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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: A NN plotter
Step2: Generate a Network
Step3: Visualize the network
Step4: Visualize the Output for a few examples
Step5: Training
Step6: B... | <ASSISTANT_TASK:>
Python Code:
from matplotlib.pylab import plt
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original')
step = 2
train_offset = 0
features = mnist['data'][train_offset:-1:step].copy()/255
target = mnist['target'][train_offset:-1:step].copy()
test_offset = 1
features_test = mnist... |
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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', 'ec-earth-consortium', 'ec-earth3-aerchem', 'land')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_con... |
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Description:
Step1: <h3> Connecting to S3 </h3>
Step2: <h4>Parsing tweets</h4>
Step3: If you already have a corpus and dictionary you can just load them.
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Python Code:
# some nlp tools
import spacy
import gensim
import pyLDAvis.gensim as gensimvis
import pyLDAvis
# tools to get data from s3
import boto3
from io import StringIO
import json
import requests
# would probably need these
import numpy as np
import pandas as pd
import itertools
nlp = spacy.load... |
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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: Building the model
Step2: Training
Step3: Plot results
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Python Code:
!wget -N https://s3.amazonaws.com/lasagne/recipes/datasets/mnist_cluttered_60x60_6distortions.npz
def load_data():
data = np.load(mnist_cluttered)
X_train, y_train = data['x_train'], np.argmax(data['y_train'], axis=-1)
X_valid, y_valid = data['x_valid'], np.argmax(data['y_vali... |
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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 Network
Step2: Explore Directions
Step3: Interactive
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Python Code:
is_stylegan_v1 = False
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import sys
import os
from datetime import datetime
from tqdm import tqdm
# ffmpeg installation location, for creating videos
plt.rcParams['animation.ffmpeg_path'] = str('/usr/bin/ffmpeg')
im... |
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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: Initialize the Clipper cluster
Step2: Define 'predict' function
Step3: Deploy Keras model and 'predict' function to the Clipper cluster
Step4:... | <ASSISTANT_TASK:>
Python Code:
from keras.applications.resnet50 import ResNet50
# https://keras.io/applications/#classify-imagenet-classes-with-resnet50
model = ResNet50(weights='imagenet')
from clipper_admin import ClipperConnection, DockerContainerManager
clipper_conn = ClipperConnection(DockerContainerManager())
cl... |
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Description:
Step1: For a particular parameter, the corresponding function of the given family is represented as heatmap of its argument, which illustrates
Step2:... | <ASSISTANT_TASK:>
Python Code:
import plotly.graph_objects as go
import numpy as np
Plotly version of the HSV colorscale, corresponding to S=1, V=1, where S is saturation and V is the value.
pl_hsv = [[0.0, 'rgb(0, 255, 255)'],
[0.0833, 'rgb(0, 127, 255)'],
[0.1667, 'rgb(0, 0, 255)'],
[0.25, 'rgb(127, 0, 255)'],
[0... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Sometimes you need the index of an element and the element itself while you iterate through a list. This can be achived with the enumerate funct... | <ASSISTANT_TASK:>
Python Code:
greek = ["Alpha", "Beta", "Gamma", "Delta"]
for element in greek:
print(element)
for i, e in enumerate(greek):
print(e + " is at index: " + str(i))
list_of_tuples = [(1,2,3),(4,5,6), (7,8,9)]
for (a, b, c) in list_of_tuples:
print(a + b + c)
for e1, e2 in zip(greek, greek):... |
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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('../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 = ''.join([c for c in reviews if... |
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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 construct the class containing the initial conditions of the problem
Step2: New legislation changes $\lambda$ to $0.2$
Step3: Now plot sto... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from quantecon.models import LakeModel
alpha = 0.012
lamb = 0.2486
b = 0.001808
d = 0.0008333
g = b-d
N0 = 100.
e0 = 0.92
u0 = 1-e0
T = 50
LM0 = LakeModel(lamb,alpha,b,d)
x0 = LM0.find_steady_state()# initial condition... |
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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 next thing we'll need is some data. To make for an illustrative example we'll need the data size to be fairly small so we can see what is go... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.datasets as data
%matplotlib inline
sns.set_context('poster')
sns.set_style('white')
sns.set_color_codes()
plot_kwds = {'alpha' : 0.5, 's' : 80, 'linewidths':0}
moons, _ = data.make_moons(n_samples=50... |
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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 CoreData
Step2: Compute Functional Connectivity
Step3: Process Navon
Step4: Process Stroop
Step5: Generate Population Configuration Mat... | <ASSISTANT_TASK:>
Python Code:
try:
%load_ext autoreload
%autoreload 2
%reset
except:
print 'NOT IPYTHON'
from __future__ import division
import os
import sys
import glob
import numpy as np
import pandas as pd
import seaborn as sns
import scipy.stats as stats
import statsmodels.api as sm
import scipy.io... |
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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 possessives that may be acceptable behavior, but how about contractions like “didn’t” or “A’dam” (short for “Amsterdam”)? If the default tok... | <ASSISTANT_TASK:>
Python Code:
from collatex import *
collation = Collation()
collation.add_plain_witness("A", "Peter's cat.")
collation.add_plain_witness("B", "Peter's dog.")
