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- import numpy as np
- import scipy.stats as sp
- def stats_calculate_all(x, stat_config):
- """Пресметка на статистиките од дадената листа x, врз основа на stat_config
- вредностите.
- :param x: листа на временската серија на податоци
- :type x: list(float)
- :param stat_config: листа со имиња на статистики кои треба да се пресметаат
- :type stat_config: list(str)
- :return: листа со пресметаните статистики според редоследот од stat_config
- :rtype: list
- """
- assert len(set(stat_config).difference(['len', 'min', 'max', 'range', 'mean', 'hmean',
- 'gmean', 'var', 'std', 'skew', 'kurtosis',
- 'median', 'mode', 'energy', 'energy_sample', 'snr'])) == 0
- x_array = np.array(x)
- n = len(x)
- if n == 0:
- values = [0 for i in range(len(stat_config))]
- return values, stat_config
- min_value = np.min(x_array)
- if min_value < 1:
- offset = 1 + np.abs(min_value)
- else:
- offset = 0
- max_value = np.max(x_array)
- values = []
- for stat_name in stat_config:
- if stat_name == 'len':
- values.append(n)
- elif stat_name == 'min':
- values.append(min_value)
- elif stat_name == 'max':
- values.append(max_value)
- elif stat_name == 'range':
- range_value = max_value - min_value
- values.append(range_value)
- elif stat_name == 'mean':
- mean_value = np.mean(x_array)
- values.append(mean_value)
- elif stat_name == 'hmean':
- hmean_value = sp.hmean(x_array + offset)
- values.append(hmean_value)
- elif stat_name == 'gmean':
- gmean_value = sp.gmean(x_array + offset)
- values.append(gmean_value)
- elif stat_name == 'var':
- std_value = np.std(x_array)
- var_value = std_value ** 2
- values.append(var_value)
- elif stat_name == 'std':
- std_value = np.std(x_array)
- values.append(std_value)
- elif stat_name == 'skew':
- skew_value = sp.skew(x_array)
- values.append(skew_value)
- elif stat_name == 'kurtosis':
- kurtosis_value = sp.kurtosis(x_array)
- values.append(kurtosis_value)
- elif stat_name == 'median':
- median_value = np.median(x_array)
- values.append(median_value)
- elif stat_name == 'mode':
- mode_value = sp.mode(x_array)[0][0]
- values.append(mode_value)
- elif stat_name == 'energy':
- energy_value = np.sum(x_array ** 2)
- values.append(energy_value)
- elif stat_name == 'energy_sample':
- energy_sample_value = np.sum(x_array ** 2) / n
- values.append(energy_sample_value)
- elif stat_name == 'snr':
- mean_value = np.mean(x_array)
- std_value = np.std(x_array)
- snr_value = 0.0
- if std_value != 0:
- snr_value = mean_value / std_value
- values.append(snr_value)
- return values
- def percentiles_all(x, iqr=True, amplitude=True, percentiles_list=[5, 10, 25, 40, 50, 60, 75, 90, 95]):
- """Пресметка на перцентили за дадените податоци
- :param x: листа на временската серија на податоци
- :type x: list(float)
- :param iqr: дали да се пресмета интерквартален ранг.
- Ако е True, percentiles_list мора да ги содржи вредностите 25 и 75
- :type iqr: bool
- :param amplitude: дали да се пресмета амплитуда.
- Ако е True, percentiles_list мора да ги содржи вредностите 1 и 99
- :type amplitude: bool
- :param percentiles_list: листа на перцентили кои е потребно да се пресметаат.
- Листата мора да има барем еден елемент и вредностите да бидат поголеми од
- 0 и помали од 100.
- :type percentiles_list: list(int)
- :return: листа со вредности за перцентилите според редоследот од параметарот
- percentiles_list, каде што доколку се бараат iqr и amplitude, тие се
- додаваат на крајот на резултантната листа.
- :rtype: list(float)
- """
- assert len(percentiles_list) > 0 and all([0 < q < 100 for q in percentiles_list])
- if len(x) == 0:
- values = [0 for i in range(len(percentiles_list))]
- return values
- values = list(np.percentile(x, percentiles_list))
- if iqr and 25 in percentiles_list and 75 in percentiles_list:
- q1 = percentiles_list.index(25)
- q3 = percentiles_list.index(75)
- values.append(values[q3] - values[q1])
- if amplitude and 1 in percentiles_list and 99 in percentiles_list:
- q1 = percentiles_list.index(1)
- q3 = percentiles_list.index(99)
- values.append(values[q3] - values[q1])
- return values
- def is_valid_example(i, window_size, y):
- """Проверка дали податоците се валидни, односно дали сите
- вредности во движечкиот прозорец имаат иста класа
- :param i: моментален индекс од податочното множество
- :type i: int
- :param window_size: големина на движечкиот прозорец
- :type window_size: int
- :param y: листа со лабели (класи)
- :type y: list
- :return: True или False
- :rtype: bool
- """
- y_val = y[i - window_size]
- for j in range(i - window_size + 1, i):
- if y[j] != y_val:
- return False
- return True
- def generate_dataset(x, y, stat_config, w_long=0, w_short=0, shift=5):
- """Генерирање на податочното множество за тренирање со дрво на одлука
- :param x: податоци од временската серија
- :type x: np.array
- :param y: податоци за лабелите (класите)
- :type y: list
- :param stat_config: листа со имиња на статистики кои треба да се пресметаат
- :type stat_config: list(str)
- :param w_long: големина на големиот движечки прозорец
- :type w_long: int
- :param w_short: големина на малиот движечки прозорец
- :type w_short: int
- :param shift: поместување при движењето на лизгачките прозорци
- :type shift: int
- :return: податочно множество, индекси од временската серија кои се влезени во податочното множество
- :rtype: list(list), list(int)
- """
- rows = []
- indices = []
- for i in range(max(w_short, w_long), len(x), shift):
- row = []
- if is_valid_example(i, max(w_short, w_long), y):
- for j in range(x.shape[1]):
- if w_long != 0:
- x_window_long = x[i - w_long:i, j]
- all_long = stats_calculate_all(x_window_long, stat_config)
- row.extend(all_long)
- if w_short != 0:
- x_window_short = x[i - w_short:i, j]
- all_short = stats_calculate_all(x_window_short, stat_config)
- row.extend(all_short)
- indices.append(i - 1)
- row.append(y[i])
- rows.append(row)
- return rows, indices
- from math import log
- def unique_counts(rows):
- """Креирај броење на можни резултати (последната колона
