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names = [
    'A', 'A#', 'B', 'C', 'C#', 'D',
    'D#', 'E', 'F', 'F#', 'G', 'G#'
]
name2int = { n:i for i, n in enumerate(names) }


class Note(object):
    def __init__(self, name, major=True) -> None:
        super().__init__()

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corr = train.corr(method='spearman')
mask = np.zeros_like(corr)
mask[np.triu_indices_from(mask)] = True

fig, ax = plt.subplots()
ax.figure.set_size_inches(10, 10)
sns.heatmap(corr, ax=ax, annot=True, mask=mask, fmt='.2f', cbar=False, square=True, cmap=plt.cm.Blues, center=0)

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 1 Physical GPUs, 1 Logical GPU
 > Preprocessing Spotify dataset. This process may take several hours.
 >> Checking if the Spotify MPD is already preprocessed.
 >> Preprocessed version was found. Skipping stage.
 > Loading Spotify MPD dataset.
 >> Dataset loaded.
 > Splitting dataset
 >> Sampling 10% of dataset
 >> Splitting sample with 20% for testing
 >> Creating Vocab.

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 > Preprocessing Spotify dataset. This process may take several hours.
 >> Checking if the Spotify MPD is already preprocessed.
 >> Preprocessed version was found. Skipping stage.
 > Loading Spotify MPD dataset.
 >> Dataset loaded.
 > Splitting dataset
 >> Sampling 1% of dataset
 >> Splitting sample with 20% for testing
 >> Creating Vocab.
 > Building tensorflow datasets.

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# (C) Júlio Guedes
# ATAL 2019.2
 
letters = input()
hello = "hello"
pointer = 0
 
for l in letters:
	if pointer < len(hello):
		if (l == hello[pointer]):

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dtree_parameters = {
    'max_depth': [12, 24, 36, 50],
    'min_samples_leaf': [5, 10, 15, 25],
    'max_features': ['sqrt', 'log2', None],
    'max_leaf_nodes': [50, 100, 175, 250]
}
decision_tree_model = DTree()
decision_tree = GSearchCV(decision_tree_model, dtree_parameters, cv=5, n_jobs=-1, scoring=acc, verbose = 10)
decision_tree.fit(rntr, ry_train)
best_tree = decision_tree.best_estimator_

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from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels
import itertools


def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix',
                          cmap=plt.cm.get_cmap('Blues'), figsize=None, savefig=None):
    # Função importada dos exemplos do SKLearn
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

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juliobguedes

# coding: utf-8
# (C) Júlio Barreto
# 22/09/2018
'''
Script to monitor any website's HTML changes
'''

import urllib2, smtplib
from getpass import getpass
from time import sleep

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import React from 'react';
import { View } from 'react-native';
import { Input } from 'react-native-elements';

type Props = {
  placeholder: string,
  value: string,
  onChangeText: () => void,
  errors: string,
  password: boolean