table = collate(collation, segmentation=False)
print(table)
from collatex import *
tokens_a = [ { "t": "Peter's" }, { "t": "cat" }, { "t": "." ... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Configure Google Cloud environment settings
Step2: Authenticate your Google Cloud account
Step3: Import libraries
Step4: 1. Define dataset me... | <ASSISTANT_TASK:>
Python Code:
!pip install -U -q google-api-python-client
!pip install -U -q pandas
PROJECT_ID = "sa-data-validation"
MODEL_NAME = 'covertype_classifier'
VERSION_NAME = 'v1'
BQ_DATASET_NAME = 'prediction_logs'
BQ_TABLE_NAME = 'covertype_classifier_logs'
!gcloud config set project $PROJECT_ID
try:
... |
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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 three other important Python libraries (which are bundled with the Canopy and Anaconda installations of Python) that come in quite han... | <ASSISTANT_TASK:>
Python Code:
import pmagpy.ipmag as ipmag
import pmagpy.pmag as pmag
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_formats = {'svg',}
%%capture
ipmag.download_magic('magic_contribution_11087.txt',
dir_path=... |
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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: First we need to find the latitude and longitude of Syracuse, then estimate the appropriate zoom level...
Step2: We get the data from the Roads... | <ASSISTANT_TASK:>
Python Code:
import folium
import pandas as pd
SYR = (43.0481, -76.1474)
map = folium.Map(location=SYR, zoom_start=14)
map
data = pd.read_csv('https://cityofsyracuse.github.io/RoadsChallenge/data/potholes.csv')
data.sample(5)
# NOTE: to_dict('records') converts a pandas dataframe back to a list of ... |
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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: Username
Step2: First of all, let's see how many different User's we have on both datasets
Step3: Unique User's in Test are close to $1$/$2$ o... | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.imports import *
from fastai.structured import *
from pandas_summary import DataFrameSummary
PATH = os.getcwd()
train_df = pd.read_csv(f'{PATH}\\train.csv', low_memory=False)
test_df = pd.read_csv(f'{PATH}\\test.csv', low_m... |
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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: Open the SPI rack connection and unlock the controller. This is necessary after bootup of the controller module. If not unlocked, no communicati... | <ASSISTANT_TASK:>
Python Code:
# Import SPI rack and D5b module
from spirack import SPI_rack, D5b_module
import logging
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
COM_port = 'COM4' # COM port of the SPI rack
COM_speed = 1e6 # Baud rate, not of much importance
timeout = 1 # Tim... |
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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: Build clustering model
Step2: Build the optimal model and apply it
Step3: Cluster Profiles
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Python Code:
def loadContributions(file, withsexe=False):
contributions = pd.read_json(path_or_buf=file, orient="columns")
rows = [];
rindex = [];
for i in range(0, contributions.shape[0]):
row = {};
row['id'] = contributions['id'][i]
rindex.append(contributions... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: If you installation was successful, you should see a version Number like '1.3.4'.
Step3: Let's test our function and see the resulting output
S... | <ASSISTANT_TASK:>
Python Code:
>>> import bokeh
>>> bokeh.__version__
import numpy as np
def mohrs_circle(stress_x=1,stress_y=1,shear=0):
A function that calculates the critical values to build a Mohr's Circle
# calculate the average stress, min stress and max stress
stress_avg=(stress_x+str... |
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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: Step 2
Step2: Step 3
Step3: The Expecation Maximization (EM) algorithm can also learn the parameters when we have some latent variables in the... | <ASSISTANT_TASK:>
Python Code:
# Use the alarm model to generate data from it.
from pgmpy.utils import get_example_model
from pgmpy.sampling import BayesianModelSampling
alarm_model = get_example_model("alarm")
samples = BayesianModelSampling(alarm_model).forward_sample(size=int(1e5))
samples.head()
# Defining the Bay... |
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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: <p style="text-align
Step2: <p style="text-align
Step3: <p style="text-align
Step4: <p style="text-align
Step5: <p style="text-align
Step6: ... | <ASSISTANT_TASK:>
Python Code:
def square(x):
return x ** 2
type(square)
ribua = square
print(square(5))
print(ribua(5))
ribua is square
def add(num1, num2):
return num1 + num2
def subtract(num1, num2):
return num1 - num2
def multiply(num1, num2):
return num1 * num2
def divide(num1, num2):
retur... |
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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: README
Step2: Other parameters to set
Step3: Pass hits to mothur aligner
Step4: Get aligned seqs that have > 50% matched to references
Step5:... | <ASSISTANT_TASK:>
Python Code:
cd /usr/local/notebooks
mkdir -p ./workdir
#check seqfile files to process in data directory (make sure you still remember the data directory)
!ls ./data/test/data
Seqfile='./data/test/data/1c.fa'
Cpu='2' # number of maxixum threads for search and alignment
Hmm='./data/SSUsearch_db/Hm... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: A very simple example also shown in the wiki to simply introduce the call signature of adjust_text
Step2: First a very simple example with labe... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
from adjustText import adjust_text
import numpy as np
import pandas as pd
np.random.seed(0)
x, y = np.random.random((2,30))
fig, ax = plt.subplots()
plt.plot(x, y, 'bo')
texts = [plt.text(x[i], y[i], 'Text%s' %i) for i in range(len(x))]
np.random.seed(0)
x... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
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Python Code::
from sklearn.linear_model import Lasso
from sklearn.metrics import mean_squared_error, mean_absolute_error, max_error, explained_variance_score, mean_absolute_percentage_error
# initialise & fit Lasso regression model with alpha set to 0.5
model = Lasso(alpha=0.5)
model.fit(X_train, y_tr... |
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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: Defining and plotting direction over a plane
Step2: Variogram map
| <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pygslib
pygslib.version.__version__
# we read the file header
# use >>> print. pygslib.gslib.read_header.__doc__ for help
#the data file is in the working directory
mydata= pygslib.gslib.read_gslib_file('../datasets/cluster.dat')
#adding elevation and a dummy ... |
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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: Setp 2
Step2: Simulate using different methods
Step3: MOMA and ROOM relly on a reference (wild-type) flux distribution and we can use the one ... | <ASSISTANT_TASK:>
Python Code:
%time
fba_result = simulation.fba(model)
%time
pfba_result = simulation.pfba(model)
model.reactions.PGI
model.reactions.PGI.knock_out()
model.reactions.PGI
%time
fba_knockout_result = simulation.fba(model)
fba_knockout_result[model.objective]
pfba_knockout_result = simulation.pfba(model... |
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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: Server Local
Step2: Export database from dashaboard about device IoT
Step3: Method
| <ASSISTANT_TASK:>
Python Code:
-- Campainha IoT - LHC - v1.1
-- ESP Inicializa pinos, Configura e Conecta no Wifi, Cria conexão TCP
-- e na resposta de um "Tocou" coloca o ESP em modo DeepSleep para economizar bateria.