- во секоја редица е класата)
- :param rows: dataset
- :type rows: list
- :return: dictionary of possible classes as keys and count
- as values
- :rtype: dict
- """
- results = {}
- for row in rows:
- # Клацата е последната колона
- r = row[len(row) - 1]
- if r not in results:
- results[r] = 0
- results[r] += 1
- return results
- def gini_impurity(rows):
- """Probability that a randomly placed item will
- be in the wrong category
- :param rows: dataset
- :type rows: list
- :return: Gini impurity
- :rtype: float
- """
- total = len(rows)
- counts = unique_counts(rows)
- imp = 0
- for k1 in counts:
- p1 = float(counts[k1]) / total
- for k2 in counts:
- if k1 == k2:
- continue
- p2 = float(counts[k2]) / total
- imp += p1 * p2
- return imp
- def entropy(rows):
- """Ентропијата е сума од p(x)log(p(x)) за сите
- можни резултати
- :param rows: податочно множество
- :type rows: list
- :return: вредност за ентропијата
- :rtype: float
- """
- log2 = lambda x: log(x) / log(2)
- results = unique_counts(rows)
- # Пресметка на ентропијата
- ent = 0.0
- for r in results.keys():
- p = float(results[r]) / len(rows)
- ent = ent - p * log2(p)
- return ent
- class DecisionNode:
- def __init__(self, col=-1, value=None, results=None, tb=None, fb=None):
- """
- :param col: индексот на колоната (атрибутот) од тренинг множеството
- која се претставува со оваа инстанца т.е. со овој јазол
- :type col: int
- :param value: вредноста на јазолот според кој се дели дрвото
- :param results: резултати за тековната гранка, вредност (различна
- од None) само кај јазлите-листови во кои се донесува
- одлуката.
- :type results: dict
- :param tb: гранка која се дели од тековниот јазол кога вредноста е
- еднаква на value
- :type tb: DecisionNode
- :param fb: гранка која се дели од тековниот јазол кога вредноста е
- различна од value
- :type fb: DecisionNode
- """
- self.col = col
- self.value = value
- self.results = results
- self.tb = tb
- self.fb = fb
- def compare_numerical(row, column, value):
- """Споредба на вредноста од редицата на посакуваната колона со
- зададена нумеричка вредност
- :param row: дадена редица во податочното множество
- :type row: list
- :param column: индекс на колоната (атрибутот) од тренирачкото множество
- :type column: int
- :param value: вредност на јазелот во согласност со кој се прави
- поделбата во дрвото
- :type value: int or float
- :return: True ако редицата >= value, инаку False
- :rtype: bool
- """
- return row[column] >= value
- def compare_nominal(row, column, value):
- """Споредба на вредноста од редицата на посакуваната колона со
- зададена номинална вредност
- :param row: дадена редица во податочното множество
- :type row: list
- :param column: индекс на колоната (атрибутот) од тренирачкото множество
- :type column: int
- :param value: вредност на јазелот во согласност со кој се прави
- поделбата во дрвото
- :type value: str
- :return: True ако редицата == value, инаку False
- :rtype: bool
- """
- return row[column] == value
- def divide_set(rows, column, value):
- """Поделба на множеството според одредена колона. Може да се справи
- со нумерички или номинални вредности.
- :param rows: тренирачко множество
- :type rows: list(list)
- :param column: индекс на колоната (атрибутот) од тренирачкото множество
- :type column: int
- :param value: вредност на јазелот во зависност со кој се прави поделбата
- во дрвото за конкретната гранка
- :type value: int or float or str
- :return: поделени подмножества
- :rtype: list, list
- """
- # Направи функција која ни кажува дали редицата е во
- # првата група (True) или втората група (False)
- if isinstance(value, int) or isinstance(value, float):
- # ако вредноста за споредба е од тип int или float
- split_function = compare_numerical
- else:
- # ако вредноста за споредба е од друг тип (string)
- split_function = compare_nominal
- # Подели ги редиците во две подмножества и врати ги
- # за секој ред за кој split_function враќа True
- set1 = [row for row in rows if
- split_function(row, column, value)]
- # set1 = []
- # for row in rows:
- # if not split_function(row, column, value):
- # set1.append(row)
- # за секој ред за кој split_function враќа False
- set2 = [row for row in rows if
- not split_function(row, column, value)]
- return set1, set2
- def build_tree(rows, scoref=entropy):
- """Градење на дрво на одлука.
- :param rows: тренирачко множество
- :type rows: list(list)
- :param scoref: функција за одбирање на најдобар атрибут во даден чекор
- :type scoref: function
- :return: коренот на изграденото дрво на одлука
- :rtype: DecisionNode object
- """
- if len(rows) == 0:
- return DecisionNode()
- current_score = scoref(rows)
- # променливи со кои следиме кој критериум е најдобар
- best_gain = 0.0
- best_criteria = None
- best_sets = None
- column_count = len(rows[0]) - 1
- for col in range(0, column_count):
- # за секоја колона (col се движи во интервалот од 0 до
- # column_count - 1)
- # Следниов циклус е за генерирање на речник од различни
- # вредности во оваа колона
- column_values = {}
- for row in rows:
- column_values[row[col]] = 1
- # за секоја редица се зема вредноста во оваа колона и се
- # поставува како клуч во column_values
- for value in column_values.keys():
- (set1, set2) = divide_set(rows, col, value)
- # Информациона добивка
- p = float(len(set1)) / len(rows)
- gain = current_score - p * scoref(set1) - (1 - p) * scoref(set2)
- if gain > best_gain and len(set1) > 0 and len(set2) > 0:
- best_gain = gain
- best_criteria = (col, value)
- best_sets = (set1, set2)
- # Креирај ги подгранките
- if best_gain > 0:
- true_branch = build_tree(best_sets[0], scoref)
- false_branch = build_tree(best_sets[1], scoref)
- return DecisionNode(col=best_criteria[0], value=best_criteria[1],
- tb=true_branch, fb=false_branch)
- else:
- return DecisionNode(results=unique_counts(rows))
- def print_tree(tree, indent=''):
- """Принтање на дрво на одлука
- :param tree: коренот на дрвото на одлучување
- :type tree: DecisionNode object
- :param indent:
- :return: None
- """
- # Дали е ова лист јазел?
