Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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3,000 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import os
import urllib
dataset = 'mnist.pkl.gz'
def reporthook(a,b,c):
print "\rdownloading: %5.1f%%"%(a*b*100.0/c),
if not os.path.isfile(dataset):
origin = "https://github.com/mnielsen/neural-networks-and-deep-learning/raw/master/data/mnist.pkl.gz"
... | <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: 下載 mnist 資料
Step2: 載入訓練資料 train_set 和測試資料 test_set
Step3: 查看 mnist 資料的概況，用 .shape 看 np.array 的形狀
Step4: 資料的第一部份，每一筆都是一個 28x28 的圖片(28*28=784... |
3,001 | <ASSISTANT_TASK:>
Python Code:
titanic_data = titanic.drop(['PassengerId','Name','Ticket'],1)
titanic_data.head()
sb.boxplot(x='Pclass',y='Age',data=titanic_data)
def age_approx(cols):
age = cols[0]
pclass = cols[1]
if pd.isnull(age):
if pclass == 1:
return 37
elif pclass == 2:... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Now need to take care of the missing data for Age variable. Need to approximate- one way, to take mean age for all the missing values.
Step2: I... |
3,002 | <ASSISTANT_TASK:>
Python Code:
import os as os
import pandas as pd
import numpy as np
from scipy import stats, integrate
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
from statsmodels.distributions.empirical_distribution import ECDF
import datetime as dt
plt.style.use('seaborn-whitegrid... | <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: Use the bash =)
Step2: So parsing does not work, do it manually
Step3: Some statistics about the payment.
Step4: So thats the statistic about... |
3,003 | <ASSISTANT_TASK:>
Python Code:
#This guided coding excercise requires associated .csv files: CE1.csv, CH1.csv, CP1.csv, Arnold1.csv, Bruce1.csv, and Tom1.csv
#make sure you have these supplemental materials ready to go in your active directory before proceeding
#Let's start coding! We first need to make sure our prelim... | <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: Scene 2
Step2: Chris Hemsworth
Step3: Our data looks good! The axes are a little strange, but we just want to make sure we have data we can wo... |
3,004 | <ASSISTANT_TASK:>
Python Code:
N = 10000
x = np.random.normal(0, np.pi, N)
y = np.sin(x) + np.random.normal(0, 0.2, N)
p = figure(webgl=True)
p.scatter(x, y, alpha=0.1)
show(p)
!conda list | egrep "jupyter|notebook"
p = figure(plot_height=200, sizing_mode='scale_width')
p.scatter(x, y, alpha=0.1)
show(p)
N = 4000
x = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Responsive in notebook
|
3,005 | <ASSISTANT_TASK:>
Python Code:
# col() selects columns from a data frame, year() works on dates, and udf() creates user
# defined functions
from pyspark.sql.functions import col, year, udf
# Plotting library and configuration to show graphs in the notebook
import matplotlib.pyplot as plt
%matplotlib inline
df = sqlCon... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Loading the data set
Step2: Year collected by continent
Step4: There are a lot of things that are not continents there! While iDigBio cleans s... |
3,006 | <ASSISTANT_TASK:>
Python Code:
#general imports
import matplotlib.pyplot as plt
import pygslib
import numpy as np
#make the plots inline
%matplotlib inline
#get the data in gslib format into a pandas Dataframe
mydata= pygslib.gslib.read_gslib_file('../datasets/cluster.dat')
# This is a 2D file, in this GSLIB ve... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Getting the data ready for work
Step2: Testing histplot
|
3,007 | <ASSISTANT_TASK:>
Python Code:
df = pd.read_csv('data/apib12tx.csv')
df.describe()
df.corr()
df.plot(kind="scatter", x="MEALS", y="API12B")
df.plot(kind="scatter", x="AVG_ED", y="API12B")
data = np.asarray(df[['API12B','MEALS']])
x, y = data[:, 1:], data[:, 0]
lr = LinearRegression()
lr.fit(x, y)
# plot the linear... | <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: Checking out correlations
Step2: The percentage of students enrolled in free/reduced-price lunch programs is often used as a proxy for poverty.... |
3,008 | <ASSISTANT_TASK:>
Python Code:
# 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 writing, sof... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Getting started
Step2: Authenticate your GCP account
Step3: ML Workflow using a BigQuery model
Step4: Define Constants
Step6: Unused Feature... |
3,009 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.DataFrame({'key1': ['a', 'a', 'b', 'b', 'a', 'c'],
'key2': ['one', 'two', 'one', 'two', 'one', 'two']})
def g(df):
return df.groupby('key1')['key2'].apply(lambda x: (x=='one').sum()).reset_index(name='count')
result = g(df.copy())
<END_TA... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
3,010 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier, export_graphviz
from IPython.display import Image
from sklearn.externals.six import StringIO
from sklearn.cross_validation import train_test_split
import matplotlib.pyplot as plt
%matplotlib inline
RAN... | <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: Import Data
Step2: Column Meanings
Step3: We firstly category Sun_hours into three levels
Step4: Preprocessing (Handling Missing Values)
Step... |
3,011 | <ASSISTANT_TASK:>
Python Code:
!pip install modin[all]
import modin.pandas as pd
import pandas
#############################################
### For the purpose of timing comparisons ###
#############################################
import time
import ray
ray.init()
from IPython.display import Markdown, display
def p... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: For further instructions on how to install Modin with conda or for specific platforms or engines, see our detailed installation guide.
