[['cntry','cntry_year','ppltrst']]
#Info
ppltrust.info()
#Clean the values in the dataframe that are null
ppltrust_clean = ppltrust[ppltrust.ppltrst.notnull()]
... |
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Description:
Step1: Data Format
Step2: Parameters
Step3: Network Parameters
Step4: TensorFlow Graph Input
Step5: MultiLayer Model
Step6: Weights and Bias
Step7... | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
# Import MINST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
type(mnist)
type(mnist.train.images)
#mnist.train.images[0]
mnist.train.images[2].shape
sample = mnist.train.images[2].resh... |
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Description:
Step1: Loading the simulation data
Step2: The documentation shows us that mne.read_epochs takes one required parameter (fname) and three optional para... | <ASSISTANT_TASK:>
Python Code:
# Don't worry about warnings in this exercise, as they can be distracting.
import warnings
warnings.simplefilter('ignore')
# Import the required Python modules
import mne
import conpy
import surfer
# Import and configure the 3D graphics backend
from mayavi import mlab
mlab.init_notebook('... |
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Description:
Step1: 2. Linear Regression
Step2: The orange line on the plot above is the number of page views in blue and the orange line is the CPU load that view... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (13.0, 8.0)
%matplotlib inline
import pickle
import sklearn
import sklearn.linear_model
import sklearn.preprocessing
import sklearn.gaussian_process
import sklearn.ensemble
import pickle # Pickle files al... |
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Description:
Step1: For this example, we're going to re-create the market segmentation
Step2: We can use the query_cases method to create two separate datasets for... | <ASSISTANT_TASK:>
Python Code:
# TEST
import larch.numba as lx
import larch
import pandas as pd
pd.set_option("display.max_columns", 999)
pd.set_option('expand_frame_repr', False)
pd.set_option('display.precision', 3)
larch._doctest_mode_ = True
import larch.numba as lx
d = lx.examples.MTC(format='dataset')
d1 = d.dc... |
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Description:
Step1: To exercise our MLP skills, we will start with a very simple Dataset. It consists of wave forms over time. They can be thought of captured accel... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import logging
logging.basicConfig(level=40)
logger = logging.getLogger()
import numpy as np
import math
import random
from neon.datasets.dataset import Dataset
class GeneratedDS(Dataset):
# for each example we will generate 400 time steps
feature_count = 4... |
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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: Example 1
Step2: Next, we can open a file containing pre calculated spatial weights for "sids2.dbf". In case you don't have spatial weights, ch... | <ASSISTANT_TASK:>
Python Code:
from pysal.lib.weights.contiguity import Queen
from pysal.lib import examples
import pysal.lib as lp
import geopandas as gpd
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
%matplotlib inline
f = gpd.read_file(examples.get_path("sids2.dbf"))
varna... |
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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 plot shows the simulated data as black points with error bars and the true function is shown as a gray line.
Step2: Then we wrap this kern... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(42)
t = np.sort(np.append(
np.random.uniform(0, 3.8, 57),
np.random.uniform(5.5, 10, 68),
)) # The input coordinates must be sorted
yerr = np.random.uniform(0.08, 0.22, len(t))
y = 0.2 * (t-5) + np.sin(3*t + 0.1*(t... |
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Description:
Step1: A Series is like a cross between a list and a dictionary. The items are stored in an order and there are labels
Step2: Querying a Series
Step3... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
animals = ["Lion", "Tiger", "Monkey", None]
s = pd.Series(animals)
print(s)
print("The name of this Series: ", s.name)
numbers = [1, 2, 3, None]
pd.Series(numbers)
import numpy as np
np.NaN == None
np.NaN == np.NaN
np.isnan(np.NaN)
sports = {'Cricket': 'India', 'Footb... |
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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: Reading data back from npz file
Step2: I use interact on my plotter function to plot the positions of the stars and galaxies in my system at ev... | <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, interactive, fixed
from plotting_function import plotter
f = open('two_star_test_sol+ic.npz','r')
r = np.load('two_star_test_sol+ic.npz')
so... |
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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: TQDM Progress Bar (ipywidget)
Step2: TQDM Progress Bar (text)
| <ASSISTANT_TASK:>
Python Code:
from mnist_model import mnist_model
