burnpiro

run following code with

d8 --trace-ic --allow-natives-syntax --trace-maps index.js

const N = 100000;

class Component {

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const startT1 = Date.now();

const N = 10000;

let f = { tmp: 3, tmp2: 3, tmp3: 3, tmp4: 3, tmp5: 3, a: 'Gandalf', b: 'The Grey' };
let f2 = { tmp: 3, tmp2: 3, tmp3: 3, tmp4: 3, tmp5: 3, a: 'Jack', b: 'Sparrow' };
let f3 = { tmp: 3, tmp2: 3, tmp3: 3, tmp4: 3, tmp5: 3, a: 'Charles', b: 'Xavier' };
let f4 = { tmp: 3, tmp2: 3, tmp3: 3, tmp4: 3, tmp5: 3, a: 'Frodo', b: 'Baggins' };
let f5 = { tmp: 3, tmp2: 3, tmp3: 3, tmp4: 3, tmp5: 3, a: 'Legolas', b: 'Thranduilion' };
let f6 = { tmp: 3, tmp2: 3, tmp3: 3, tmp4: 3, tmp5: 3, a: 'Indiana', b: 'Jones' };

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eval(`
const startT1 = Date.now();

const N = 10000;

let f = { tmp: 3, tmp2: 3, tmp3: 3, tmp4: 3, tmp5: 3, a: 'Gandalf', b: 'The Grey' };
let f2 = { tmp: 3, tmp2: 3, tmp3: 3, tmp4: 3, tmp5: 3, a: 'Jack', b: 'Sparrow' };
let f3 = { tmp: 3, tmp2: 3, tmp3: 3, tmp4: 3, tmp5: 3, a: 'Charles', b: 'Xavier' };
let f4 = { tmp: 3, tmp2: 3, tmp3: 3, tmp4: 3, tmp5: 3, a: 'Frodo', b: 'Baggins' };
let f5 = { tmp: 3, tmp2: 3, tmp3: 3, tmp4: 3, tmp5: 3, a: 'Legolas', b: 'Thranduilion' };

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LABEL_COLUMN = 'total_cases'
NUMERIC_COLUMNS = ['year',
                   'weekofyear',
                   'ndvi_ne',
                   'ndvi_nw',
                   'ndvi_se',
                   'ndvi_sw',
                   'precipitation_amt_mm',
                   'reanalysis_air_temp_k',
                   'reanalysis_avg_temp_k',

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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)

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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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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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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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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)