-- Se nenhuma resposta for recebida em 15 segundos coloca o ESP em DeepSleep.
led_pin = 3
status_led ... |
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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: Exoplanet properties
Step2: Use np.genfromtxt with a delimiter of ',' to read the data into a NumPy array called data
Step3: Make a histogram ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
!head -n 30 open_exoplanet_catalogue.txt
data = np.genfromtxt('open_exoplanet_catalogue.txt', delimiter=',')
assert data.shape==(1993,24)
plt.hist(data[:,2], bins=24, range=(0,12))
plt.xlabel('M_JUP')
plt.ylabel('Num... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Import necessary libraries.
Step2: Lab Task #1
Step3: The source dataset
Step4: Create the training and evaluation data tables
Step5: Lab Ta... | <ASSISTANT_TASK:>
Python Code:
%%bash
sudo pip freeze | grep google-cloud-bigquery==1.6.1 || \
sudo pip install google-cloud-bigquery==1.6.1
import os
from google.cloud import bigquery
%%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
... |
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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 use of watermark is optional. You can install this IPython extension via "pip install watermark". For more information, please see
Step2: S... | <ASSISTANT_TASK:>
Python Code:
%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,sklearn
from IPython.display import Image
%matplotlib inline
# Added version check for recent scikit-learn 0.18 checks
from distutils.version import LooseVersion as Version
from sklearn import __ver... |
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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: Explore the data
Step2: How do we know the calls in a particular training audio clip?
Step3: Exercises
Step4: What about the sound clips?
Ste... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import bird_data as data
import wav_utils
from IPython.core.display import HTML
# Plot in the notebook
%pylab inline
data.call_df.head(10)
HTML(data.label_df.head(10).to_html())
data.call_df.type.unique()
wavs = data.get_wav_dict()
three_call_cl... |
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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
Step2: OPERATIONS IN DICT
Step3: Add
Step4: Remove
Step5: Creating a dictionary from two lists
Step7: Why to use dict? Because it's ... | <ASSISTANT_TASK:>
Python Code:
##Some code to run at the beginning of the file, to be able to show images in the notebook
##Don't worry about this cell
#Print the plots in this screen
%matplotlib inline
#Be able to plot images saved in the hard drive
from IPython.display import Image
#Make the notebook wider
from IPy... |
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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: tf.data を使って NumPy データをロードする
Step2: .npz ファイルからのロード
Step3: tf.data.Dataset を使って NumPy 配列をロード
Step4: データセットの使用
Step5: モデルの構築と訓練
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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: Data collection (2/6)
Step2: Data preparation for labevents table (3/6)
Step3: Data preparation for prescriptions table (4/6)
Step4: Create a... | <ASSISTANT_TASK:>
Python Code:
# # Dash packages installation
# !conda install -c conda-forge dash-renderer -y
# !conda install -c conda-forge dash -y
# !conda install -c conda-forge dash-html-components -y
# !conda install -c conda-forge dash-core-components -y
# !conda install -c conda-forge plotly -y
import dash
imp... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
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Python Code:
import numpy as np
import pandas as pd
import torch
idx, B = load_data()
C = B.index_select(1, idx)
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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: training MultiNB & parameter tuning
Step2: X_counts로 cv했을때
Step3: X_tfidf로 cv했을때
Step4: Tuning & Improvement
Step5: Retraining with new para... | <ASSISTANT_TASK:>
Python Code:
df = pd.read_csv('../resource/final_df3.csv')
sample = df.title
y = df['rating(y)'].values
real_X = df[['avg_rating']].values
cat_X = df.text.fillna("").values
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, TfidfTransformer
count_vect = CountVectorizer()
X_... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 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', 'miroc', 'miroc6', 'land')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "email")... |
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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: Loading all the classifiers from their respective pickle files
Step2: passing it voting class function
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Python Code:
import nltk
import random
from nltk.corpus import movie_reviews
import pickle
from nltk.classify import ClassifierI
from statistics import mode
## defing the voteclassifier class
class VoteClassifier(ClassifierI):
def __init__(self, *classifiers):
self._classifiers = classifie... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Peak finding
Step3: Here is a string with the first 10000 digits of $\pi$ (after the decimal). Write code to perform the following
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Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
def find_peaks(a):
Find the indices of the local maxima in a sequence.
peaks = []
for i in range(len(a)):
if i==0:
if a[i]>a[i+1]:
peaks.append... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Download Isochrones
Step2: Build the Isochrone Library and Synthesize CMD planes
Step3: Here we visualize the isochrone bins in $\log(\mathrm{... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format='retina'
# %config InlineBackend.figure_format='svg'
import matplotlib as mpl
import matplotlib.pyplot as plt
import os
import time
from glob import glob
import numpy as np
brick = 23
STARFISH = os.getenv("STARFISH")
isoc_dir = "b23ir... |
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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: Baseline evaluation
Step2: Now we have a baseline score that we'll iterate towards improving.
