init
ADITYA VARMA
2 years ago
commit
54aea6a4f4
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/.venv/
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/.idea/
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from random import seed
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from random import randrange
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import random
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from csv import reader
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from tabulate import tabulate
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def load_csv(filename, skip = False):
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dataset = []
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with open(filename, newline='') as file:
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csv_reader = reader(file)
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if skip:
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next(csv_reader)
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for row in csv_reader:
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if row:
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dataset.append(row)
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return dataset
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def print_the_dataset(dataset, contents=True, length=True):
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if (contents):
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print(tabulate(dataset))
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if (length):
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print(len(dataset))
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def train_test_split(dataset, split):
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# Create an empty list for the training set
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train_set = []
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# Define the size of the training set
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train_size = int(split * len(dataset))
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# Copy the original dataset to
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dataset_copy = dataset.copy()
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# Loops only to the size of the training set
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while len(train_set) < train_size:
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index = randrange(len(dataset_copy))
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# Populate the training set, by moving the data points from the
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# dataset/test set to the training set
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train_set.append(dataset_copy.pop(index))
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# Return both the training set and test set
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return train_set, dataset_copy
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def k_fold_cross_validation(dataset, k):
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n = len(dataset) # Length of the dataset
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fold_size = n // k # Divide the length into smaller folds
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folds = [] # Empty list of folds
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# Shuffle the dataset
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shuffled_dataset = dataset.copy()
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random.shuffle(shuffled_dataset)
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for i in range(k):
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# Assign a start and end variables in respect to the fold size
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start = i * fold_size
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end = start + fold_size
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# Generate all the test indices for the current fold