- if tree.results:
- print(str(tree.results))
- else:
- # Се печати условот
- print(str(tree.col) + ':' + str(tree.value) + '? ')
- # Се печатат True гранките, па False гранките
- print(indent + 'T->', end='')
- print_tree(tree.tb, indent + ' ')
- print(indent + 'F->', end='')
- print_tree(tree.fb, indent + ' ')
- def classify(observation, tree):
- """Класификација на нов податочен примерок со изградено дрво на одлука
- :param observation: еден ред од податочното множество за предвидување
- :type observation: list
- :param tree: коренот на дрвото на одлучување
- :type tree: DecisionNode object
- :return: речник со класите како клуч и бројот на појавување во листот на дрвото
- за класификација како вредност во речникот
- :rtype: dict
- """
- if tree.results:
- return tree.results
- else:
- value = observation[tree.col]
- if isinstance(value, int) or isinstance(value, float):
- compare = compare_numerical
- else:
- compare = compare_nominal
- if compare(observation, tree.col, tree.value):
- branch = tree.tb
- else:
- branch = tree.fb
- return classify(observation, branch)
- labels = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,
- 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
- 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
- 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
- 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
- 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
- 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
- 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
- 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
- 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
- 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,
- 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
- 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1,
- 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
- 1, 1]
- data = [[0, 16, 4, 19, 500, 55, 4.8, 1018.8, -9.8, 0, 2.3], [1, 25, 3, 23, 1100, 34, 2.0, 1019.1, -8.4, 0, 1.6],
- [2, 34, 21, 22, 1100, 12, 1.0, 1019.3, -8.5, 0, 1.3], [3, 17, 7, 17, 700, 19, 0.8, 1019.6, -9.0, 0, 1.6],
- [4, 24, 6, 24, 700, 17, 0.1, 1020.0, -9.1, 0, 1.2], [5, 15, 7, 31, 600, 23, 1.9, 1020.5, -10.3, 0, 2.8],
- [6, 16, 8, 39, 800, 25, 1.4, 1021.4, -10.1, 0, 1.2], [7, 10, 8, 55, 900, 13, 0.7, 1022.5, -9.9, 0, 1.4],
- [8, 24, 10, 52, 900, 18, 2.8, 1023.6, -12.7, 0, 2.9], [9, 13, 12, 36, 700, 34, 4.7, 1024.4, -12.7, 0, 4.7],
- [10, 15, 8, 25, 600, 44, 5.9, 1024.9, -12.7, 0, 4.9], [11, 11, 7, 23, 500, 47, 6.8, 1025.1, -13.5, 0, 5.2],
- [12, 6, 5, 19, 400, 51, 8.4, 1024.7, -13.3, 0, 3.7], [13, 10, 4, 18, 400, 51, 9.0, 1024.0, -12.8, 0, 4.9],
- [14, 12, 5, 18, 300, 51, 9.7, 1023.4, -12.2, 0, 3.8], [15, 7, 5, 18, 400, 51, 9.6, 1023.4, -13.0, 0, 4.6],
- [16, 3, 4, 20, 400, 51, 9.5, 1023.4, -13.0, 0, 3.6], [17, 11, 6, 27, 500, 47, 7.8, 1023.4, -12.6, 0, 2.9],
- [18, 39, 23, 68, 1500, 15, 5.8, 1023.5, -11.8, 0, 1.1], [19, 55, 19, 82, 1400, 2, 4.4, 1023.3, -10.9, 0, 0.9],
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- [10, 13, 15, 24, 700, 37, 2.1, 1020.5, -12.9, 0, 2.0], [11, 11, 12, 19, 500, 42, 3.3, 1019.8, -14.0, 0, 3.3],
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- [4, 26, 8, 49, 1100, 3, -5.5, 1020.6, -11.8, 0, 0.4], [5, 36, 8, 62, 2500, 2, -6.2, 1020.4, -11.5, 0, 1.6],
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- [22, 75, 16, 71, 2000, 2, -2.3, 1019.4, -11.1, 0, 1.0], [23, 47, 11, 70, 1600, 2, -2.9, 1019.8, -10.9, 0, 0.7],
- [0, 65, 8, 69, 2100, 2, -3.6, 1020.1, -10.9, 0, 1.3], [1, 149, 17, 67, 4900, 4, -4.0, 1019.9, -10.6, 0, 1.1],