Step2: D... |
3,012 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
import numpy as np
np.set_printoptions(suppress=True)
sess = tf.InteractiveSession()
# Imports for visualization
import PIL.Image
from cStringIO import StringIO
from IPython.display import clear_output, Image, display
import scipy.ndimage as nd
import scipy.signal... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Load image
Step11: We need to find the chessboard squares within the image (assuming images will vary, boards will vary in color, etc. between ... |
3,013 | <ASSISTANT_TASK:>
Python Code:
# Initialize PYTHONPATH for pyopencga
import sys
import os
from pprint import pprint
cwd = os.getcwd()
print("current_dir: ...."+cwd[-10:])
base_modules_dir = os.path.dirname(cwd)
print("base_modules_dir: ...."+base_modules_dir[-10:])
sys.path.append(base_modules_dir)
from pyopencga.openc... | <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: Setting credentials for LogIn
Step2: Creating ConfigClient for server connection configuration
Step3: LogIn with user credentials
|
3,014 | <ASSISTANT_TASK:>
Python Code:
def generate_autocorrelated_data(theta, mu, sigma, N):
X = np.zeros((N, 1))
for t in range(1, N):
X[t] = theta * X[t-1] + np.random.normal(mu, sigma)
return X
def newey_west_SE(data):
ind = range(0, len(data))
ind = sm.add_constant(ind)
model = regression.l... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Data
Step2: Exercise 1
Step3: b. Standard Deviation
Step4: c. Standard Error
Step5: d. Confidence Intervals
Step6: Exercise 2
Step7: Exerc... |
3,015 | <ASSISTANT_TASK:>
Python Code:
first_digit(100)
first_digit(399)
import random
def do_drawing(bucket_size, runs):
digits = [first_digit(random.randint(1,bucket_size)) for x in range(runs)]
return digits
import collections
counters=[(top_end, collections.Counter(do_drawing(top_end, 100))) for top_end in range(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Now, we're going to simulate picking numbers out of a hat, doing it 'runs' times. The bucket_size is the number of nubmer in the hat.
Step2: No... |
3,016 | <ASSISTANT_TASK:>
Python Code:
# set up imports
import numpy
import statsmodels.nonparametric.smoothers_lowess
import matplotlib.pyplot as plt
from scipy.optimize import minimize
%matplotlib inline
# softmax response function
def softmax(q,temp):
p=numpy.exp(q[0]/temp)/(numpy.exp(q[0]/temp)+numpy.exp(q[1]/temp... | <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: First, we need to generate some data.
Step2: Now, we want to fit a model to the behavior above. It is challenging to estimate both the learnin... |
3,017 | <ASSISTANT_TASK:>
Python Code:
# Create a string
my_str = 'This is my string'
# Split it...
my_str_split = my_str.split()
print(my_str_split)
# ... and restore (join) it again
my_str_joined = ' '.join(my_str_split) # ' ' means join with space
print(my_str_joined)
# Find first occurence of word containing 'str'
print(my... | <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: Count words in Hamlet
Step2: Use collections to do the same thing
|
3,018 | <ASSISTANT_TASK:>
Python Code:
import em1d
import numpy as np
nx = 120
box = 4 * np.pi
dt = 0.1
tmax = 50.0
ndump = 10
ppc = 500
ufl = [0.4, 0.0, 0.0]
uth = [0.001,0.001,0.001]
right = em1d.Species( "right", -1.0, ppc, ufl = ufl, uth = uth )
ufl[0] = -ufl[0]
left = em1d.Species( "left", -1.0, ppc, ufl = ufl, uth... | <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: Initializing a ZPIC simulation requires setting the simulation box and timestep
Step2: Next we need to describe the particle species in the sim... |
3,019 | <ASSISTANT_TASK:>
Python Code:
%config InlineBackend.figure_format = 'retina'
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set_style("white")
import util
df = util.load_burritos()
N = df.shape[0]
m_corr = ['Google','Yelp','Hunge... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load data
Step2: Correlation matrix
Step3: Correlation
Step4: Positive correlation
Step5: Positive correlation
Step6: Positive correlation
... |
3,020 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne import io
from mne.datasets import sample
from mne.cov import compute_covariance
print(__doc__)
data_path = samp... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Set parameters
Step2: Compute covariance using automated regularization
Step3: Show whitening
|
3,021 | <ASSISTANT_TASK:>
Python Code:
report_files = ["/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing6_200_512_04drb/encdec_noing6_200_512_04drb.json", "/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing10_200_512_04drb/encdec_noing10_200_512_04drb.json", "/Users/bking/IdeaP... | <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: Perplexity on Each Dataset
Step2: Loss vs. Epoch
Step3: Perplexity vs. Epoch
Step4: Generations
Step5: BLEU Analysis
Step6: N-pairs BLEU An... |
3,022 | <ASSISTANT_TASK:>
Python Code:
PROJECT_ID_BILLING = "" # Set the project ID
! gcloud config set project $PROJECT_ID_BILLING
import sys
# If you are running this notebook in Colab, run this cell and follow the
# instructions to authenticate your GCP account. This provides access to your
# Cloud Storage bucket and lets ... | <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: Authenticate your GCP account
Step2: Create a BigQuery dataset
Step3: Validate that your dataset created successfully (this will throw an erro... |
3,023 | <ASSISTANT_TASK:>
Python Code:
# Load CSV into a dataframe named nba
# Print the number of rows and columns in the dataframe
# Print the first row of data
# Print the mean of each column
%matplotlib inline
# Use seaborn or pandas to plot the scatter matrix
# Use a clustering model like K-means to cluster the play... | <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: Remember to import the pandas library to get access to Dataframes. Dataframes are two-dimensional arrays (matrices) where each column can be of ... |
3,024 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True, reshape=False)
DO NOT MODIFY THIS CELL
def fully_connected(prev_layer, num_units):
Create a fully connectd layer with the given layer... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step3: Batch Normalization using tf.layers.batch_normalization<a id="example_1"></a>