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test_indices = shuffled_dataset[start:end]
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# Generate all the train indices for the all other folds
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train_indices = shuffled_dataset[:start] + shuffled_dataset[end:]
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# Create a test set that is randomly populated via the test_indices
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test_set = test_indices
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# Create a train set that is randomly populated via the train_indices
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train_set = train_indices
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folds.append((train_set, test_set))
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return folds
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seed(1)
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filename = 'big_heart.csv'
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dataset = load_csv(filename, skip = True)
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print_the_dataset(dataset)
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training, test = train_test_split(dataset, 0.8)
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print(len(training))
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print(len(test))
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k = 5 # Number of folds for cross-validation
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folds = k_fold_cross_validation(dataset, k)
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# Print the size of each fold
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for i, fold in enumerate(folds):
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train_set, test_set = fold
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print(f"Fold {i+1}: Training set size: {len(train_set)}, Test set size: {len(test_set)}")
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@ -0,0 +1,150 @@
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from math import sqrt
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from matplotlib import pyplot as plot
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from random import seed
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from random import randrange
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from csv import reader
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def load_csv(filename, skip=False):
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dataset = list()
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with open(filename, newline='') as file:
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csv_reader = reader(file)
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if skip:
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next(csv_reader)
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for row in csv_reader:
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if not row:
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continue
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dataset.append(row)
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return dataset
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def string_column_to_float(dataset, column):
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for row in dataset:
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# The strip() function remove white space
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# then convert the data into a decimal number (float)
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# and overwrite the original data
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row[column] = float(row[column].strip())
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def mean(values):
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mean_results = 0.0
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mean_results = sum(values) / float(len(values))
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return mean_results