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- [10, 119, 42, 92, 3500, 3, -2.4, 1018.0, -10.0, 0, 0.6],
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- [19, 228, 43, 128, 4000, 3, -0.2, 1013.1, -7.3, 0, 1.6],
- [20, 222, 31, 125, 3900, 2, -0.3, 1013.5, -7.1, 0, 1.3],
- [21, 215, 31, 125, 4000, 2, -0.6, 1013.4, -6.8, 0, 1.1],
- [22, 246, 22, 125, 4700, 3, -1.1, 1013.9, -6.5, 0, 0.6],
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- [17, 22, 12, 67, 1000, 13, 1.3, 1020.5, -15.7, 0, 2.8], [18, 47, 15, 80, 1300, 5, 0.8, 1020.6, -14.8, 0, 1.6],
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- [17, 233, 29, 141, 4200, 2, 2.6, 1013.8, -6.5, 0, 0.8], [18, 250, 20, 137, 4800, 3, 0.9, 1014.2, -6.2, 0, 0.9],
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- [1, 132, 16, 49, 2300, 4, -2.2, 1015.5, -8.1, 0, 1.9], [2, 128, 35, 36, 1700, 2, -2.3, 1015.5, -8.2, 0, 1.9],
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- [13, 204, 40, 107, 3300, 11, 6.5, 1014.4, -5.5, 0, 1.6],
- [14, 210, 40, 118, 3300, 12, 7.9, 1014.0, -5.6, 0, 1.3],
- [15, 201, 40, 120, 3200, 14, 8.4, 1014.1, -5.1, 0, 1.3],
- [16, 205, 37, 121, 3200, 13, 7.8, 1014.6, -5.3, 0, 1.1], [17, 193, 21, 122, 3100, 5, 5.6, 1015.2, -5.1, 0, 1.1],
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- [2, 314, 4, 101, 6800, 3, -2.6, 1019.4, -6.2, 0, 1.2], [3, 362, 5, 105, 8500, 4, -3.4, 1019.5, -6.7, 0, 0.2],
- [4, 390, 6, 110, 'NA', 4, -3.8, 1018.8, -7.1, 0, 1.0], [5, 374, 4, 99, 8300, 2, -3.9, 1018.6, -6.8, 0, 0.9],
- [6, 364, 4, 96, 8600, 3, -3.2, 1019.1, -5.5, 0, 0.6], [7, 440, 5, 88, 'NA', 3, -3.6, 1018.8, -5.5, 0, 0.7],
- [8, 452, 6, 81, 'NA', 3, -4.0, 1019.8, -5.7, 0, 0.8], [9, 507, 9, 113, 'NA', 4, -3.0, 1020.6, -5.3, 0, 1.0],
- [10, 489, 11, 119, 'NA', 2, -1.9, 1020.7, -4.4, 0, 0.4], [11, 552, 22, 159, 'NA', 2, 1.2, 1020.6, -3.5, 0, 1.0],
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- [14, 346, 17, 154, 6300, 2, 2.6, 1018.3, -4.6, 0, 1.0], [15, 366, 21, 167, 6700, 2, 2.5, 1018.8, -4.9, 0, 1.0],
- [16, 322, 16, 151, 5700, 2, 2.2, 1018.8, -5.0, 0, 0.3], [17, 302, 13, 153, 6100, 2, 1.8, 1019.4, -4.5, 0, 0.9],
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- [22, 389, 8, 154, 8200, 2, -0.2, 1020.4, -2.9, 0, 1.2], [23, 402, 9, 142, 8300, 2, -0.3, 1020.7, -2.8, 0, 1.9],
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- [4, 335, 3, 75, 4400, 2, -1.6, 1018.6, -3.0, 0, 0.7], [5, 336, 3, 99, 6200, 2, -1.6, 1018.1, -3.0, 0, 0.3],
- [6, 323, 4, 99, 8200, 3, -1.8, 1017.8, -3.2, 0, 0.9], [7, 338, 5, 101, 9400, 4, -1.8, 1018.0, -3.2, 0, 0.9],
- [8, 392, 7, 112, 'NA', 5, -2.1, 1018.1, -3.5, 0, 1.2], [9, 392, 8, 124, 'NA', 4, -1.8, 1018.4, -3.1, 0, 0.9],
- [10, 398, 9, 127, 10000, 3, -0.8, 1018.5, -2.5, 0, 1.0], [11, 517, 18, 169, 'NA', 4, 1.1, 1017.7, -2.1, 0, 1.0],
- [12, 576, 20, 201, 'NA', 3, 1.9, 1016.4, -2.2, 0, 1.1], [13, 596, 22, 226, 'NA', 3, 3.3, 1015.0, -3.5, 0, 1.1],
- [14, 492, 21, 205, 8400, 3, 3.8, 1014.2, -3.0, 0, 1.2], [15, 388, 15, 172, 6800, 6, 3.9, 1014.4, -3.4, 0, 1.2],
- [16, 405, 14, 190, 7200, 3, 3.6, 1014.6, -3.2, 0, 0.8], [17, 415, 11, 196, 7500, 2, 3.4, 1014.7, -3.2, 0, 0.9],
- [18, 423, 10, 202, 7800, 3, 2.9, 1015.3, -3.0, 0, 1.0], [19, 432, 12, 199, 7900, 3, 2.9, 1015.4, -3.0, 0, 1.0],
- [20, 437, 15, 193, 8100, 4, 2.7, 1015.3, -3.2, 0, 0.8], [21, 439, 21, 196, 8500, 4, 2.7, 1015.0, -3.0, 0, 0.9],
- [22, 436, 22, 198, 8600, 3, 2.8, 1014.7, -3.1, 0, 0.9], [23, 421, 20, 183, 8300, 3, 2.6, 1014.5, -3.3, 0, 1.1],
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- [2, 18, 10, 42, 1200, 20, 5.1, 1014.0, -8.4, 0, 4.7], [3, 14, 7, 35, 1100, 28, 4.0, 1014.1, -9.1, 0, 4.7],