Step6: We'll use the following function to create convolutional l... |
3,025 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from qutip import *
from qutip.expect import expect_rho_vec
from matplotlib import rcParams
rcParams['font.family'] = 'STIXGeneral'
rcParams['mathtext.fontset'] = 'stix'
rcParams['font.size'] = '14'
N = 15
w0 = 0.5 * 2... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Direct photo-detection
Step2: Highly efficient detection
Step3: Highly inefficient photon detection
Step4: Efficient homodyne detection
Step5... |
3,026 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import igraph
import timeit
import itertools
def enumerate_matrix(gmat, i):
return np.nonzero(gmat[i,:])[1].tolist()
def enumerate_adj_list(adj_list, i):
return adj_list[i]
def enumerate_edge_list(edge_list, i):
inds1 = np.where(edge_list[:,0] == i)[0]
... | <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, define a function that returns the index numbers of the neighbors of a vertex i, when the
Step2: Define a function that enumerates the ne... |
3,027 | <ASSISTANT_TASK:>
Python Code:
from theano import function, config, shared, sandbox
import theano.tensor as T
import numpy
import time
vlen = 10 * 30 * 768 # 10 x #cores x # threads per core
iters = 1000
rng = numpy.random.RandomState(22)
x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
f = function([], T.exp(... | <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: Yup, it looks like it is not using the GPU
Step2: I do, and the solution says it has something to do with my path not being set properly. Lets... |
3,028 | <ASSISTANT_TASK:>
Python Code:
Ax = [-1, 3, -4, 5, 1, -6, 2, 1]
def solution_1(A):
addition_list = list()
list_index = 1
addition_list.append(A[0])
try:
if len(A) >= 0 and len(A) <= 100000:
for i, int_in_arr in enumerate(A):
# print i, " ", int_in_arr
... | <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: V- 1 - used numpy to sum soon realized numpy does not work on codality. I have usually required more time working on solutions when solving some... |
3,029 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import graphlab
products = graphlab.SFrame('amazon_baby_subset.gl/')
products.head()
# The same feature processing (same as the previous assignments)
# ---------------------------------------------------------------
import json
with open('important_words.... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load and process review dataset
Step2: Just like we did previously, we will work with a hand-curated list of important words extracted from the... |
3,030 | <ASSISTANT_TASK:>
Python Code:
# import packages
import numpy as np
import matplotlib.pyplot as plt
from reg_utils import sigmoid, relu, plot_decision_boundary, initialize_parameters, load_2D_dataset, predict_dec
from reg_utils import compute_cost, predict, forward_propagation, backward_propagation, update_parameters
i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Problem Statement
Step3: Each dot corresponds to a position on the football field where a football player has hit the ball with his/her head af... |
3,031 | <ASSISTANT_TASK:>
Python Code::
from numpy import array
from pickle import load
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from keras.utils import plot_model
from keras.models import Model
from keras.layers import Input
fr... | <SYSTEM_TASK:>
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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3,032 | <ASSISTANT_TASK:>
Python Code:
index.output_prefix
index.save('prakhar')
index[vec_lsi]
index.output_prefix
index2 = similarities.Similarity.load('/home/prakhar/Documents/test/shard/prakhar')
index2.output_prefix
index2[vec_lsi]
#index2.output_prefix
#index2.output_prefix = '/home/prakhar/Documents/gentestOLD/prakhar... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Now, user needs to provide only the file name, as file will now be saved inside "shard" directory.
Step2: Now, for portability the user needs t... |
3,033 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
x = [1,2,3]
y = [4,5,6]
x + y
B = np.array([[1,2,3], [4,5,6]]) # habiendo corrido import numpy as np
B + 2*B # Python sabe sumar y multiplicar arrays como algebra lineal
np.matmul(B.transpose(), B) # B^t*B
B[1,1]
B[1,:]
B[:,2]
B[0:2,0:2]
B.shape
vec = np.array... | <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: Lo que el codigo anterior hace es asociar al nombre np todas las herramientas de la libreria numpy. Ahora podremos llamar funciones de numpy com... |
3,034 | <ASSISTANT_TASK:>
Python Code:
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = ""
#os.environ['THEANO_FLAGS'] = "device=gpu2"
from keras.models import load_model
from keras.models import Sequential
from keras.layers.core import Dense, Dropout
from keras.o... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Data preparation (keras.dataset)
Step2: Training
Step3: Plotting Network Performance Trend
|
3,035 | <ASSISTANT_TASK:>
Python Code:
import math
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow_datasets as tfds
from tensorflow import keras
from tensorflow.keras import layers
# Dataset hyperparameters
unlabeled_dataset_size = 100000
labeled_dataset_size = 5000
image_size = 96
image_channels = 3... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Hyperparameter setup
Step2: Dataset
Step3: Image augmentations
Step4: Encoder architecture
Step5: Supervised baseline model
Step6: Self-sup... |
3,036 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bnu', 'sandbox-1', 'toplevel')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "em... | <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: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
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Python Code:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
matplotlib.rc('savefig', dpi=120)
import warnings
warnings.simplefilter("ignore", Warning)
from matplotlib import dates
import sunpy
sunpy.system_info()
from sunpy.net import hek
client = hek.HEKClien... | <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: SunPy version (stable) 0.5
Step2: We can find out when this event occured
Step3: and where it occurred
Step4: Lightcurves!
