burnpiro

Install V8 on Linux

Requirements

• git

Installation

depot_tools

burnpiro

import tensorflow as tf
from data.data_generator import DataGenerator
from config import cfg

## Create train dataset
train_datagen = DataGenerator(file_path=cfg.TRAIN.DATA_PATH, config_path=cfg.TRAIN.ANNOTATION_PATH)

## Create validation dataset
val_generator = DataGenerator(file_path=cfg.TEST.DATA_PATH, config_path=cfg.TEST.ANNOTATION_PATH, debug=False)

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Spread vs JSON.parse speed when calling simple function

const N = 100000;

function test(obj) {
  var result = obj.a + obj.b;
  return result;
}
function test2(obj) {
 var result = obj.a + obj.b;

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export const DicomDictionary = {
  '00000000': 'Command Group Length',
  '00000001': 'Command Length to End',
  '00000002': 'Affected SOP Class UID',
  '00000003': 'Requested SOP Class UID',
  '00000010': 'Command Recognition Code',
  '00000100': 'Command Field',
  '00000110': 'Message ID',
  '00000120': 'Message ID Being Responded To',
  '00000200': 'Initiator',

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# Load the TensorBoard notebook extension
%load_ext tensorboard

# Clear out any prior log data. (optional)
!rm -rf logs

import datetime
import io
import itertools
import numpy as np

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def plot_confusion_matrix(cm, class_names=class_names):
    """
    Returns a matplotlib figure containing the plotted confusion matrix.

    Args:
    cm (array, shape = [n, n]): a confusion matrix of integer classes
    class_names (array, shape = [n]): String names of the integer classes
    """
    figure = plt.figure(figsize=(8, 8))
    plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)

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unnormalized_train_data = extract_data(path_to_train_file)
normalized_train_data, train_scale = preproc_data(unnormalized_train_data, norm_cols, scale_cols)
// Create and train model
unnormalized_test_data = extract_data(path_to_test_file)
normalized_test_data, _ = preproc_data(unnormalized_test_data, norm_cols, scale_cols, train_scale)

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if scale_cols:
    # Scale year and week no but within (0,1)
    new_data[scale_cols] = MinMaxScaler(feature_range=(0, 1)).fit(train_scale[scale_cols]).transform(
        new_data[scale_cols])

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if norm_cols:
    # Normalize temp and percipation
    new_data[norm_cols] = StandardScaler().fit(train_scale[norm_cols]).transform(new_data[norm_cols])

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import pandas as pd
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from data_info import cols_to_norm, cols_to_scale
def preproc_data(data, norm_cols=cols_to_norm, scale_cols=cols_to_scale, train_scale=None):
    """
    :param data: Dataframe
    :param norm_cols: List<string>
    :param scale_cols: List<string>
    :param train_scale: Dataframe
    :return: Tuple(Dataframe, Dataframe)