- [4, 12, 3, 25, 800, 38, 3.3, 1014.2, -10.0, 0, 4.8], [5, 13, 2, 20, 700, 43, 2.1, 1014.7, -11.1, 0, 4.5],
- [6, 12, 2, 18, 600, 45, 1.0, 1015.2, -11.8, 0, 1.7], [7, 21, 2, 22, 700, 41, 1.0, 1015.9, -12.1, 0, 2.8],
- [8, 12, 2, 21, 600, 41, 0.7, 1016.1, -12.4, 0, 3.8], [9, 19, 2, 20, 600, 40, 0.8, 1016.2, -12.3, 0, 2.0],
- [10, 18, 2, 23, 600, 39, 2.3, 1015.9, -12.7, 0, 5.0], [11, 15, 3, 27, 600, 39, 3.0, 1015.2, -12.5, 0, 6.3],
- [12, 17, 2, 25, 600, 43, 3.5, 1014.4, -13.3, 0, 5.9], [13, 18, 2, 23, 500, 46, 4.2, 1013.4, -12.7, 0, 6.1],
- [14, 12, 2, 23, 500, 46, 4.6, 1012.7, -12.8, 0, 5.7], [15, 13, 3, 29, 600, 42, 4.8, 1012.7, -13.1, 0, 5.8],
- [16, 8, 2, 28, 500, 43, 4.3, 1013.1, -13.1, 0, 5.6], [17, 'NA', 2, 23, 900, 'NA', 3.0, 1013.8, -12.9, 0, 3.7],
- [18, 7, 2, 15, 100, 34, 2.4, 1014.8, -13.4, 0, 2.5], [19, 13, 2, 23, 100, 52, 1.3, 1016.1, -14.4, 0, 2.3],
- [20, 14, 2, 17, 100, 59, 0.1, 1016.9, -14.3, 0, 1.6], [21, 18, 2, 26, 100, 49, 0.3, 1017.6, -14.1, 0, 2.2],
- [22, 11, 2, 21, 100, 53, -0.7, 1018.2, -14.3, 0, 1.2], [23, 17, 2, 21, 100, 51, -1.2, 1018.9, -13.4, 0, 1.0],
- [0, 19, 2, 27, 100, 44, -1.0, 1019.1, -13.9, 0, 1.6], [1, 13, 2, 18, 100, 38, -2.2, 1019.3, -13.4, 0, 1.1],
- [2, 21, 28, 15, 100, 19, -2.8, 1019.6, -13.1, 0, 1.1], [3, 17, 10, 16, 500, 24, -2.8, 1019.9, -13.1, 0, 0.9],
- [4, 9, 6, 15, 300, 24, -3.2, 1020.2, -13.2, 0, 1.0], [5, 10, 6, 26, 400, 24, -2.5, 1020.0, -13.4, 0, 2.3],
- [6, 11, 8, 27, 600, 34, -2.7, 1020.2, -13.3, 0, 1.9], [7, 12, 9, 33, 700, 29, -2.8, 1020.8, -13.1, 0, 2.8],
- [8, 13, 9, 30, 700, 31, -3.1, 1022.1, -13.1, 0, 1.4], [9, 16, 9, 24, 600, 37, -3.5, 1022.8, -12.9, 0, 1.9],
- [10, 33, 13, 40, 1100, 24, -1.8, 1023.2, -13.3, 0, 2.1], [11, 30, 9, 24, 700, 38, -0.1, 1022.8, -13.8, 0, 1.6],
- [12, 21, 8, 21, 500, 41, 1.4, 1021.8, -13.2, 0, 1.2], [13, 25, 7, 23, 500, 41, 2.0, 1020.5, -13.4, 0, 1.8],
- [14, 22, 7, 28, 400, 37, 2.7, 1019.9, -13.6, 0, 2.3], [15, 14, 8, 29, 500, 39, 3.3, 1019.9, -13.1, 0, 1.8],
- [16, 21, 9, 31, 500, 39, 2.9, 1020.3, -13.0, 0, 1.5], [17, 20, 10, 44, 600, 26, 1.5, 1020.3, -12.7, 0, 1.1],
- [18, 27, 8, 52, 800, 16, -0.4, 1020.5, -12.7, 0, 1.6], [19, 58, 10, 67, 1400, 4, -0.5, 1020.8, -12.5, 0, 1.4],
- [20, 60, 10, 63, 1500, 2, -1.5, 1021.1, -11.9, 0, 0.9], [21, 56, 9, 64, 1500, 2, -2.5, 1021.4, -12.3, 0, 0.6],
- [22, 49, 9, 55, 1400, 3, -2.2, 1021.6, -11.8, 0, 0.7], [23, 43, 7, 53, 1200, 4, -2.9, 1022.4, -11.9, 0, 1.0],
- [0, 66, 7, 66, 2000, 2, -2.8, 1022.1, -11.5, 0, 1.1], [1, 72, 9, 70, 2100, 2, -2.7, 1021.7, -11.0, 0, 0.8],
- [2, 83, 13, 72, 2500, 2, -3.4, 1021.9, -10.9, 0, 0.8], [3, 90, 19, 75, 2900, 2, -3.8, 1021.8, -10.7, 0, 0.9],
- [4, 80, 16, 70, 2800, 2, -4.4, 1021.6, -10.6, 0, 0.6], [5, 73, 11, 68, 2600, 2, -5.5, 1021.1, -10.7, 0, 1.3],
- [6, 77, 9, 75, 2200, 2, -5.5, 1021.1, -10.7, 0, 0.9], [7, 69, 7, 59, 1600, 2, -5.8, 1021.6, -11.0, 0, 1.2],
- [8, 78, 8, 64, 2100, 2, -6.1, 1021.8, -10.7, 0, 0.9], [9, 80, 10, 61, 1900, 2, -4.9, 1022.2, -10.3, 0, 1.0],
- [10, 85, 17, 63, 2200, 2, -2.4, 1022.4, -10.0, 0, 0.7], [11, 92, 22, 71, 2300, 2, -1.2, 1022.0, -11.1, 0, 1.7],
- [12, 89, 25, 73, 1900, 3, -0.4, 1021.5, -10.6, 0, 1.3], [13, 100, 29, 77, 2000, 3, 0.2, 1020.3, -9.8, 0, 1.8],
- [14, 122, 32, 95, 2400, 2, 0.9, 1020.1, -8.9, 0, 1.4], [15, 123, 27, 95, 2200, 2, 0.9, 1019.8, -8.9, 0, 1.7],
- [16, 131, 23, 100, 2200, 2, 0.7, 1019.8, -8.8, 0, 1.0], [17, 166, 17, 115, 2800, 2, -0.5, 1020.4, -8.7, 0, 1.1],
- [18, 198, 26, 121, 3800, 2, -1.7, 1020.6, -8.9, 0, 0.9],
- [19, 181, 24, 115, 3500, 2, -2.7, 1020.8, -8.8, 0, 0.7],
- [20, 222, 21, 121, 5100, 3, -3.5, 1021.0, -8.8, 0, 0.9],