from keras_tqdm import TQDMCallback, TQDMNotebookCallback
mnist_model(0, [TQDMNotebookCallback()])
mnist_model(0, [TQDMCallback()])
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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: Selection and Indexing
Step2: DataFrame Columns are just Series
Step3: Creating a new column
Step4: Removing Columns
Step5: Can also drop ro... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
from numpy.random import randn
np.random.seed(101)
df = pd.DataFrame(randn(5, 4),
index = 'A B C D E'.split(),
columns = 'W X Y Z'.split())
df
df['W']
# Pass a list of column names
df[['W', 'Z']]
# SQL Syntax (NOT... |
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Description:
Step1: Example 2
Step2: The previous step constructs a log-linear approximation of the model and then solves for the endogenous variables as functions... | <ASSISTANT_TASK:>
Python Code:
# 1. Input model parameters
parameters = pd.Series()
parameters['rhoa'] = .9
parameters['sigma'] = 0.001
print(parameters)
# 2. Define a function that evaluates the equilibrium conditions
def equilibrium_equations(variables_forward,variables_current,parameters):
# Parameters
... |
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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: 測試資料：
Step2: (三)FeatureUnionc
Step3: (四)找到最佳的結果
| <ASSISTANT_TASK:>
Python Code:
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.grid_search import GridSearchCV
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
from sklearn.feature_selection import SelectKBest
iris = load_iris()
X, y = iris.data, ... |
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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: Sorting
Step2: Q2. Sort pairs of surnames and first names and return their indices. (first by surname, then by name).
Step3: Q3. Get the indic... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.__version__
author = 'kyubyong. longinglove@nate.com'
x = np.array([[1,4],[3,1]])
out = np.sort(x, axis=1)
x.sort(axis=1)
assert np.array_equal(out, x)
print out
surnames = ('Hertz', 'Galilei', 'Hertz')
first_names = ('Heinrich', 'Galileo', 'Gustav')
print np... |
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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: Create a new Workspace and Run in a workspace
Step2: Create an execution in a run
Step3: Log a data set and a model
Step4: A Log_output log a... | <ASSISTANT_TASK:>
Python Code:
# To use the latest publish `kubeflow-metadata` library, you can run:
!pip install kubeflow-metadata --user
# Install other packages:
!pip install pandas --user
# Then restart the Notebook kernel.
import pandas
from kubeflow.metadata import metadata
from datetime import datetime
from uuid... |
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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: 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', 'bcc', 'sandbox-3', 'landice')
# 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: Make code compatible with AI Platform Training Service
Step2: Move code into a python package
Step3: Paste existing code into model.py
Step4: ... | <ASSISTANT_TASK:>
Python Code:
# change these to try this notebook out
PROJECT = <YOUR PROJECT>
BUCKET = <YOUR PROJECT>
REGION = <YOUR REGION>
import os
os.environ['PROJECT'] = PROJECT
os.environ['BUCKET'] = BUCKET
os.environ['REGION'] = REGION
os.environ['TFVERSION'] = "2.1"
%%bash
gcloud config set project $PROJECT
g... |
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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: Create Dataframe
Step2: View Column
Step3: View Two Columns
Step4: View First Two Rows
Step5: View Rows Where Coverage Is Greater Than 50
St... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
data = {'name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
'year': [2012, 2012, 2013, 2014, 2014],
'reports': [4, 24, 31, 2, 3],
'coverage': [25, 94, 57, 62, 70]}
df = pd.DataFrame(data, index = ['Cochice', 'Pima', 'Santa Cruz', 'Maricopa', '... |
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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: Downloading Data
Step2: Munging Data
Step3: Training Models
Step4: Trying different hyperparameters
Step5: Creating your own estimator
Step6... | <ASSISTANT_TASK:>
Python Code:
# packages for downloading the data
import os
import urllib
# packages for munging, plotting, machine learning
import pandas as pd
import numpy as np
import warnings
# xgboost uses the deprecated sklearn.cross_validate module, but we don't depend on it
with warnings.catch_warnings():
... |
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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 for Everyone!<br/>Oregon Curriculum Network
Step2: y's value is an ordinary int, equivalently the value of MyClass.__dict__['y'], wherea... | <ASSISTANT_TASK:>
Python Code:
class RevealAccess(object):
A data descriptor that sets and returns values
normally and prints a message logging their access.