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def regularisation(parameter, lambda_value=0.01):
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parameter = parameter * (1 - lambda_value)
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return parameter
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def leastSquares(dataset):
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x = list()
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y = list()
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for row in dataset:
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x.append(row[0])
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for row in dataset:
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y.append(row[1])
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b0 = 0
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b1 = 0
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# using the formula to calculate the b1 and b0
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numerator = 0
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denominator = 0
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x_mean = mean(x)
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y_mean = mean(y)
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numerator = sum((x[i] - x_mean) * (y[i] - y_mean) for i in range(len(x)))
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denominator = sum((x[i] - x_mean) ** 2 for i in range(len(x)))
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b1 = numerator / denominator
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b0 = y_mean - b1 * x_mean
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return [b0, b1]
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def root_mean_square_error(actual, predicted):
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rmse = 0.0
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sum_error = 0.0
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sum_error = sum((predicted[i] - actual[i]) ** 2 for i in range(len(actual)))
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rmse = sqrt(sum_error / len(actual))
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return rmse
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def simple_linear_regression(train, test):
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predictions = list()
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b0, b1 = leastSquares(train)
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# Calculate the prediction (yhat)
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for row in test:
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yhat = b0 + b1 * row[0]
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predictions.append(yhat)
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return predictions
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def train_test_split(dataset, split):
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train = list()
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test = list(dataset)
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train_size = int(split * len(dataset))
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while len(train) < train_size:
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index = randrange(len(test))
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train.append(test.pop(index))
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return train, test
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def evaluate_simple_linear_regression(dataset, split=0):
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train, test = train_test_split(dataset, split)
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test_set = list()
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for row in test:
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row_copy = list(row)
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row_copy[-1] = None
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test_set.append(row_copy)
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predicted = simple_linear_regression(train, test_set)