- [21, 237, 26, 116, 5200, 2, -3.9, 1021.5, -8.8, 0, 0.7], [22, 154, 14, 92, 2800, 2, -3.8, 1021.7, -8.5, 0, 1.5],
- [23, 152, 12, 93, 2600, 2, -3.9, 1021.7, -8.6, 0, 1.6], [0, 183, 11, 101, 4200, 2, -5.1, 1021.2, -9.0, 0, 1.4],
- [1, 163, 9, 76, 3200, 2, -4.9, 1020.9, -8.7, 0, 1.1], [2, 131, 41, 49, 1800, 2, -5.0, 1021.1, -8.9, 0, 1.3],
- [3, 163, 12, 52, 2700, 2, -4.9, 1020.9, -8.8, 0, 1.3], [4, 127, 6, 58, 1400, 2, -5.4, 1020.5, -9.1, 0, 1.1],
- [5, 111, 5, 75, 1500, 2, -5.1, 1020.2, -9.0, 0, 0.7], [6, 99, 8, 75, 1900, 2, -4.8, 1020.1, -9.1, 0, 1.5],
- [7, 121, 12, 81, 2400, 2, -4.8, 1020.4, -8.9, 0, 1.2], [8, 112, 12, 76, 2000, 2, -4.2, 1020.4, -8.9, 0, 0.8],
- [9, 101, 12, 73, 1900, 2, -4.0, 1021.0, -8.5, 0, 1.1], [10, 111, 15, 75, 2400, 2, -2.4, 1021.2, -8.1, 0, 0.1],
- [11, 117, 23, 81, 2300, 2, -0.6, 1020.8, -7.2, 0, 0.9], [12, 135, 24, 86, 2400, 3, 0.2, 1019.9, -7.3, 0, 1.0],
- [13, 144, 22, 91, 2500, 3, 0.7, 1018.7, -7.8, 0, 1.1], [14, 167, 27, 105, 3000, 2, 1.1, 1017.9, -7.9, 0, 0.9],
- [15, 171, 30, 107, 2900, 2, 1.3, 1017.5, -7.7, 0, 1.5], [16, 145, 24, 93, 2200, 3, 0.9, 1017.7, -7.4, 0, 0.9],
- [17, 138, 16, 93, 2200, 2, -0.1, 1018.0, -7.6, 0, 0.9], [18, 149, 11, 93, 2400, 2, -0.3, 1018.9, -6.7, 0, 0.7],
- [19, 163, 19, 95, 2700, 2, -0.5, 1018.9, -7.1, 0, 1.1], [20, 169, 20, 95, 2600, 2, -0.4, 1019.3, -6.8, 0, 0.9],
- [21, 164, 20, 92, 2700, 2, -0.4, 1019.7, -6.6, 0, 0.9], [22, 222, 31, 107, 4300, 3, -0.3, 1019.8, -6.9, 0, 0.7],
- [23, 197, 34, 105, 3900, 2, -0.1, 1019.9, -6.5, 0, 0.8], [0, 232, 43, 114, 4900, 3, -0.3, 1020.2, -6.5, 0, 1.5],
- [1, 184, 48, 112, 3700, 2, -0.2, 1019.9, -6.4, 0, 1.5], [2, 204, 40, 94, 4400, 2, 1.1, 1020.3, -6.5, 0, 1.1],
- [3, 46, 23, 46, 1100, 9, 3.1, 1020.4, -9.2, 0, 2.4], [4, 35, 16, 38, 900, 17, 2.8, 1020.7, -9.8, 0, 2.5],
- [5, 31, 15, 39, 800, 15, 2.2, 1021.0, -9.1, 0, 1.9], [6, 29, 14, 33, 700, 20, 2.2, 1021.3, -9.4, 0, 1.9],
- [7, 23, 14, 33, 700, 23, 2.0, 1021.3, -9.9, 0, 1.5], [8, 26, 15, 36, 700, 22, 1.8, 1022.2, -9.8, 0, 0.3],
- [9, 25, 16, 37, 800, 21, 2.1, 1023.3, -10.8, 0, 1.4], [10, 25, 16, 35, 700, 30, 2.4, 1024.1, -11.2, 0, 1.7],
- [11, 22, 16, 27, 600, 40, 2.9, 1023.7, -12.6, 0, 2.2], [12, 18, 11, 23, 500, 46, 3.2, 1023.1, -13.6, 0, 4.9],
- [13, 11, 9, 22, 400, 48, 4.0, 1022.1, -13.8, 0, 4.2], [14, 11, 9, 22, 400, 48, 3.9, 1021.2, -14.9, 0, 4.0],
- [15, 9, 9, 25, 400, 45, 3.9, 1021.6, -15.4, 0, 4.9], [16, 13, 8, 23, 400, 47, 3.2, 1022.1, -15.5, 0, 3.8],
- [19, 13, 'NA', 'NA', 'NA', 'NA', -0.6, 1023.4, -14.6, 0, 0.9],
- [20, 39, 22, 65, 1500, 12, -0.6, 1023.4, -17.3, 0, 1.6],
- [21, 34, 23, 57, 1400, 14, -0.9, 1023.8, -18.0, 0, 1.3],
- [22, 20, 14, 33, 1100, 35, -1.5, 1024.4, -17.7, 0, 1.6], [23, 12, 12, 20, 900, 49, -1.8, 1024.3, -17.9, 0, 1.7],
- [0, 17, 15, 24, 1000, 43, -2.3, 1024.7, -17.6, 0, 1.3], [1, 19, 18, 27, 1000, 28, -2.9, 1024.9, -16.6, 0, 1.4],
- [2, 20, 25, 17, 800, 17, -3.5, 1024.9, -15.8, 0, 1.0], [3, 36, 14, 40, 1300, 2, -4.1, 1025.5, -15.1, 0, 1.1],
- [4, 30, 5, 17, 400, 'NA', -3.4, 1025.3, -13.7, 0, 1.7], [5, 22, 9, 28, 500, 22, -2.9, 1025.6, -14.9, 0, 2.2],
- [6, 15, 8, 23, 600, 37, -3.7, 1026.3, -15.4, 0, 1.9], [7, 21, 9, 30, 700, 30, -3.7, 1026.6, -15.4, 0, 3.2],
- [8, 17, 10, 33, 800, 28, -3.3, 1027.5, -15.9, 0, 2.4], [9, 23, 11, 32, 800, 29, -2.5, 1028.4, -16.2, 0, 3.6],
- [10, 23, 8, 22, 600, 37, -1.0, 1028.5, -16.8, 0, 4.8], [11, 14, 6, 15, 500, 42, 0.0, 1028.0, -16.4, 0, 4.8],
- [12, 16, 5, 15, 400, 41, 0.5, 1027.1, -16.4, 0, 5.7], [13, 20, 5, 14, 400, 44, 1.5, 1025.6, -16.4, 0, 4.5],
- [14, 15, 6, 15, 300, 45, 2.3, 1024.4, -16.2, 0, 4.7], [15, 18, 6, 14, 300, 48, 2.8, 1023.4, -16.3, 0, 2.9],