Step5: The data i... |
3,038 | <ASSISTANT_TASK:>
Python Code:
import libpysal as ps
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import pandas as pd
import geopandas as gpd
from giddy.markov import FullRank_Markov
income_table = pd.read_csv(ps.examples.get_path("usjoin.csv"))
income_table.head()
pci = ... | <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: Full Rank Markov
Step2: Full rank Markov transition probability matrix
Step3: Full rank first mean passage times
Step4: Geographic Rank Marko... |
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Python Code:
from IPython.display import Image
Image("Malaga_map.jpg")
Image("workflow.jpg")
#Import all necesary modules as follows:
#import flexible container object, designed to store hierarchical data structures in memory
import xml.etree.cElementTree as ET
#import function to supply missing valu... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: <body> In order to complete the project the following steps shown in the diagram below must be followed <body>
Step3: 1. Audit data
Step4: 2. ... |
3,040 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 变量简介
Step2: 创建变量
Step3: 变量与张量的定义方式和操作行为都十分相似，实际上，它们都是 tf.Tensor 支持的一种数据结构。与张量类似，变量也有 dtype 和形状，并且可以导出至 NumPy。
Step4: 大部分张量运算在变量上也可以按预期运行，不过变量... |
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Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import fourstate
import itertools
import networkx as nx
import numpy as np
import operator
bhs = fourstate.FourState()
fig, ax = plt.subplots()
ax.bar([0.5,1.5,2.5], -1./bhs.evals[1:], width=1)
ax.set_xlabel(r'Eigenvalue', fontsize=16)
a... | <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: By doing this we get a few variables initialized. First, a symmetric transition count matrix, $\mathbf{N}$, where we see that the most frequent ... |
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Python Code:
salmon = pd.read_table("../data/salmon.dat", delim_whitespace=True, index_col=0)
plt.scatter(x=salmon.spawners, y=salmon.recruits)
fig, axes = plt.subplots(1, 2, figsize=(14,6))
xvals = np.arange(salmon.spawners.min(), salmon.spawners.max())
fit1 = np.polyfit(salmon.spawners, salmon.recr... | <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: On the one extreme, a linear relationship is underfit; on the other, we see that including a very large number of polynomial terms is clearly ov... |
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Python Code:
import pandas as pd
import numpy as np
from sklearn.model_selection import cross_val_score
df = pd.read_csv('../scikit/tweets.csv')
target = df['is_there_an_emotion_directed_at_a_brand_or_product']
text = df['tweet_text']
# We need to remove the empty rows from the text before we pass int... | <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: This SVM is 67% accurate.
|
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Python Code:
# boilerplate code
import os
from cStringIO import StringIO
import numpy as np
from functools import partial
import PIL.Image
from IPython.display import clear_output, Image, display, HTML
import tensorflow as tf
#!wget https://storage.googleapis.com/download.tensorflow.org/models/incept... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <a id='loading'></a>
Step6: To take a glimpse into the kinds of patterns that the network learned to recognize, we will try to generate images ... |
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Python Code:
%matplotlib inline
from keras.models import model_from_json
from keras.optimizers import SGD
from os import path
from train import infer_sizes
import models
cache_dir = '../cache/mpii-cooking/' # Change me!
orig_path = path.join(cache_dir, 'keras-checkpoints/checkpoints/model-iter-16640... | <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: Configuration and metadata (layer size) gathering
Step2: Load a model
Step3: Upgrade the model
Step4: Save a description of the model and its... |
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Python Code:
import json
# ukazkova data
message = [
{"time": 123, "value": 5},
{"time": 124, "value": 6},
{"status": "ok", "finish": [True, False, False]},
]
# zabalit zpravu
js_message = json.dumps(message)
# show result
print(type(js_message))
print(js_message)
# unpack message
messag... | <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: Následuje příklad, ve kterém se vezme JSON text z předchozího příkladu a zpátku se z něj složí objekt.
Step2: Ve formátu json můžou být libovol... |
3,047 | <ASSISTANT_TASK:>
Python Code:
import os,sys
import numpy
%matplotlib inline
import matplotlib.pyplot as plt
import statsmodels.tsa.stattools
from dcm_sim import sim_dcm_dataset
sys.path.insert(0,'../')
from utils.graph_utils import show_graph_from_adjmtx,show_graph_from_pattern
# first we simulate some data using our ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Now compute Granger causality across all pairs of timeseries
|
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Python Code:
import numpy as np
import matplotlib.pyplot as plt
from line_profiler import LineProfiler
from sklearn.metrics.pairwise import pairwise_distances
import seaborn as sns
from sklearn import datasets
from sklearn.base import ClassifierMixin
from sklearn.datasets import fetch_mldata
from skle... | <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: IRIS
Step2: MNIST
Step3: Задание 5
|
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Python Code:
import sisl
import sisl.viz
# First, we create the geometry
BN = sisl.geom.graphene(atoms=["B", "N"])
# Create a hamiltonian with different on-site terms
H = sisl.Hamiltonian(BN)
H[0, 0] = 2
H[1, 1] = -2
H[0, 1] = -2.7
H[1, 0] = -2.7
H[0, 1, (-1, 0)] = -2.7
H[0, 1, (0, -1)] = -2.7
H[1, 0... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: For this notebook we will create a toy "Boron nitride" tight binding
Step2: Note that we could have obtained this hamiltonian from any other so... |
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Python Code:
import numpy as np
lst = [10, 20, 30, 40]
arr = np.array([10, 20, 30, 40])
print(lst)
print(arr)
print(lst[0], arr[0])
print(lst[-1], arr[-1])
print(lst[2:], arr[2:])
lst[-1] = 'a string inside a list'
lst
arr[-1] = 'a string inside an array'
print('Data type :', arr.d... | <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: NumPy, at its core, provides a powerful array object. Let's start by exploring how the NumPy array differs from a Python list.