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actual = [row[-1] for row in test]
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rmse = root_mean_square_error(actual, predicted)
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return rmse
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def visualise_dataset(dataset):
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test_set = list()
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for row in dataset:
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row_copy = list(row)
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row_copy[-1] = None
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test_set.append(row_copy)
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sizes, prices = [], []
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for i in range(len(dataset)):
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sizes.append(dataset[i][0])
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prices.append(dataset[i][1])
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plot.figure()
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plot.plot(sizes, prices, 'x')
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plot.plot(test_set, simple_linear_regression(dataset, test_set))
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plot.xlabel('Fertility rate')
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plot.ylabel('Worker percent')
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plot.grid()
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plot.tight_layout()
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plot.show()
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seed(1)
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filename = 'fertility_rate-worker_percent.csv'
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dataset = load_csv(filename, skip=True)
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for i in range(len(dataset[0])):
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string_column_to_float(dataset, i)
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split = 0.6
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rmse = evaluate_simple_linear_regression(dataset, split)
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print('Root Mean Square Error: %.3f' % rmse)
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visualise_dataset(dataset)
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@ -0,0 +1,206 @@
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age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,target
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43,1,2,130,315,0,1,162,0,1.9,2,1,2,1
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53,1,2,130,246,1,0,173,0,0,2,3,2,1
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42,1,3,148,244,0,0,178,0,0.8,2,2,2,1
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59,1,3,178,270,0,0,145,0,4.2,0,0,3,1
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63,0,1,140,195,0,1,179,0,0,2,2,2,1
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42,1,2,120,240,1,1,194,0,0.8,0,0,3,1
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50,1,2,129,196,0,1,163,0,0,2,0,2,1
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68,0,2,120,211,0,0,115,0,1.5,1,0,2,1
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69,1,3,160,234,1,0,131,0,0.1,1,1,2,1
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45,0,0,138,236,0,0,152,1,0.2,1,0,2,1
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||||
50,0,1,120,244,0,1,162,0,1.1,2,0,2,1
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||||
50,0,0,110,254,0,0,159,0,0,2,0,2,1
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||||
64,0,0,180,325,0,1,154,1,0,2,0,2,1
|
||||
57,1,2,150,126,1,1,173,0,0.2,2,1,3,1
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||||
64,0,2,140,313,0,1,133,0,0.2,2,0,3,1
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||||
43,1,0,110,211,0,1,161,0,0,2,0,3,1
|
||||
55,1,1,130,262,0,1,155,0,0,2,0,2,1
|
||||