Step3: The first thing we notice is that there's... | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
import importlib
import os
import sys
from elasticsearch import Elasticsearch
from skopt.plots import plot_objective
# project library
sys.path.insert(0, os.path.abspath('..'))
import qopt
importlib.reload(qopt)
from qopt.notebooks import evaluate_mrr100... |
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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 rbf kernel has an inverse bandwidth-parameter gamma, where large gamma mean a very localized influence for each data point, and
Step2: Exer... | <ASSISTANT_TASK:>
Python Code:
from sklearn.metrics.pairwise import rbf_kernel
line = np.linspace(-3, 3, 100)[:, np.newaxis]
kernel_value = rbf_kernel(line, [[0]], gamma=1)
plt.plot(line, kernel_value)
from figures import plot_svm_interactive
plot_svm_interactive()
from sklearn import datasets
digits = datasets.load_... |
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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: Matrix Factorisation
Step2: Let $S \in \mathbb{R}^{M \times D}, P \in \mathbb{R}^{N \times D}, Y \in \mathbb{R}^{M \times N}$ be the latent fac... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os, sys, time, gzip
import pickle as pkl
import numpy as np
from scipy.sparse import lil_matrix, csr_matrix, issparse
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm import tqdm
from tools import calc_metrics, diversity, pairwise_distance_hamming,... |
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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: 这里，mnist是一个轻量级的类。它以Numpy数组的形式存储着训练、校验和测试数据集。同时提供了一个函数，用于在迭代每一小批数据，后面我们将会用到。
Step2: 计算图
Step3: 这里的x和y_并不是特定的值，相反，他们都只是一个占位符，可以在TensorFlow运行某一计算... | <ASSISTANT_TASK:>
Python Code:
import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
import tensorflow as tf
sess = tf.InteractiveSession()
x = tf.placeholder("float", shape=[None, 784])
y_ = tf.placeholder("float", shape=[None, 10])
W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.ze... |
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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: Use list comprehension and lambda/map function to define <b>stuff</b>.
Step2: <u>Problem 2</u>
Step3: <u>Problem 3</u>
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Python Code:
words = 'The quick brown fox jumps over the lazy dog'.split()
print words
stuff = []
for w in words:
stuff.append([w.upper(), w.lower(), len(w)])
for i in stuff:
print i
stuff = map(lambda w: [w.upper(), w.lower(), len(w)],words)
for i in stuff: print i
sentence = "It's a myth t... |
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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: Run as a Python module
Step2: Now, let's do it on Cloud ML Engine so we can train on GPU
Step3: Monitoring training with TensorBoard
Step4: H... | <ASSISTANT_TASK:>
Python Code:
import os
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT ID
BUCKET = "cloud-training-demos-ml" # REPLACE WITH YOUR BUCKET NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
MODEL_TYPE = "linear" # "linear", "dnn", "dnn_dropout", or "cnn"
# do not... |
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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: 练习 2：共n个随机整数，整数范围在m与k之间，求西格玛log(随机整数)及西格玛1/log(随机整数)
Step2: 练习 3：写函数，求s=a+aa+aaa+aaaa+aa...a的值，其中a是[1,9]之间的随机整数。例如2+22+222+2222+22222(此时共有5个数相加... | <ASSISTANT_TASK:>
Python Code:
n = int(input('请输入你想取的整数的个数'))
m = int(input('请输入取整范围的下限'))
k = int(input('请输入取整范围的上限'))
import random,math
def get_num():
number = random.randint(m,k)
return number
i = 0
total = 0
while i < n:
total = total + get_num()
i = i + 1
print(get_num())
average = total/... |
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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: Matching coordinates
Step2: Plot $W_1-J$ vs $W_1$
Step3: W1-J < -1.7 => galaxy
Step4: Collect relevant data
Step5: Analysis
Step6: DBSCAN
S... | <ASSISTANT_TASK:>
Python Code:
#obj = ["3C 454.3", 343.49062, 16.14821, 1.0]
obj = ["PKS J0006-0623", 1.55789, -6.39315, 1.0]
#obj = ["M87", 187.705930, 12.391123, 1.0]
#### name, ra, dec, radius of cone
obj_name = obj[0]
obj_ra = obj[1]
obj_dec = obj[2]
cone_radius = obj[3]
obj_coord = coordinates.SkyCoord(ra=obj_... |
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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: 2.2 Your first script
Step2: Notice that Hello world! is printed at the bottom of the cell as an output. In general, this is how output of a py... | <ASSISTANT_TASK:>
Python Code:
# change this cell into a Markdown cell. Then type something here and execute it (Shift-Enter)
'''Make sure you are in "edit" mode and that this cell is for Coding ( You should see the In [ ]:)
on the left of the cell. '''
print("Hello world!")
# print your name in this cell.
# Addit... |
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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 are grouped operations currently handled in pandas?
Step2: What about composing f_elwise and f_agg operations?