Step2: Elemen... |
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Python Code:
import numpy as np
import tensorflow as tf
from collections import Counter
with open('../sentiment_network/reviews.txt', 'r') as f:
reviews = f.read()
with open('../sentiment_network/labels.txt', 'r') as f:
labels = f.read()
reviews[:2000]
from string import punctuation
all_text ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Data preprocessing
Step2: Encoding the words
Step3: Encoding the labels
Step4: If you built labels correctly, you should see the next output.... |
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Python Code:
# This is a Python comment
# the next line is a line of Python code
print("Hello World!")
# These two lines turn on inline plotting
%matplotlib inline
import matplotlib.pyplot as plt
plt.plot([1,2,3])
a = 1
a + 1
b = 2.1
b + 1
a + b
type(a + b)
c = 1.5 + 0.5j # complex numbers
print... | <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: Cells can also be used to create textual materials using the markup language Markdown.
Step2: <img class="logo" src="images/python-logo.png" ... |
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Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import astropy.io.fits as fits
plt.style.use('ggplot')
plt.rc('axes', grid=False) # turn off the background grid for images
data_file = "./MyData/bsg01.fits"
my_fits_file = fits.open(data_file)
my_fits_file.info()
im... | <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: FITS files consist of at least two parts - Header and Data
Step2: FITS format preserves the full dynamic range of data
Step3: You can use mask... |
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Python Code:
def equvInverse(arr , N , P ) :
cntElem = 0
for i in range(0 , N ) :
if(( arr[i ] * arr[i ] ) % P == 1 ) :
cntElem = cntElem + 1
return cntElem
if __name__== "__main __":
arr =[1 , 6 , 4 , 5 ]
N = len(arr )
P = 7
print(equvInverse(arr , N , P ) )
<END_TAS... | <SYSTEM_TASK:>
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:
class newNode :
def __init__(self , data ) :
self . data = data
self . left = self . right = None
def deleteLeaves(root , x ) :
if(root == None ) :
return None
root . left = deleteLeaves(root . left , x )
root . right = deleteLeaves(root . right , x )
if(root .... | <SYSTEM_TASK:>
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:
print("Hello man")
def fibo(n):
if n == 0:
return 0
elif n == 1:
return 1
return fibo(n-1) + fibo(n-2)
%timeit fibo(20)
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 1, ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Example code block
Step2: Note
Step3: Debugging with %pdb
|
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Python Code:
import csv
def parse_registers(input_path):
registers = []
with open(input_path, 'rt') as f_input:
csv_reader = csv.reader(f_input, delimiter=' ')
for line in csv_reader:
registers.append((line[0], tuple(line[1:])))
return registers
from collection... | <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: Part 1
Step2: Solution
Step3: Part 2
Step4: Test
Step5: Solution
|
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Python Code:
import numpy as np
%pylab inline
# Load the data
# TODO
# Normalize the data
from sklearn import preprocessing
X = preprocessing.normalize(X)
# Set up a stratified 10-fold cross-validation
from sklearn import cross_validation
folds = cross_validation.StratifiedKFold(y, 10, shuffle=True)
d... | <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: 2016-11-04
Step2: 1. Decision trees
Step3: Question Compute the mean and standard deviation of the area under the ROC curve of these 5 trees. ... |
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Python Code:
# TEST
import numpy as np
import pandas as pd
import larch.numba as lx
from pytest import approx
import larch.numba as lx
# HIDDEN
df_ca = pd.read_csv("example-data/tiny_idca.csv")
cats = df_ca['altid'].astype(pd.CategoricalDtype(['Car', 'Bus', 'Walk'])).cat
df_ca['altnum'] = cats.codes ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: In this guide, we'll take a look at building a discrete choice model using Larch.
Step2: The basic structure of a choice model in Larch is cont... |
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Python Code:
import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
reviews = pd.read_csv('reviews.txt', header=None)
labels = pd.read_csv('labels.txt', header=None)
from collections import Counter
total_counts = Counter()
for _, r... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Preparing the data
Step2: Counting word frequency
Step3: Let's keep the first 10000 most frequent words. As Andrew noted, most of the words in... |
3,061 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from brainiak.reconstruct import iem as IEM
import matplotlib.pyplot as plt
import numpy.matlib as matlib
import scipy.signal
# Set up parameters
n_channels = 6
cos_exponent = 5
range_start = 0
range_stop = 360
feature_resolution = 360
iem_obj = IEM.InvertedEncoding1D(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: In this example, we will assume that the stimuli are patches of different motion directions. These stimuli span a 360-degree, circular feature s... |
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Python Code:
lva = v.model.node.storages.lva('IrrigationOnlyStorage')
lva
scaled_lva = lva * 2
scaled_lva
# v.model.node.storages.load_lva(scaled_lva) # Would load the same table into ALL storages
# v.model.node.storages.load_lva(scaled_lva,nodes=['StorageOnlyStorage','BothStorage']) # Will load i... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Specifying full supply and initial conditions
Step2: Releases
Step3: We can create each type of release mechanism.
|
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Python Code:
# Configure Jupyter so figures appear in the notebook
%matplotlib inline
# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'
# import functions from the modsim.py module
from modsim import *
radian = ... | <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: Unrolling
Step2: And a few more parameters in the Params object.