37,0,2,120,215,0,1,170,0,0,2,0,2,1
|
||||
41,1,2,130,214,0,0,168,0,2,1,0,2,1
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||||
56,1,3,120,193,0,0,162,0,1.9,1,0,3,1
|
||||
46,0,1,105,204,0,1,172,0,0,2,0,2,1
|
||||
46,0,0,138,243,0,0,152,1,0,1,0,2,1
|
||||
64,0,0,130,303,0,1,122,0,2,1,2,2,1
|
||||
59,1,0,138,271,0,0,182,0,0,2,0,2,1
|
||||
41,0,2,112,268,0,0,172,1,0,2,0,2,1
|
||||
54,0,2,108,267,0,0,167,0,0,2,0,2,1
|
||||
39,0,2,94,199,0,1,179,0,0,2,0,2,1
|
||||
34,0,1,118,210,0,1,192,0,0.7,2,0,2,1
|
||||
47,1,0,112,204,0,1,143,0,0.1,2,0,2,1
|
||||
67,0,2,152,277,0,1,172,0,0,2,1,2,1
|
||||
52,0,2,136,196,0,0,169,0,0.1,1,0,2,1
|
||||
74,0,1,120,269,0,0,121,1,0.2,2,1,2,1
|
||||
54,0,2,160,201,0,1,163,0,0,2,1,2,1
|
||||
49,0,1,134,271,0,1,162,0,0,1,0,2,1
|
||||
42,1,1,120,295,0,1,162,0,0,2,0,2,1
|
||||
41,1,1,110,235,0,1,153,0,0,2,0,2,1
|
||||
41,0,1,126,306,0,1,163,0,0,2,0,2,1
|
||||
49,0,0,130,269,0,1,163,0,0,2,0,2,1
|
||||
60,0,2,120,178,1,1,96,0,0,2,0,2,1
|
||||
62,1,1,128,208,1,0,140,0,0,2,0,2,1
|
||||
57,1,0,110,201,0,1,126,1,1.5,1,0,1,1
|
||||
64,1,0,128,263,0,1,105,1,0.2,1,1,3,1
|
||||
51,0,2,120,295,0,0,157,0,0.6,2,0,2,1
|
||||
43,1,0,115,303,0,1,181,0,1.2,1,0,2,1
|
||||
42,0,2,120,209,0,1,173,0,0,1,0,2,1
|
||||
67,0,0,106,223,0,1,142,0,0.3,2,2,2,1
|
||||
76,0,2,140,197,0,2,116,0,1.1,1,0,2,1
|
||||
70,1,1,156,245,0,0,143,0,0,2,0,2,1
|
||||
44,0,2,118,242,0,1,149,0,0.3,1,1,2,1
|
||||
60,0,3,150,240,0,1,171,0,0.9,2,0,2,1
|
||||
44,1,2,120,226,0,1,169,0,0,2,0,2,1
|
||||
42,1,2,130,180,0,1,150,0,0,2,0,2,1
|
||||
66,1,0,160,228,0,0,138,0,2.3,2,0,1,1
|
||||
71,0,0,112,149,0,1,125,0,1.6,1,0,2,1
|
||||
64,1,3,170,227,0,0,155,0,0.6,1,0,3,1
|
||||
66,0,2,146,278,0,0,152,0,0,1,1,2,1
|
||||
39,0,2,138,220,0,1,152,0,0,1,0,2,1
|
||||
58,0,0,130,197,0,1,131,0,0.6,1,0,2,1
|
||||
47,1,2,130,253,0,1,179,0,0,2,0,2,1
|
||||
35,1,1,122,192,0,1,174,0,0,2,0,2,1
|
||||
58,1,1,125,220,0,1,144,0,0.4,1,4,3,1
|
||||
56,1,1,130,221,0,0,163,0,0,2,0,3,1
|
||||
56,1,1,120,240,0,1,169,0,0,0,0,2,1
|
||||
55,0,1,132,342,0,1,166,0,1.2,2,0,2,1
|
||||
41,1,1,120,157,0,1,182,0,0,2,0,2,1
|
||||
38,1,2,138,175,0,1,173,0,0,2,4,2,1
|
||||
38,1,2,138,175,0,1,173,0,0,2,4,2,1
|
||||
67,1,0,160,286,0,0,108,1,1.5,1,3,2,0
|
||||
67,1,0,120,229,0,0,129,1,2.6,1,2,3,0
|
||||
62,0,0,140,268,0,0,160,0,3.6,0,2,2,0
|
||||
63,1,0,130,254,0,0,147,0,1.4,1,1,3,0
|
||||
53,1,0,140,203,1,0,155,1,3.1,0,0,3,0
|
||||
56,1,2,130,256,1,0,142,1,0.6,1,1,1,0
|
||||
48,1,1,110,229,0,1,168,0,1,0,0,3,0
|
||||
58,1,1,120,284,0,0,160,0,1.8,1,0,2,0
|
||||
58,1,2,132,224,0,0,173,0,3.2,2,2,3,0
|
||||
60,1,0,130,206,0,0,132,1,2.4,1,2,3,0
|
||||
40,1,0,110,167,0,0,114,1,2,1,0,3,0
|
||||
60,1,0,117,230,1,1,160,1,1.4,2,2,3,0
|
||||
64,1,2,140,335,0,1,158,0,0,2,0,2,0
|
||||
43,1,0,120,177,0,0,120,1,2.5,1,0,3,0
|
||||
57,1,0,150,276,0,0,112,1,0.6,1,1,1,0
|
||||
55,1,0,132,353,0,1,132,1,1.2,1,1,3,0
|
||||
65,0,0,150,225,0,0,114,0,1,1,3,3,0
|
||||
61,0,0,130,330,0,0,169,0,0,2,0,2,0
|
||||
58,1,2,112,230,0,0,165,0,2.5,1,1,3,0
|
||||
50,1,0,150,243,0,0,128,0,2.6,1,0,3,0
|
||||
44,1,0,112,290,0,0,153,0,0,2,1,2,0
|
||||
60,1,0,130,253,0,1,144,1,1.4,2,1,3,0
|
||||
54,1,0,124,266,0,0,109,1,2.2,1,1,3,0
|
||||
50,1,2,140,233,0,1,163,0,0.6,1,1,3,0
|
||||
41,1,0,110,172,0,0,158,0,0,2,0,3,0
|
||||
51,0,0,130,305,0,1,142,1,1.2,1,0,3,0
|
||||
58,1,0,128,216,0,0,131,1,2.2,1,3,3,0
|
||||
54,1,0,120,188,0,1,113,0,1.4,1,1,3,0
|
||||
60,1,0,145,282,0,0,142,1,2.8,1,2,3,0
|
||||
60,1,2,140,185,0,0,155,0,3,1,0,2,0
|
||||
59,1,0,170,326,0,0,140,1,3.4,0,0,3,0
|
||||
46,1,2,150,231,0,1,147,0,3.6,1,0,2,0
|
||||
67,1,0,125,254,1,1,163,0,0.2,1,2,3,0
|
||||
62,1,0,120,267,0,1,99,1,1.8,1,2,3,0
|
||||
65,1,0,110,248,0,0,158,0,0.6,2,2,1,0
|
||||
44,1,0,110,197,0,0,177,0,0,2,1,2,0
|
||||
60,1,0,125,258,0,0,141,1,2.8,1,1,3,0
|
||||
58,1,0,150,270,0,0,111,1,0.8,2,0,3,0
|
||||
68,1,2,180,274,1,0,150,1,1.6,1,0,3,0
|
||||
62,0,0,160,164,0,0,145,0,6.2,0,3,3,0
|
||||
52,1,0,128,255,0,1,161,1,0,2,1,3,0
|
||||
59,1,0,110,239,0,0,142,1,1.2,1,1,3,0
|
||||
60,0,0,150,258,0,0,157,0,2.6,1,2,3,0
|
||||
49,1,2,120,188,0,1,139,0,2,1,3,3,0
|
||||
59,1,0,140,177,0,1,162,1,0,2,1,3,0
|
||||
57,1,2,128,229,0,0,150,0,0.4,1,1,3,0