Step3: Also, as noted in th... | <ASSISTANT_TASK:>
Python Code:
%%capture
import pandas as pd
pd.set_option("display.max_rows", 5)
from siuba import _
from siuba.data import mtcars
g_cyl = mtcars.groupby("cyl")
## Both snippets below raise an error.... :/
g_cyl.mpg + g_cyl.mpg
g_cyl.add(g_cyl.mpg)
# two ways to do it f_elwise
ser_mpg2 = mtcars.mpg + ... |
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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: Por la ecuación logística, $\mu(x)$ representa una tasa de crecimiento de la población. Por la gráfica, cuando la poblacion es pequeña esta tasa... | <ASSISTANT_TASK:>
Python Code:
# Numeral 1
# Importar librerías necesarias
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Definimos funcion mu
def mu(x, r):
return r*(1-x)
# Definimos conjunto de valores en x
x = np.linspace(0, 1.2, 50)
# Valor del parametro solicitado
r = 1
# Conjunto de v... |
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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 the distributions for the guest and the prize. Note that both distributions are independent of one another.
Step2: Now let's creat... | <ASSISTANT_TASK:>
Python Code:
import math
from pomegranate import *
guest = DiscreteDistribution( { 'A': 1./3, 'B': 1./3, 'C': 1./3 } )
prize = DiscreteDistribution( { 'A': 1./3, 'B': 1./3, 'C': 1./3 } )
monty = ConditionalProbabilityTable(
[[ 'A', 'A', 'A', 0.0 ],
[ 'A', 'A', 'B', 0.5 ],
[ 'A', 'A', 'C', 0.5 ]... |
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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: Get data from S3
Step2: Adding an RGB layer to the map
Step3: To add the layer we call M.add_layer passing in a subset of the raster data set'... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pylab as plt
!curl -o /tmp/L57.Globe.month09.2010.hh09vv04.h6v1.doy247to273.NBAR.v3.0.tiff http://golden-tile-geotiffs.s3.amazonaws.com/L57.Globe.month09.2010.hh09vv04.h6v1.doy247to273.NBAR.v3.0.tiff
# Set the center of the map to the location t... |
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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: [normalization.BatchNormalization.1] epsilon=1e-02, axis=-1, center=True, scale=True
Step2: [normalization.BatchNormalization.2] epsilon=1e-05,... | <ASSISTANT_TASK:>
Python Code:
data_in_shape = (4, 3)
norm = BatchNormalization(epsilon=1e-05, axis=-1, center=True, scale=True)
layer_0 = Input(shape=data_in_shape)
layer_1 = norm(layer_0)
model = Model(inputs=layer_0, outputs=layer_1)
# set weights to random (use seed for reproducibility)
weights = []
for i, w in enu... |
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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: Extension types
Step5: Extension types
Step6: The tf.experimental.ExtensionType base class works similarly to typing.NamedTuple and @dataclass... | <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: Use the built-in library methods
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Python Code:
import pandas as pd
import sklearn
df = pd.read_table('https://raw.githubusercontent.com/sinanuozdemir/sfdat22/master/data/sms.tsv', sep='\t', header=None, names=['label', 'msg'])
df
df.label.value_counts()
value_probablity = df.label.value_counts()/len(df)
spam_probability = value_probab... |
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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: Al paso del tiempo
Step2: Tamaños de clases de poblaciones
Step3: Proporciones de tamaños de clases de poblaciones
Step4: Eigenvector, eigenv... | <ASSISTANT_TASK:>
Python Code:
# el modelo de Leslie
L = np.array([[0, 1, 2],
[0.8, 0, 0],
[0, 0.7, 0]])
# las cuatro clases de edad tienen 100 pobladoras al inicio
poblacion_inicial = [100,100,100]
# lista para contener un vector de tamaños de población por clase para
# cada unid... |
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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: Locating the third reference point
Step2: Now a least squares estimator is used to work out the x-y coordinates of the third reference point (x... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy.optimize import minimize
x0 = [0,0]
x1 = [0, 2209]
d_0_2 = 2047
d_1_2 = 3020
def dist(a,b):
return np.sqrt((a[0]-b[0])**2 + (a[1]-b[1])**2)
def f(x):
return (dist(x0, x) - d_0_2)**2 + (dist(x1, x) - d_1_2)**2
initial_guess = [2047, 0]
res = minimiz... |
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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: Ignore any warnings above (I coudn't be bothered to compile audiolab with Alsa). Below you will find the method to download the Voxforge databas... | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('../python')
from voxforge import *
downloadVoxforgeData('../audio')
f=loadFile('../audio/Joel-20080716-qoz.tgz')
print f.props
print f.prompts
print f.data
%xdel f
corp=loadBySpeaker('../audio', limit=30)
addPhonemesSpk(corp,'../data/lex.tgz')
print corp.ke... |
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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 sites will block request from urlib, so we set a custom 'User-Agent' header
Step2: Let's check the HTTP status and the message.