Step4: make_system computes rho_h, which we'll need to compute moment of iner... |
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Python Code:
import re
import numpy as np
import pandas as pd
import email
#Plotting stuff
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context('poster')
import hypergraph
import os
#for root, user, file in os.walk('/Users/jchealy/Downloads/maildir/'):
root = '/Use... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Read the data into hyperedges. We preserve order only in so far as the first element in each array is the sender. Email addresses may appear m... |
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Python Code:
# first, define our isnobal spatiotemporal parameters
isnobal_params = dict(
# generate a 10x8x(n_timesteps) grid for each variable
nlines=10, nsamps=8,
# with a resolution of 1.0m each; samp is north-south, so it's negative
dline=1.0, dsamp=-1.0,
# set base fake origi... | <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: Next
Step2: The standard name above refers to the CF Conventions standard name. By using this, other netCDF software tools can interpret the ti... |
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Python Code:
# The usual, we need to load some libraries
from SimPEG import Mesh, Utils, Maps, PF
from SimPEG import mkvc, Regularization, DataMisfit, Optimization, InvProblem, Directives,Inversion
from SimPEG.Utils import mkvc
from SimPEG.Utils.io_utils import download
import numpy as np
import scipy... | <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: Setup
Step2: Forward system
Step3: Inverse problem
Step4: View the inversion results
|
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Python Code:
%autosave 120
import numpy as np
np.random.seed(1337)
import datetime
import graphviz
from IPython.display import SVG
import keras
from keras import activations
from keras import backend as K
from keras.datasets import mnist
from keras.layers import (
concatenate,
... | <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: deep learning understanding
Step2: saliency
Step3: class saliency map extraction
Step4: class saliency map statistical uncertainties
Step5: ... |
3,068 | <ASSISTANT_TASK:>
Python Code:
import numba
import numpy as np
import numexpr as ne
import matplotlib.pyplot as plt
def metric_python(x, y):
standard Euclidean distance
ret = x-y
ret *= ret
return np.sqrt(ret).sum()
def inf_dist_python(x, Y):
inf distance between row x and array Y
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step4: En esta actividad implementaremos una conocida métrica para medir disimilitud entre conjuntos
Step8: Paso 2.
Step9: Paso 3.
|
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Python Code:
def find_passes(duration):
#instrument function calls
sat_azel.calls = 0
# orbital period in seconds
period = 24.0 * 60.0 * 60.0 / sat._n
# coarse steps to find the points near (enough) to elevation peaks
time_coarse = np.arange(0, INTERVAL_SECONDS, period/STEPS_PE... | <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: Find time to compute using pyephem.observer.next_pass()
Step2: Speedup factor and percentage reduction.
|
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Python Code:
%matplotlib inline
from SeisCL import SeisCL
import matplotlib.pyplot as plt
import numpy as np
seis = SeisCL()
# Constants for the modeling
seis.ND = 2
N = 200
seis.N = np.array([N, 2*N])
seis.dt = dt = 0.25e-03
seis.dh = dh = 2
seis.NT = NT = 1000
seis.seisout = 1
seis.f0 = 20
# Source... | <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: We first create a constant velocity model, with one source in the middle
Step2: To output a movie, we have to set the input 'movout' to a numbe... |
3,071 | <ASSISTANT_TASK:>
Python Code:
from skimage import data
color_image = data.chelsea()
print(color_image.shape)
plt.imshow(color_image);
red_channel = color_image[:, :, 0] # or color_image[..., 0]
plt.imshow(red_channel);
red_channel.shape
from skimage import io
color_image = io.imread('../images/balloon.jpg')
impor... | <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: Slicing and indexing
Step2: But when we plot the red channel...
Step3: Obviously that's not red at all. The reason is that there's nothing to ... |
3,072 | <ASSISTANT_TASK:>
Python Code:
Yummly API Example
"id": "Garlic-Cheese-Chicken-1041442",
"recipeName": "Garlic Cheese Chicken",
"ingredients": ["melted butter",
"garlic cloves",
"garlic powder",
"salt",
"corn flakes",
"shredded cheddar chee... | <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: Engine
|
3,073 | <ASSISTANT_TASK:>
Python Code:
#The Python Imaging Library (PIL)
from PIL import Image, ImageDraw
# Basic math and color tools
import math, colorsys, numpy
# Mathematical plotting
import matplotlib as mpl
from matplotlib import colors as mplcolors
import matplotlib.pyplot as plt
# Displaying real graphical images (pict... | <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: This sets up the colors we want in our fractal image.