Descriptor Example:
https://docs.python.org/3/howto/descriptor.html
def __init__(self, initval=None, name='var'):
... |
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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 first generate some random input points within a specified region using v.random (we use a fixed seed here for reproducibility)
Step2: Workf... | <ASSISTANT_TASK:>
Python Code:
import os
os.environ['GRASS_OVERWRITE'] = '1'
import grass.script as gscript
gscript.run_command('g.region', n=225200, s=222500, w=637500, e=640000, raster='elevation')
gscript.run_command('v.random', output='input_points', npoints=20, seed=2, quiet=True)
from grass.pygrass.vector.geome... |
<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: 量子卷积神经网络
Step2: 安装 TensorFlow Quantum：
Step3: 现在，导入 TensorFlow 和模块依赖项：
Step4: 1. 构建 QCNN
Step5: 检查输入张量：
Step6: 检查输出张量：
Step8: 虽然不使用 tfq.la... | <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: Now, as data scientists we dont know this relationship between y and x. Rather we have collected observations of y. These observations are bound... | <ASSISTANT_TASK:>
Python Code:
# import libraries
import matplotlib
import IPython
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
import pylab
import seaborn as sns
import sklearn as sk
%matplotlib inline
# Ignore for now!
x = np.array(np.linspace(0,10,400))
y = 10*x+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:
Step1: Next get the login credentials
Step2: Making requests
Step3: Finding available cols/frags
Step4: transcriptions
Step5: The actual data looks... | <ASSISTANT_TASK:>
Python Code:
import sys, pprint, json
try:
import requests
except ImportError:
!conda install --yes --prefix {sys.prefix} requests
import requests
try:
from genson import SchemaBuilder
except ImportError:
!conda install --yes --prefix {sys.prefix} genson
from genson import... |
<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 basics of generators
Step2: Advanced uses of generators
Step3: Generator.throw
Step4: Generator.close
Step5: Generators as suspendable/r... | <ASSISTANT_TASK:>
Python Code:
def range_generator_function(stop):
Naive implementation of builtins.range generator.
# This function runs immediately, since it has no `yield` statements.
# It is a normal function, which happens to return a generator iterator.
print("Running line 1")
if not isinstanc... |
<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: one-liner equivalent
Step2: The general form of list comprehension is
Step3: which is equivalent to
Step4: since this expression implements a... | <ASSISTANT_TASK:>
Python Code:
l = []
for i in range(10):
l.append(2*i+1)
l
l = [2*i+1 for i in range(10)]
l
even = [n*n for n in range(20) if n % 2 == 0]
even
even = []
for n in range(20):
if n % 2 == 0:
even.append(n)
even
l = [1, 0, -2, 3, -1, -5, 0]
signum_l = [int(n / abs(n)) if n != 0 else 0 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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<USER_TASK:>
Description:
Step1: Download Necessary Models
Step3: Example
Step4: We can query all entities mentioned in a text.
Step5: Or, we can query entites per sentence
S... | <ASSISTANT_TASK:>
Python Code:
from polyglot.downloader import downloader
print(downloader.supported_languages_table("ner2", 3))
%%bash
polyglot download embeddings2.en ner2.en
from polyglot.text import Text
blob = The Israeli Prime Minister Benjamin Netanyahu has warned that Iran poses a "threat to the entire world"... |
<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: Introduction to Fairness Indicators
Step2: You must restart the Colab runtime after installing. Select Runtime > Restart runtime from the Colab... | <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: Physical Data
Step2: The IncidentNeutron class
Step3: Cross sections
Step4: Cross sections for each reaction can be stored at multiple temper... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os
from pprint import pprint
import shutil
import subprocess
import urllib.request
import h5py
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm
from matplotlib.patches import Rectangle
import openmc.data
openmc.data.atomic_mass('Fe54')
ope... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: If CPLEX is not installed, you can install CPLEX Community edition.