Step3: We... | <ASSISTANT_TASK:>
Python Code:
import urllib.request
url = 'https://medium.com/tag/machine-learning'
req = urllib.request.Request(url, headers={'User-Agent' : "Magic Browser"})
con = urllib.request.urlopen(req)
print(con.status, con.msg)
con.getheader('Content-Type')
text = con.read()
text[:500]
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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: Veja a seguir uma matriz bidimensional de dados ponto flutuante de 2 linhas e 3 colunas. Observe que a tupla do shape aumenta para a esquerda, i... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
a = np.array( [2,3,4,-1,-2,256, 270] ,'uint8')
print ('Dimensões: a.shape=', a.shape)
print ('Tipo dos elementos: a.dtype=', a.dtype)
print ('Imprimindo o array completo:\n a=',a)
m = a.max()
print(m)
print(m.dtype)
b = a//a.max()
print (b)
b = np.array( [ [[1.5, 2.3, ... |
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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: Run the simulation, save the spectra
Step2: Simulation outputs
Step3: the table of simulated quasars, including redshift, luminosity, syntheti... | <ASSISTANT_TASK:>
Python Code:
M1450 = linspace(-30,-22,20)
zz = arange(0.7,3.5,0.5)
ple = bossqsos.BOSS_DR9_PLE()
lede = bossqsos.BOSS_DR9_LEDE()
for z in zz:
if z<2.2:
qlf = ple if z<2.2 else lede
plot(M1450,qlf(M1450,z),label='z=%.1f'%z)
legend(loc='lower left')
xlim(-21.8,-30.2)
xlabel("$M_{1450}$")... |
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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: Try something that doesn't work
Step2: Try something that works
| <ASSISTANT_TASK:>
Python Code:
# Create some data
scores = [23,453,54,235,74,234]
# Try to:
try:
# Add a list of integers and a string
scores + 'A string of characters.'
# If you get an error, set the error as 'e',
except Exception as e:
# print the error, e
print('Error:', e)
# Then,
finally:
# pr... |
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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: パラメータの設定
Step2: メッシュの読み込み
Step3: 次のコマンドで、mに設定したメッシュをgmshのスクリプトでpng画像に打ち出し確認します。
Step4: 以下のようにipythonの機能を使用して、システムのコマンドラインでの操作を行いIpython上に画像を表... | <ASSISTANT_TASK:>
Python Code:
import getfem as gf
import numpy as np
file_msh = './mesh/tripod.mesh'
E = 1e3
Nu = 0.3
Lambda = E*Nu/((1+Nu)*(1-2*Nu))
Mu = E/(2*(1+Nu))
m = gf.Mesh('load',file_msh)
m.set('optimize_structure')
m.export_to_pos('./pos/m.pos')
%%writefile gscript
Print "./png/m.png";
Exit;
!gmsh ./pos/... |
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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: TV Script Generation
Step3: Explore the Data
Step6: Implement Preprocessing Functions
Step9: Tokenize Punctuation
Step11: Preprocess all the... | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
view_sentence_range = (0, 10)
DON'T MODIFY ANYTHING IN THIS CELL
import num... |
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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: Day 5
Step2: Example
Step3: Question 1
Step4: Question 2
| <ASSISTANT_TASK:>
Python Code:
import math
from matplotlib import pylab as plt
%matplotlib inline
def pdf(x, m, variance):
sigma = math.sqrt(variance)
probability density function
return 1 / (sigma * math.sqrt(2 * math.pi)) * math.e ** (-1 * ((x - m)**2 / (2 * variance ** 2)))
pdf(20, 20, 4)
N = range(50)
... |
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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: TV Script Generation
Step3: Explore the Data
Step6: Implement Preprocessing Functions
Step9: Tokenize Punctuation
Step11: Preprocess all the... | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
view_sentence_range = (10, 20)
DON'T MODIFY ANYTHING IN THIS CELL
import nu... |
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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: Implementing Gates
Step4: Constant Gates
Step5: <hr/>
Step7: For more info on broadcasting, take a look at the Theano documentation on broadc... | <ASSISTANT_TASK:>
Python Code:
import theano
from theano import tensor
import numpy as np
from collections import namedtuple
Gate = namedtuple("Gate", "arity module")
# Theano's to_one_hot converts a numpy array
# to a one-hot encoded version. In order to
# know how big to make the vector, all gates
# take M, the sma... |
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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 consistency, we will be using a random number generator with a seed for some of the iterators.
Step2: Let's start by creating a small datas... | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import numpy as np
import pyxis as px
rng = np.random.RandomState(1234)
nb_samples = 10
X = rng.rand(nb_samples, 254, 254, 3)
y = np.arange(nb_samples, dtype=np.uint8)
db = px.Writer(dirpath='data', map_size_limit=30, ram_gb_limit=1)
db.put_samples... |
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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: uncomment this to download the data
Step2: Loading Data with Pandas
Step3: Because we'll use it so much, we often import under a shortened nam... | <ASSISTANT_TASK:>
Python Code:
!ls
# !curl -o pronto.csv https://data.seattle.gov/api/views/tw7j-dfaw/rows.csv?accessType=DOWNLOAD
import pandas
import pandas as pd
data = pd.read_csv('pronto.csv')
data.head()
data.tail()
data.shape
data.columns
data.index
data.dtypes
data.columns
data['tripduration']
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: <p>Equivalente a</p>
Step2: $
Step3: Equivalente a
Step4:
Step5: $
Step6: Implementação
Step7: $$
| <ASSISTANT_TASK:>
Python Code:
L = k(X, .7)
D = diag(L)
M = inv(D).dot(L)
# Mi,j denotes the transition probability
# from the point xi to the point xj in one time step
print M
L/L.sum(axis=1).reshape(-1,1)
Ms = (diag(D,.5)).dot(M).dot(diag(D,-.5))
Ms
p = L.sum(axis=1)
for i in range(0,3):
a = []
for j in ra... |
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Description:
Step1: Now try setting time range via eventTime.