Step2: Let's use over Mandelbrot test function to check some examples of "c"
Step3: 3. G... |
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Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'sandbox-3', 'seaice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
3,075 | <ASSISTANT_TASK:>
Python Code:
from price import *
import matplotlib.pyplot as plt
player1, player2 = MakePlayers(path='../code')
MakePrice1(player1, player2)
plt.legend();
class Pdf(object):
def Density(self, x):
raise UnimplementedMethodException()
def MakePmf(self, xs):
pmf = Pmf()
f... | <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: This shows the distribution of prices for these
Step2: Density takes a value, x, and returns the
Step3: __init__ takes mu and sigma, which are... |
3,076 | <ASSISTANT_TASK:>
Python Code:
cmr=CMR("../cmr.cfg")
results = cmr.searchGranule(entry_title='MODIS/Aqua Near Real Time (NRT) Thermal Anomalies/Fire 5-Min L2 Swath 1km (C005)',
temporal="2016-04-11T12:00:00Z,2016-04-11T13:00:00Z")
for res in results:
print(res.getDownloadUrl())
results = c... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Fires in Nepal
Step2: Further subset using bounding box
|
3,077 | <ASSISTANT_TASK:>
Python Code:
# prepare some imports
import numpy as np
from sklearn.datasets import fetch_mldata
import matplotlib.pyplot as plt
%matplotlib inline
# plot samples from MNIST
mnist = fetch_mldata('MNIST original')
for i in range(9):
plt.subplot(3, 3, i+1)
sample = mnist.data[20000+i]
sampl... | <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: Here are a few examples from the data set (samples $20000-20009$)
Step2: A common task then looks like this
Step3: Note that data matrices in ... |
3,078 | <ASSISTANT_TASK:>
Python Code:
# first, get some text:
import fileinput
try:
import ujson as json
except ImportError:
import json
documents = []
for line in fileinput.FileInput("example_tweets.json"):
documents.append(json.loads(line)["text"])
print("One document: \"{}\"".format(documents[0]))
from nltk.s... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 1) Document
Step2: 2) Tokenization
Step3: 3) Text corpus
Step4: 4) Stop words
Step5: 5) Vectorize
Step6: Bag of words
|
3,079 | <ASSISTANT_TASK:>
Python Code:
# Set up code checking
# This can take a few seconds
from learntools.core import binder
binder.bind(globals())
from learntools.feature_engineering.ex2 import *
import numpy as np
import pandas as pd
from sklearn import preprocessing, metrics
import lightgbm as lgb
clicks = pd.read_parque... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The next code cell repeats the work that you did in the previous exercise.
Step3: Next, we define a couple functions that you'll use to test th... |
3,080 | <ASSISTANT_TASK:>
Python Code:
import graphlab
from __future__ import division
import numpy as np
graphlab.canvas.set_target('ipynb')
products = graphlab.SFrame('amazon_baby.gl/')
def remove_punctuation(text):
import string
return text.translate(None, string.punctuation)
# Remove punctuation.
review_clean = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load amazon review dataset
Step2: Extract word counts and sentiments
Step3: Now, let's remember what the dataset looks like by taking a quick ... |
3,081 | <ASSISTANT_TASK:>
Python Code:
#
# Simple python program to calculate s as a function of t.
# Any line that begins with a '#' is a comment.
# Anything in a line after the '#' is a comment.
#
lam=0.01 # define some variables: lam, dt, s, s0 and t. Set initial values.
dt=1.0
s=s0=100.0
t=0.0
def f_s(s,t): ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Now lets add a plot. Turn on the "pylab" environment
Step2: Good! Next collect some lists of data (slist and tlist) and use the "plot" function... |
3,082 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np # math
import pandas as pd # manipulating data
import matplotlib.pyplot as plt # graphing
import os # useful for handling filenames etc.
from scipy.stats import pearsonr # calculat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Let's remove all the NaN values using the Pandas DataFrame.dropna function.
Step2: Now let's use the Pandas DataFrame.corr function to make a c... |
3,083 | <ASSISTANT_TASK:>
Python Code:
# Enter your username:
YOUR_GMAIL_ACCOUNT = '******' # Whatever is before @gmail.com in your email address
# Libraries for this section:
import os
import cv2
import pickle
import numpy as np
from sklearn import preprocessing
# Directories:
PREPROC_DIR = os.path.join('/home', YOUR_GMAIL_AC... | <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: Feature Engineering Functions
Step5: Harris Corner Detector Histograms
Step8: Building Feature Vectors
Step9: Standardize and save
|
3,084 | <ASSISTANT_TASK:>
Python Code:
remote_yaml = 'https://raw.githubusercontent.com/teamdigitale/api-starter-kit/master/openapi/simple.yaml.src'
render_markdown(f'''
[Swagger Editor]({oas_editor_url(remote_yaml)}) is a simple webapp
for editing OpenAPI 3 language specs.
''')
render_markdown(f'''
1- open [this incomplete O... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Start with Metadata
Step2: Custom fields
|
3,085 | <ASSISTANT_TASK:>
Python Code:
project = 'Input your PROJECT ID'
region = 'Input GCP region' # For example, 'us-central1'
output = 'Input your GCS bucket name' # No ending slash
!python3 -m pip install 'kfp>=0.1.31' --quiet
import kfp.deprecated.components as comp
dataflow_python_op = comp.load_component_from_url(
... | <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: Install Pipeline SDK
Step2: Load the component using KFP SDK
Step3: Use the wordcount python sample
Step4: Example pipeline that uses the com... |
3,086 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.random.seed(42)
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import tensorflow as tf
tf.set_random_seed(42)
xs = [0., 1., 2., 3., 4., 5., 6., 7.] # feature (independent variable)
ys = [-.82, -.94, -.12, .26, .39, .64, 1.02, 1.] # labels (de... | <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 very small data set
Step2: Define variables -- the model parameters we'll learn -- and initialize them with "random" values
Step3: On... |
3,087 | <ASSISTANT_TASK:>
Python Code:
%%bash
date
system_profiler SPSoftwareDataType
bsmaploc="/Users/Shared/Apps/bsmap-2.74/"
cd /Volumes/web/halfshell/working-directory/