- [16, 17, 6, 15, 400, 49, 2.6, 1023.2, -17.0, 0, 1.7], [17, 15, 5, 14, 300, 49, 0.8, 1023.0, -16.6, 0, 1.1],
- [18, 23, 12, 36, 800, 30, 0.4, 1022.9, -14.0, 0, 2.1], [19, 76, 15, 82, 1500, 4, -2.4, 1022.9, -14.2, 0, 1.2],
- [20, 84, 15, 76, 2000, 3, -3.3, 1023.2, -13.8, 0, 0.8], [21, 97, 12, 77, 2000, 3, -3.7, 1022.9, -12.9, 0, 0.7],
- [22, 109, 9, 75, 1900, 3, -4.5, 1023.1, -12.4, 0, 1.1], [23, 119, 8, 72, 1900, 3, -4.6, 1022.8, -11.2, 0, 1.5],
- [0, 93, 9, 45, 1500, 14, -4.9, 1022.5, -12.1, 0, 0.7], [1, 85, 7, 35, 1500, 11, -5.9, 1022.0, -12.6, 0, 1.8],
- [2, 79, 44, 29, 1100, 2, -6.4, 1021.6, -12.3, 0, 1.0], [3, 80, 8, 29, 1100, 2, -7.1, 1021.8, -12.0, 0, 1.0],
- [4, 77, 2, 23, 600, 49, -6.6, 1021.2, -12.5, 0, 1.3], [5, 79, 2, 54, 1000, 2, -6.8, 1020.7, -12.7, 0, 0.8],
- [6, 71, 3, 55, 1300, 2, -7.2, 1021.1, -13.1, 0, 1.6], [7, 68, 8, 57, 1500, 4, -6.8, 1021.6, -13.7, 0, 1.7],
- [8, 48, 22, 57, 1500, 5, -5.0, 1022.5, -14.9, 0, 1.9], [9, 38, 24, 48, 1400, 13, -3.1, 1023.2, -15.8, 0, 2.9],
- [10, 21, 15, 33, 1000, 30, -1.2, 1024.0, -17.0, 0, 6.1], [11, 5, 8, 23, 600, 40, -0.5, 1024.1, -17.7, 0, 6.0],
- [12, 12, 7, 22, 500, 40, 0.7, 1023.7, -18.1, 0, 5.8], [13, 14, 7, 22, 400, 40, -0.6, 1023.6, -18.2, 0, 5.7],
- [14, 20, 7, 21, 400, 41, 0.1, 1023.3, -18.1, 0, 4.3], [15, 7, 6, 20, 400, 42, 0.6, 1023.3, -18.2, 0, 4.9],
- [16, 5, 6, 21, 400, 41, 1.2, 1023.4, -19.3, 0, 5.0], [17, 14, 8, 33, 500, 30, 0.1, 1024.2, -19.2, 0, 4.2],
- [18, 8, 5, 21, 500, 43, -0.8, 1025.6, -19.9, 0, 3.9], [19, 8, 6, 22, 500, 43, -1.3, 1026.5, -19.3, 0, 3.4],
- [20, 7, 5, 19, 400, 46, -2.6, 1027.2, -19.1, 0, 1.6], [21, 11, 5, 17, 400, 47, -3.0, 1027.5, -19.0, 0, 1.2],
- [22, 13, 7, 19, 700, 43, -4.0, 1027.7, -18.7, 0, 2.0], [23, 22, 8, 21, 700, 39, -2.8, 1028.0, -20.1, 0, 4.7],
- [0, 8, 6, 17, 500, 43, -3.0, 1028.2, -20.3, 0, 4.6], [1, 8, 6, 17, 600, 42, -3.0, 1028.0, -19.9, 0, 4.8],
- [2, 6, 6, 15, 500, 44, -2.9, 1027.7, -19.8, 0, 5.8], [3, 5, 4, 16, 500, 42, -3.0, 1028.1, -20.3, 0, 5.7],
- [4, 6, 5, 17, 600, 40, -3.2, 1027.8, -20.0, 0, 3.9], [5, 8, 8, 18, 700, 40, -4.5, 1026.9, -19.5, 0, 3.2],
- [6, 13, 8, 22, 700, 37, -4.9, 1027.1, -19.5, 0, 2.4], [7, 16, 9, 26, 700, 34, -4.9, 1027.2, -19.5, 0, 3.6],
- [8, 13, 8, 23, 700, 38, -5.0, 1027.4, -19.9, 0, 1.8], [9, 18, 12, 29, 900, 34, -4.0, 1027.7, -19.5, 0, 1.9],
- [10, 19, 12, 23, 800, 38, -0.9, 1027.9, -20.0, 0, 1.0], [11, 34, 16, 50, 1100, 16, -0.3, 1027.2, -19.0, 0, 1.6],
- [12, 38, 22, 52, 1100, 19, 0.9, 1026.0, -19.6, 0, 1.9], [13, 21, 17, 44, 900, 26, 1.9, 1024.6, -19.4, 0, 2.7],
- [14, 25, 3, 17, 600, 11, 2.4, 1023.8, -18.9, 0, 2.8], [15, 36, 3, 2, 'NA', 2, 2.7, 1023.3, -17.0, 0, 2.7],
- [16, 45, 4, 2, 100, 2, 1.7, 1023.0, -16.3, 0, 2.3], [17, 56, 3, 3, 'NA', 47, 0.4, 1022.9, -15.6, 0, 2.2],
- [18, 65, 3, 3, 'NA', 47, -0.6, 1023.3, -14.9, 0, 1.2], [19, 80, 3, 3, 'NA', 47, -2.6, 1023.5, -15.0, 0, 1.0],
- [20, 91, 'NA', 56, 'NA', 'NA', -3.1, 1023.6, -15.1, 0, 0.9],
- [21, 68, 11, 68, 1700, 5, -4.4, 1023.5, -15.7, 0, 1.4], [22, 73, 13, 64, 1700, 6, -4.4, 1023.2, -15.7, 0, 0.8],
- [23, 68, 9, 63, 1800, 5, -5.7, 1023.3, -15.5, 0, 1.4], [0, 75, 10, 55, 1900, 8, -6.0, 1023.3, -15.5, 0, 0.8],
- [1, 59, 10, 42, 1400, 14, -6.2, 1022.9, -16.0, 0, 1.6], [2, 54, 32, 29, 1000, 7, -6.7, 1022.5, -15.9, 0, 1.5],
- [3, 54, 10, 28, 1000, 3, -7.3, 1022.2, -16.0, 0, 0.4], [4, 62, 2, 24, 900, 'NA', -6.9, 1021.7, -15.6, 0, 1.1],
- [5, 79, 3, 62, 1900, 2, -8.2, 1021.4, -15.7, 0, 1.6], [6, 91, 11, 69, 3000, 3, -7.8, 1020.9, -15.7, 0, 1.4],
- [7, 69, 8, 62, 2000, 2, -9.0, 1020.9, -15.6, 0, 0.9], [8, 88, 9, 64, 3100, 3, -8.1, 1021.8, -13.9, 0, 1.1],