| <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from owslib.sos import SensorObservationService
import pdb
from owslib.etree import etree
import pandas as pd
import datetime as dt
import numpy as np
url = 'http://sdf.ndbc.noaa.gov/sos/server.php?request=GetCapabilities&service=SOS&version=1.0.0'
ndbc = SensorObserva... |
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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: Model Inputs
Step2: Generator network
Step3: Discriminator
Step4: Hyperparameters
Step5: Build network
Step6: Discriminator and Generator L... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pickle as pkl
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data')
def model_inputs(real_dim, z_dim):
inputs_real = tf.placeholde... |
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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: Experimental PD affinity estimate
Step2: Load in Data
Step3: Merge experimental and model score data
Step4: Examine distribution of scores fo... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# Tuple of form: (Seq, Stringency, Kd)
validated_core_seqs = [ ('TTTGGTGGATAGTAA', 1, '< 512 nM'),
('AGAGGATTTGGTGGATAGT', 0, '> 512nM'),
('AGAGGAT... |
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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: 첫 번째 신경망 훈련하기
Step2: 패션 MNIST 데이터셋 임포트하기
Step3: load_data() 함수를 호출하면 네 개의 넘파이(NumPy) 배열이 반환됩니다
Step4: 데이터 탐색
Step5: 비슷하게 훈련 세트에는 60,000개의 레이... | <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>
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Description:
Step1: tf.keras를 사용한 Neural Style Transfer
Step2: 이미지를 다운로드받고 스타일 참조 이미지와 콘텐츠 이미지를 선택합니다
Step3: 입력 시각화
Step4: 이미지를 출력하기 위한 간단한 함수를 정의합니다
Step5: TF-... | <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: XML example
Step2: XML exercise
Step3: 10 countries with the lowest infant mortality rates
Step4: 10 cities with the largest population
Step5... | <ASSISTANT_TASK:>
Python Code:
from xml.etree import ElementTree as ET
document_tree = ET.parse( './data/mondial_database_less.xml' )
# print names of all countries
for child in document_tree.getroot():
print (child.find('name').text)
# print names of all countries and their cities
for element in document_tree.ite... |
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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: Project Euler
Step2: Now write a set of assert tests for your number_to_words function that verifies that it is working as expected.
Step4: No... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
numbers = {0: "", 1:"one", 2:"two", 3:"three", 4:"four", 5:"five", 6:"six", 7:"seven", 8:"eight", 9:"nine", 10:"ten", 11:"eleven",
12:"twelve", 13:"thirteen", 14:"fourteen", 15:"fifteen", 16:"sixteen", 17:"seventeen", 18:"eighteen", 19:"nineteen",
20... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Using interact for animation with data
Step3: To create an animation of a soliton propagating in time, we are going to precompute the soliton d... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
def soliton(x, t, c, a):
Return phi(x, t) for a soliton wave with constants c and a.
phiarg = (np.sqrt(c)/2... |
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Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Loading source dataset MNIST
Step2: Loading target dataset MNIST-M
Step3: Naive model
Step4: After training on our source dataset MNIST, we e... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
USE_SUBSET = True
def get_subset(x, y):
if not USE_SUBSET:
return x, y
subset_index = 10000
np.random.seed(1)
indexes = np.random.permutation(len(x))[:subset_index]
x, y = x[indexes], y[indexes]
return x, y
from tensorflow.keras.datasets... |
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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: Radiative efficiencies of each gas
Step2: Impulse response functions (IRF) for CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O
Step4: CO<su... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import matplotlib as mpl
from scipy.interpolate import interp1d
from scipy.signal import fftconvolve
from scipy.integrate import cumtrapz
import seaborn as sns
sns.set_palette('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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<USER_TASK:>
Description:
Step1: As always, let's create a dataset with MNIST data
Step2: We will use the standard ResNet from the BatchFlow models.
Step3: Now create pipeline... | <ASSISTANT_TASK:>
Python Code:
import sys
import numpy as np
import tensorflow as tf
from tqdm import tqdm_notebook as tqn
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('seaborn-poster')
plt.style.use('ggplot')
sys.path.append('../../..')
from batchflow import B, V
from batchflow.opensets import MNIS... |
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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: ⚠️
Step2: Adding and Inspecting Attributes
Step3: Adding Attributes for each existing node
Step4: Reading in Different Representations of Gra... | <ASSISTANT_TASK:>
Python Code:
import csv
import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# Create empty graph
G = nx.Graph()
# Add nodes
G.add_node(1)
G.add_nodes_from([2, 3])
G.add_node(4)
G.nodes()
# add edges
G.add_edge(1, 2)
# get graph info
print(nx.info(G))
nx.dra... |
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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: Replace by your GCP project and bucket
Step2: Loading the dataset in GCS
Step3: It has very specialized language such as
Step4: and for gcs-... | <ASSISTANT_TASK:>
Python Code:
#!pip freeze | grep tensorflow-hub==0.7.0 || pip install tensorflow-hub==0.7.0
import os
import tensorflow as tf
import tensorflow_hub as hub
PROJECT = !(gcloud config get-value core/project)
PROJECT = PROJECT[0]
BUCKET = PROJECT
REGION = "us-central1"
%env PROJECT = {PROJECT}
%env BUCKE... |
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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:
Step4: Cython -- A Transcompiler Language
Step6: Now let's see the time difference between a cyfib and pyfib ...
Step7: Introducing runcython !!