!ls -lh
mkdir 16-10-29
cd 16-10-29
# Genome
cd ../data
!curl -O http://owl.fish.washington.edu/halfshell/working-directory/16-10-24/Ostrea_lurida-Scaff-... | <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: <img src="http
Step2: <img src="http
|
3,088 | <ASSISTANT_TASK:>
Python Code:
import importlib
import os
import sys
def is_interactive():
import __main__ as main
return not hasattr(main, '__file__')
# defaults
shell_mode = not is_interactive()
plot_graphs = importlib.util.find_spec("matplotlib") is not None and not shell_mode
# Matplotlib
if (plot_graphs):... | <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: TB Hamiltonian
Step2: Kwant routines
Step3: Apply parameters and extract the Hamiltonian
Step4: Dump Hamiltonian ordered along the position o... |
3,089 | <ASSISTANT_TASK:>
Python Code:
from pyquil import Program, get_qc
qc = get_qc('Aspen-8')
cals = qc.compiler.calibration_program
from pyquil.quilatom import Qubit, Frame
from pyquil.quilbase import Pulse, Capture, DefMeasureCalibration
qubit = Qubit(0)
measure_defn = next(defn for defn in cals.calibrations
... | <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: Peeking at a MEASURE calibration
Step2: There are a few things note about the above
Step3: An almost-trivial example
Step4: Raw capture resul... |
3,090 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
spark_home = os.environ['SPARK_HOME'] = '/Users/ozimmer/GoogleDrive/berkeley/w261/spark-2.0.0-bin-hadoop2.6'
if not spark_home:
raise ValueError('SPARK_HOME enviroment variable is not set')
sys.path.insert(0,os.path.join(spark_home,'python'))
sys.path.insert(0,os.... | <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: Non-RDD Example
Step2: DataProc - submit a job
|
3,091 | <ASSISTANT_TASK:>
Python Code:
!python --version
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(rig... | <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: Basics of Python
Step2: Basic data types
Step3: Note that unlike many languages, Python does not have unary increment (x++) or decrement (x--)... |
3,092 | <ASSISTANT_TASK:>
Python Code:
# execute example code here
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
iris = datasets.load_iris()
RFclf = RandomForestClassifier().fit(iris.data, iris.target)
# complete
# complete
print(np.shape(# complete
print(# complete
print( # complete
pri... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Generally speaking, the procedure for scikit-learn is uniform across all machine-learning algorithms. Models are accessed via the various module... |
3,093 | <ASSISTANT_TASK:>
Python Code:
# Numpy handles matrix multiplication, see http://www.numpy.org/
import numpy as np
# PyPlot is a matlab like plotting framework, see https://matplotlib.org/api/pyplot_api.html
import matplotlib.pyplot as plt
# This line makes it easier to plot PyPlot graphs in Jupyter Notebooks
%matplotl... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The regressor class
Step2: Using the regressor
Step3: Revisiting the training process
Step4: Looking at the data we see that it is possible t... |
3,094 | <ASSISTANT_TASK:>
Python Code:
metadata_tb = Table.read_table('data/poemeta.csv', keep_default_na=False)
metadata_tb.show(5)
reception_mask = (metadata_tb['recept']=='reviewed') + (metadata_tb['recept']=='random')
clf_tb = metadata_tb.where(reception_mask)
clf_tb.show(5)
# Create list that will contain a series of di... | <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: We see above a recept column that has their reception status. We want to look at reviewed and random, just like Underwood and Sellers did
Step2:... |
3,095 | <ASSISTANT_TASK:>
Python Code:
import nltk
text = nltk.word_tokenize("And now for something completely different")
nltk.pos_tag(text)
nltk.tag.str2tuple('fly/NN')
# tagged_words() 是一個已經表示成tuple形態的資料
nltk.corpus.brown.tagged_words()
# 用參數 tagset='universal' 可以換成簡單的tag
nltk.corpus.brown.tagged_words(tagset='universal')
... | <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: 上面的範例中，CC是對等連接詞、RB是副詞、IN是介系詞、NN是名詞、JJ則是形容詞。如果想知道詳細的tag定義，可以用nltk.help.upenn_tagset('RB')來查詢。
Step2: corpus中也有tagged sentences
Step3: Mapping W... |
3,096 | <ASSISTANT_TASK:>
Python Code:
# restart your notebook if prompted on Colab
try:
import verta
except ImportError:
!pip install verta
HOST = "app.verta.ai"
PROJECT_NAME = "Census Income Classification"
EXPERIMENT_NAME = "Logistic Regression"
WORKSPACE = "Demos"
# import os
# os.environ['VERTA_EMAIL'] =
# os.en... | <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: This example features
Step2: Phase 1
Step3: Prepare hyperparameters
Step4: Instantiate client
Step5: Train models
Step6: Revisit Workflow
S... |
3,097 | <ASSISTANT_TASK:>
Python Code:
Make a request from the Forecast.io API for where you were born (or lived, or want to visit!)
import requests
!pip3 install requests
#new york
response = requests.get("https://api.forecast.io/forecast/94bc3fa3628bfad686b10e7054c67f71/40.7141667, -74.0063889")
data = response.json()
print(... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2) What's the current wind speed? How much warmer does it feel than it actually is?
Step2: 3) The first daily forecast is the forecast for toda... |
3,098 | <ASSISTANT_TASK:>
Python Code:
from bs4 import BeautifulSoup
from urllib.request import urlopen
html = urlopen("http://static.decontextualize.com/cats.html").read()
document = BeautifulSoup(html, "html.parser")
cafe_list = list()
cafe_table = document.find('table', {'class': 'cafe-list'})
tbody = cafe_table.find('tbod... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Let's tackle the list of cafes first. In the cell below, write some code that creates a list of dictionaries with information about each cafe, a... |
3,099 | <ASSISTANT_TASK:>
Python Code:
print(glob.glob('data/inflammation*.csv'))
# loop here
counter = 0
for filename in glob.glob('data/*.csv'):
#counter+=1
counter = counter + 1
print("number of files:", counter)
counter = 0
for filename in glob.glob('data/infl*.csv'):
#counter+=1
counter = counter + 1
prin... | <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: results in a list of strings, we can loop oer
Step2: We can ask Python to take different actions, depending on a condition, with an if statemen... |
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