Step2: 2. Model the data
Step3: 3. Set up the prescriptive model
Step4: W... | <ASSISTANT_TASK:>
Python Code:
import sys
try:
import docplex.mp
except:
raise Exception('Please install docplex. See https://pypi.org/project/docplex/')
try:
import cplex
except:
raise Exception('Please install CPLEX. See https://pypi.org/project/cplex/')
B = [15, 15, 15]
C = [
[ 6, 10, 1],
[... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load the data from the publication
Step2: Create and fit a receptive field model
Step3: Investigate model coefficients
Step4: Create and fit ... | <ASSISTANT_TASK:>
Python Code:
# Authors: Chris Holdgraf <choldgraf@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# Nicolas Barascud <nicolas.barascud@ens.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import loadmat
from os.path import join
impor... |
<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: .csv — Comma-Separated Values
Step2: But there are more than just strings or rows with a single data type. We can use the csv library to handle... | <ASSISTANT_TASK:>
Python Code:
#pandas is commonly imported as pd
import pandas as pd
#We'll import the other libraries as needed
print("Split on comma as strings")
csv_row = '1,2.0,Three point five,True'
print(csv_row.split(','))
print("\nSplit on comma and converted to ints")
csv_row = '1,2,3,4,5,6,7,8,9'
print([int... |
<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: Modify the network a bit
Step2: Add ramp limit
Step3: Add additional storage units (cyclic and non-cyclic) and fix one state_of_charge
Step4: ... | <ASSISTANT_TASK:>
Python Code:
import pypsa
import pandas as pd
import os
n = pypsa.examples.ac_dc_meshed(from_master=True)
n.generators.loc[n.generators.carrier == "gas", "p_nom_extendable"] = False
n.generators.loc[n.generators.carrier == "gas", "ramp_limit_down"] = 0.2
n.generators.loc[n.generators.carrier == "gas... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Setup of the current experiment
Step2: Setup of the model priors
Step3: Setup of the forecast Fisher matrix
Step4: Running the main foxi algo... | <ASSISTANT_TASK:>
Python Code:
import sys
path_to_foxi = '/Users/Rob/work/foxi' # Give your path to foxi here.
sys.path.append(path_to_foxi + '/foxisource/')
from foxi import foxi
# These imports aren't stricly necessary to run foxi but they will be useful in our examples.
import numpy as np
from scipy.stats import mu... |
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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: Train
Step2: Predict
Step4: Analyze
| <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import re
import numpy as np
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
%matplotlib inline
#%qtconsole
!rm train_ect.vw.cache
!rm mnist_train_ect.model
!vw -d data/mnist_train.vw -b 19 --ect 10 -f mnist_train_ect.model ... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: You can explore the files if you'd like, but we're going to get the ones from convote_v1.1/data_stage_one/development_set/. It's a bunch of text... | <ASSISTANT_TASK:>
Python Code:
# If you'd like to download it through the command line...
!curl -O http://www.cs.cornell.edu/home/llee/data/convote/convote_v1.1.tar.gz
# And then extract it through the command line...
!tar -zxf convote_v1.1.tar.gz
# glob finds files matching a certain filename pattern
import glob
# Gi... |
<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: 합성곱 신경망
Step2: MNIST 데이터셋 다운로드하고 준비하기
Step3: 합성곱 층 만들기
Step4: 지금까지 모델의 구조를 출력해 보죠.
Step5: 위에서 Conv2D와 MaxPooling2D 층의 출력은 (높이, 너비, 채널) 크기의 3... | <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: Loading the Books Dataset
Step2: Some Books don't have unique ISBN, creating a 1
Step3: Data Preparation/ Cleaning <br>
Step4: Sampling <br>
... | <ASSISTANT_TASK:>
Python Code:
ratings = pd.read_csv('../raw-data/BX-Book-Ratings.csv', encoding='iso-8859-1', sep = ';')
ratings.columns = ['user_id', 'isbn', 'book_rating']
print(ratings.dtypes)
print()
print(ratings.head())
print()
print("Data Points :", ratings.shape[0])
books = pd.read_csv('../raw-data/BX-Books.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: When read() receives no data from the socket, it interprets the read event as the other side of the connection being closed instead of sending d... | <ASSISTANT_TASK:>
Python Code:
# %load selectors_echo_server.py
import selectors
import socket
mysel = selectors.DefaultSelector()
keep_running = True
def read(connection, mask):
"Callback for read events"
global keep_running
client_address = connection.getpeername()
print('read({})'.format(client_addre... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: A simple function
Step2: Using header files
Step3: Notes
Step4: A reusable Makefile
Step5: Linking to a library
Step6: Arrays, pointers and... | <ASSISTANT_TASK:>
Python Code:
%mkdir hello
%cd hello
%%file hello.cpp
#include <iostream>
using std::cout;
using std::endl;
int main() {
cout << "Hello, world" << endl;
}
! g++ hello.cpp -o hello
! ./hello
%cd ..