Step... | <ASSISTANT_TASK:>
Python Code:
%%file ./src/helloCython.pyx
import cython
import sys
def message():
print(" Hello World ....\n")
print(" Hello Central Ohio Python User Group ...\n")
print(" The 614 > 650::True")
print(" Another line ")
print(" The Python version is %s" % sys.version)
print(" Th... |
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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 data is from the Council Grove gas reservoir in Southwest Kansas. The Panoma Council Grove Field is predominantly a carbonate gas reservoi... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
from pandas import set_option
set_option("display.max_rows", 10)
pd.options.mode.ch... |
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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:
| <ASSISTANT_TASK:>
Python Code::
import tensorflow as tf
from sklearn.model_selection import train_test_split
TRAINING_FILENAMES, VALIDATION_FILENAMES = train_test_split(
tf.io.gfile.glob(r'data\tfrecords\ld_train*.tfrec'),
test_size=0.3, random_state=101
)
<END_TASK>
|
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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: First, set filename to what we want to examine and read PhSF header
Step2: Checking PhSF header parameters
Step3: Energy Spectrum tests
Step4:... | <ASSISTANT_TASK:>
Python Code:
import math
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import BEAMphsf
import text_loader
import H1Dn
import H1Du
import ListTable
%matplotlib inline
C = 25
phsfname = "PHSF" + "." + str(C)
phsfname = "../" + phsfname
print ("We're reading the {1}mm phase space ... |
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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: Flower power
Step2: ConvNet Codes
Step3: Below I'm running images through the VGG network in batches.
Step4: Building the Classifier
Step5: ... | <ASSISTANT_TASK:>
Python Code:
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
vgg_dir = 'tensorflow_vgg/'
# Make sure vgg exists
if not isdir(vgg_dir):
raise Exception("VGG directory doesn't exist!")
class DLProgress(tqdm):
last_block = 0
def hook(self, block_... |
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Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step2: We're going to be building a model that recognizes these digits as 5, 0, and 4.
Step3: Working with the images
Step4: The first 10 pixels are ... | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
from IPython.display import Image
import base64
Image(data=base64.decodestring("iVBORw0KGgoAAAANSUhEUgAAAMYAAABFCAYAAAARv5krAAAYl0lEQVR4Ae3dV4wc1bYG4D3YYJucc8455yCSSIYrBAi4EjriAZHECyAk3rAID1gCIXGRgIvASIQr8UTmgDA5imByPpicTcYGY+yrbx+tOUWpu2e6u7qnZ7qXVFP... |
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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: Q1
Step2: Q2
Step3: Q3
Step4: La méthode insere prévoit de ne rien faire dans le cas où le mot s passé en argument est égal à l'attribut m... | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
class NoeudTri (object):
def __init__(self,s):
self.mot = s
NoeudTri("a")
class NoeudTri (object):
def __init__(self,s):
self.mot = s
def __str__(self):
return self.mot + "\n" ... |
<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 into a Pandas DataFrame from a CSV file.
Step2: While the coordinates for each store are contained in an X (longitude) and Y (lat... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import arcgis
df = pd.read_csv('./store_locations.csv', index_col='OBJECTID')
df.head()
df['SHAPE'] = df.apply(lambda row: arcgis.geometry.Point({'x': row.X, 'y': row.Y, 'spatialReference': {'wkid': 4326}}), axis=1)
df = df.drop(['X', 'Y'], axis=1)
df.head()
sdf = 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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<USER_TASK:>
Description:
Step2: Next, a function that extracts the net rating for a team.
Step3: The Pistons
Step4: As experienced as a fan this year, we are a bit bi-modal, ... | <ASSISTANT_TASK:>
Python Code:
import csv
import numpy as np
import matplotlib.pyplot as plt
import os
import itertools
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
nba_games = []
fname = 'nba-games.csv'
with open(fname,"r") as f:
# game_id,game_date,team_id,team_name,net_rating
reader = cs... |
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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: We can do a lil bit better
Step2: Linear regression example
Step3: Using the Normal Equation
Step4: Batch gradient descent with manually comp... | <ASSISTANT_TASK:>
Python Code:
tf.reset_default_graph()#important for tensorboard when using jupyter notebook
students = tf.Variable(13, name="students")
coffee = tf.Variable(-10, name="coffee")
lees_checking = students*coffee
#one way to do it but pretty verbose
sess = tf.Session()
sess.run(students.initializer)
se... |
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Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: 2 - Overview of the Problem set
Step2: We added "_orig" at the end of image datasets (train and test) because we are going to preprocess them. ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset
%matplotlib inline
# Loading the data (cat/non-cat)
train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dat... |
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Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Create the signal
Step2: Listen to the signal
Step3: Plot the signal
Step4: Segmentation Using Python List Comprehensions
Step5: Define a he... | <ASSISTANT_TASK:>
Python Code:
T = 3.0 # duration in seconds
sr = 22050 # sampling rate in Hertz
amplitude = numpy.logspace(-3, 0, int(T*sr), endpoint=False, base=10.0) # time-varying amplitude
print(amplitude.min(), amplitude.max()) # starts at 110 Hz, ends at 880 Hz
t = numpy.linspace(0, T, int(T*sr), endpoin... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step5: As we can see from the cross-validation results above, the performance of the three models is almost equal.
Step6: <br>
Step8: <br>
| <ASSISTANT_TASK:>
Python Code:
from sklearn import datasets
iris = datasets.load_iris()
X, y = iris.data[:, 1:3], iris.target
from sklearn import cross_validation
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
import nu... |
<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: TensorBoard Scalars
Step2: 配置数据用来训练回归
Step3: 训练模型和记录损失 (loss)
Step4: 使用 TensorBoard 检查损失 (loss)
Step5: <img class="tfo-display-only-on-site"... | <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: Read and preprocess the data. Preprocessing consists of
Step2: Define a function that runs ICA on the raw MEG data and plots the components
Ste... | <ASSISTANT_TASK:>
Python Code:
# Authors: Pierre Ablin <pierreablin@gmail.com>
#
# License: BSD-3-Clause
from time import time
import mne
from mne.preprocessing import ICA
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'... |
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