- [9, 101, 12, 70, 3200, 3, -7.4, 1022.0, -14.7, 0, 1.7], [10, 116, 34, 83, 3400, 3, -5.0, 1022.0, -14.3, 0, 1.0],
- [11, 136, 46, 91, 3500, 4, -2.2, 1021.2, -13.4, 0, 1.9],
- [12, 148, 45, 105, 3700, 3, -1.4, 1020.0, -13.3, 0, 1.5],
- [13, 149, 54, 111, 3500, 3, -0.4, 1018.8, -13.3, 0, 0.9],
- [14, 154, 58, 118, 3600, 3, 0.8, 1018.0, -13.0, 0, 1.3],
- [15, 138, 53, 108, 3000, 3, 0.8, 1017.8, -13.7, 0, 1.6], [16, 120, 44, 99, 2400, 4, 0.6, 1017.9, -13.9, 0, 0.9],
- [17, 109, 36, 96, 2100, 2, -0.6, 1017.7, -13.5, 0, 0.7],
- [18, 118, 30, 99, 2400, 2, -1.4, 1018.2, -13.6, 0, 0.9],
- [19, 142, 31, 103, 2900, 3, -2.0, 1018.3, -12.7, 0, 0.5],
- [20, 157, 31, 103, 3500, 3, -2.4, 1018.7, -13.0, 0, 0.9],
- [21, 150, 23, 102, 3400, 3, -3.3, 1018.9, -12.8, 0, 0.9],
- [22, 143, 25, 96, 3500, 2, -4.7, 1018.6, -13.1, 0, 1.3],
- [23, 128, 26, 88, 3100, 2, -5.4, 1018.6, -13.5, 0, 0.9], [0, 121, 24, 85, 3000, 2, -5.7, 1018.8, -13.5, 0, 0.9],
- [1, 122, 22, 84, 2900, 2, -4.6, 1018.4, -13.2, 0, 1.1], [2, 112, 21, 87, 2600, 2, -4.8, 1018.4, -13.2, 0, 1.1],
- [3, 116, 22, 92, 2900, 2, -5.4, 1018.4, -13.0, 0, 1.3], [4, 116, 15, 88, 2800, 2, -6.2, 1018.0, -12.9, 0, 1.7],
- [5, 116, 15, 89, 3000, 2, -6.7, 1017.8, -13.0, 0, 0.6], [6, 119, 12, 89, 3000, 2, -7.2, 1017.8, -12.9, 0, 1.8],
- [7, 126, 9, 88, 3100, 2, -6.8, 1018.3, -12.5, 0, 1.1], [8, 139, 9, 90, 3500, 2, -6.4, 1018.7, -11.7, 0, 0.9],
- [9, 162, 12, 91, 4100, 2, -6.5, 1019.1, -12.2, 0, 1.4],
- [10, 213, 38, 113, 5900, 4, -3.2, 1019.3, -11.0, 0, 1.2],
- [11, 265, 53, 127, 5900, 3, -1.6, 1019.0, -10.9, 0, 1.3],
- [12, 261, 48, 133, 4800, 2, 0.5, 1017.9, -9.8, 0, 0.9], [13, 284, 41, 135, 3900, 3, 1.8, 1017.1, -9.2, 0, 1.7],
- [14, 284, 42, 144, 3700, 4, 2.5, 1016.3, -9.4, 0, 1.1], [15, 295, 45, 154, 4100, 3, 2.7, 1016.3, -9.3, 0, 1.3],
- [16, 300, 44, 159, 4200, 2, 2.3, 1016.4, -8.7, 0, 1.1], [17, 294, 30, 164, 4200, 2, 1.0, 1016.3, -8.8, 0, 1.0],
- [18, 292, 26, 164, 4500, 2, -1.3, 1016.7, -9.2, 0, 1.3],
- [19, 283, 23, 151, 4700, 3, -2.2, 1017.0, -10.0, 0, 1.2],
- [20, 259, 29, 133, 4500, 3, -2.5, 1017.1, -10.3, 0, 1.4],
- [21, 226, 24, 126, 4100, 2, -3.3, 1017.2, -10.6, 0, 1.4],
- [22, 207, 27, 123, 3900, 2, -3.7, 1017.2, -10.6, 0, 0.9],
- [23, 196, 19, 119, 3400, 2, -4.3, 1017.3, -10.7, 0, 1.5]]
- if __name__ == '__main__':
- n = int(input())
- stat = input()
- x = np.array(data)
- for i in reversed(range(0, len(x))):
- for j in range(x.shape[1]):
- pom = []
- if x[i, j] == 'NA':
- window_long = x[i - 10:i, j]
- for el in window_long:
- if el == 'NA':
- continue
- else:
- pom.append(el)
- new_window_long = np.array(pom).astype(float)
- s1 = stats_calculate_all(new_window_long, stat_config=[stat])[0]
- x[i, j] = s1
- final = np.array(x).astype(float)
- dataset, indices = generate_dataset(final, labels, ['mean', 'max'], 8, 3, 1)
- pos = [row for row in dataset if row[-1] == 1]
- neg = [row for row in dataset if row[-1] == 0]
- delba_pos = int(len(pos) * (n / 100))
- delba_neg = int(len(neg) * (n / 100))
- train_dataset = pos[:delba_pos] + neg[:delba_neg]
- test_dataset = pos[delba_pos:] + neg[delba_neg:]
- tree = build_tree(train_dataset)
- tp, fp, tn, fn = 0, 0, 0, 0
- for red in test_dataset:
- c = max(classify(red[:-1], tree).items(), key=lambda x: x[1])[0]
- if red[-1] == 1:
- if c == red[-1]:
- tp += 1
- else:
- fn += 1
- else:
- if c == red[-1]:
- tn += 1
- else:
- fp += 1
- tocnhnost = (tp + tn) / (tp + tn + fp + fn)
- preciznost = tp / (tp + fp)
- odziv = tp / (tp + fn)
- print(f'Tochnost: {tocnhnost}')
- print(f'Preciznost: {preciznost}')
- print(f'Odziv: {odziv}')
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