%mkdir add1
%cd add1
%%file add.cpp
#include <iostream>
using std::cout;
using std::... |
<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: $\langle \rho_{diffuse, cosmic}\rangle$
Step2: $\langle n_{e,cosmic}\rangle$
Step3: $\langle DM_{cosmic}\rangle$
Step4: $\langle DM_{halos}\r... | <ASSISTANT_TASK:>
Python Code:
# imports
from importlib import reload
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline as IUS
from astropy import units as u
from frb.halos import ModifiedNFW
from frb import halos as frb_halos
from frb import igm as frb_igm
from frb.figures import utils as f... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Import TensorFlow and enable eager execution
Step2: Load the dataset
Step3: Use tf.data to create batches and shuffle the dataset
Step4: Writ... | <ASSISTANT_TASK:>
Python Code:
# to generate gifs
!pip install imageio
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 os
import time
import numpy as np
import glob
import matplotlib.pyplo... |
<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: Emulate the Distribution
Step2: The diagram below is what our volume's max-intensity projection would look like if it were perfectly uniform. Q... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy
import csv
data = open('../data/data.csv', 'r').readlines()
fieldnames = ['x', 'y', 'z', 'unmasked', 'synapses']
reader = csv.reader(data)
reader.next()
rows = [[int(col) for col in row] for row in reader]
sorted_x = sor... |
<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: Упражнение
Step3: Что такое потоки?
Step4: Упражнение
Step5: Ipyparallel
Step6: Примитивы синхронизации - мьютекс
| <ASSISTANT_TASK:>
Python Code:
a = 1
b = 3
a + b
a.__add__(b)
type(a)
isinstance(a, int)
class Animal(object):
mammal = True # class variable
def __init__(self, name, voice, color="black"):
self.name = name
self.__voice = voice # "приватный" или "защищенный" атрибут
self._col... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: <u>Do preprocessing</u>
Step2: Tokenize and load the corpus data
Step3: Create a Hash Table for Probable words for Trigram sentences
Step4: S... | <ASSISTANT_TASK:>
Python Code:
from nltk.util import ngrams
from collections import defaultdict
from collections import OrderedDict
import string
import time
import gc
start_time = time.time()
#returns: string
#arg: string
#remove punctuations and make the string lowercase
def removePunctuations(sen):
#split the 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: Linear Gaussian Models - The Process
| <ASSISTANT_TASK:>
Python Code:
# from pgmpy.factors.continuous import LinearGaussianCPD
import sys
import numpy as np
import pgmpy
sys.path.insert(0, "../pgmpy/")
from pgmpy.factors.continuous import LinearGaussianCPD
mu = np.array([7, 13])
sigma = np.array([[4 , 3],
[3 , 6]])
cpd = LinearGaussianCPD... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Define the flash LED task
Step2: Create the task
Step3: Monitoring the CPU Usage
Step4: Run the event loop
Step5: Clean up
Step6: Now if we... | <ASSISTANT_TASK:>
Python Code:
from pynq import Overlay, PL
from pynq.board import LED, Switch, Button
Overlay('base.bit').download()
buttons = [Button(i) for i in range(4)]
leds = [LED(i) for i in range(4)]
switches = [Switch(i) for i in range(2)]
import asyncio
@asyncio.coroutine
def flash_led(num):
while True:
... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: From 1D to 2D acoustic finite difference modelling
Step2: Comparison of 2D finite difference with analytical solution
| <ASSISTANT_TASK:>
Python Code:
# Execute this cell to load the notebook's style sheet, then ignore it
from IPython.core.display import HTML
css_file = '../style/custom.css'
HTML(open(css_file, "r").read())
# Import Libraries
# ----------------
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from ... |
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