|
||||
61,1,0,120,260,0,1,140,1,3.6,1,1,3,0
|
||||
39,1,0,118,219,0,1,140,0,1.2,1,0,3,0
|
||||
61,0,0,145,307,0,0,146,1,1,1,0,3,0
|
||||
56,1,0,125,249,1,0,144,1,1.2,1,1,2,0
|
||||
43,0,0,132,341,1,0,136,1,3,1,0,3,0
|
||||
62,0,2,130,263,0,1,97,0,1.2,1,1,3,0
|
||||
63,1,0,130,330,1,0,132,1,1.8,2,3,3,0
|
||||
65,1,0,135,254,0,0,127,0,2.8,1,1,3,0
|
||||
48,1,0,130,256,1,0,150,1,0,2,2,3,0
|
||||
63,0,0,150,407,0,0,154,0,4,1,3,3,0
|
||||
55,1,0,140,217,0,1,111,1,5.6,0,0,3,0
|
||||
65,1,3,138,282,1,0,174,0,1.4,1,1,2,0
|
||||
56,0,0,200,288,1,0,133,1,4,0,2,3,0
|
||||
54,1,0,110,239,0,1,126,1,2.8,1,1,3,0
|
||||
70,1,0,145,174,0,1,125,1,2.6,0,0,3,0
|
||||
62,1,1,120,281,0,0,103,0,1.4,1,1,3,0
|
||||
35,1,0,120,198,0,1,130,1,1.6,1,0,3,0
|
||||
59,1,3,170,288,0,0,159,0,0.2,1,0,3,0
|
||||
64,1,2,125,309,0,1,131,1,1.8,1,0,3,0
|
||||
47,1,2,108,243,0,1,152,0,0,2,0,2,0
|
||||
57,1,0,165,289,1,0,124,0,1,1,3,3,0
|
||||
55,1,0,160,289,0,0,145,1,0.8,1,1,3,0
|
||||
64,1,0,120,246,0,0,96,1,2.2,0,1,2,0
|
||||
70,1,0,130,322,0,0,109,0,2.4,1,3,2,0
|
||||
51,1,0,140,299,0,1,173,1,1.6,2,0,3,0
|
||||
58,1,0,125,300,0,0,171,0,0,2,2,3,0
|
||||
60,1,0,140,293,0,0,170,0,1.2,1,2,3,0
|
||||
77,1,0,125,304,0,0,162,1,0,2,3,2,0
|
||||
35,1,0,126,282,0,0,156,1,0,2,0,3,0
|
||||
70,1,2,160,269,0,1,112,1,2.9,1,1,3,0
|
||||
59,0,0,174,249,0,1,143,1,0,1,0,2,0
|
||||
64,1,0,145,212,0,0,132,0,2,1,2,1,0
|
||||
57,1,0,152,274,0,1,88,1,1.2,1,1,3,0
|
||||
56,1,0,132,184,0,0,105,1,2.1,1,1,1,0
|
||||
48,1,0,124,274,0,0,166,0,0.5,1,0,3,0
|
||||
56,0,0,134,409,0,0,150,1,1.9,1,2,3,0
|
||||
66,1,1,160,246,0,1,120,1,0,1,3,1,0
|
||||
54,1,1,192,283,0,0,195,0,0,2,1,3,0
|
||||
69,1,2,140,254,0,0,146,0,2,1,3,3,0
|
||||
51,1,0,140,298,0,1,122,1,4.2,1,3,3,0
|
||||
43,1,0,132,247,1,0,143,1,0.1,1,4,3,0
|
||||
62,0,0,138,294,1,1,106,0,1.9,1,3,2,0
|
||||
67,1,0,100,299,0,0,125,1,0.9,1,2,2,0
|
||||
59,1,3,160,273,0,0,125,0,0,2,0,2,0
|
||||
45,1,0,142,309,0,0,147,1,0,1,3,3,0
|
||||
58,1,0,128,259,0,0,130,1,3,1,2,3,0
|
||||
50,1,0,144,200,0,0,126,1,0.9,1,0,3,0
|
||||
62,0,0,150,244,0,1,154,1,1.4,1,0,2,0
|
||||
38,1,3,120,231,0,1,182,1,3.8,1,0,3,0
|
||||
66,0,0,178,228,1,1,165,1,1,1,2,3,0
|
||||
52,1,0,112,230,0,1,160,0,0,2,1,2,0
|
||||
53,1,0,123,282,0,1,95,1,2,1,2,3,0
|
||||
63,0,0,108,269,0,1,169,1,1.8,1,2,2,0
|
||||
54,1,0,110,206,0,0,108,1,0,1,1,2,0
|
||||
66,1,0,112,212,0,0,132,1,0.1,2,1,2,0
|
||||
55,0,0,180,327,0,2,117,1,3.4,1,0,2,0
|
||||
49,1,2,118,149,0,0,126,0,0.8,2,3,2,0
|
||||
54,1,0,122,286,0,0,116,1,3.2,1,2,2,0
|
||||
56,1,0,130,283,1,0,103,1,1.6,0,0,3,0
|
||||
46,1,0,120,249,0,0,144,0,0.8,2,0,3,0
|
||||
61,1,3,134,234,0,1,145,0,2.6,1,2,2,0
|
||||
67,1,0,120,237,0,1,71,0,1,1,0,2,0
|
||||
58,1,0,100,234,0,1,156,0,0.1,2,1,3,0
|
||||
47,1,0,110,275,0,0,118,1,1,1,1,2,0
|
||||
52,1,0,125,212,0,1,168,0,1,2,2,3,0
|
||||
58,1,0,146,218,0,1,105,0,2,1,1,3,0
|
||||
57,1,1,124,261,0,1,141,0,0.3,2,0,3,0
|
||||
58,0,1,136,319,1,0,152,0,0,2,2,2,0
|
||||
61,1,0,138,166,0,0,125,1,3.6,1,1,2,0
|
||||
42,1,0,136,315,0,1,125,1,1.8,1,0,1,0
|
||||
52,1,0,128,204,1,1,156,1,1,1,0,0,0
|
||||
59,1,2,126,218,1,1,134,0,2.2,1,1,1,0
|
||||
40,1,0,152,223,0,1,181,0,0,2,0,3,0
|
||||
61,1,0,140,207,0,0,138,1,1.9,2,1,3,0
|
||||
46,1,0,140,311,0,1,120,1,1.8,1,2,3,0
|
||||
59,1,3,134,204,0,1,162,0,0.8,2,2,2,0
|
||||
57,1,1,154,232,0,0,164,0,0,2,1,2,0
|
||||
57,1,0,110,335,0,1,143,1,3,1,1,3,0
|
||||
55,0,0,128,205,0,2,130,1,2,1,1,3,0
|
||||
61,1,0,148,203,0,1,161,0,0,2,1,3,0
|
||||
58,1,0,114,318,0,2,140,0,4.4,0,3,1,0
|
||||
58,0,0,170,225,1,0,146,1,2.8,1,2,1,0
|
||||
67,1,2,152,212,0,0,150,0,0.8,1,0,3,0
|
||||
44,1,0,120,169,0,1,144,1,2.8,0,0,1,0
|
||||
63,1,0,140,187,0,0,144,1,4,2,2,3,0
|
||||
63,0,0,124,197,0,1,136,1,0,1,0,2,0
|
||||
59,1,0,164,176,1,0,90,0,1,1,2,1,0
|
||||
57,0,0,140,241,0,1,123,1,0.2,1,0,3,0
|
||||
45,1,3,110,264,0,1,132,0,1.2,1,0,3,0
|
||||
68,1,0,144,193,1,1,141,0,3.4,1,2,3,0
|
||||
57,1,0,130,131,0,1,115,1,1.2,1,1,3,0
|
||||
57,0,1,130,236,0,0,174,0,0,1,1,2,0
|
||||
|
@ -0,0 +1,570 @@
|
||||
radius_se,texture_se,diagnosis
|
||||
1.095,0.9053,M
|
||||
0.5435,0.7339,M
|
||||
0.7456,0.7869,M
|
||||
0.4956,1.156,M
|
||||
0.7572,0.7813,M
|
||||
0.3345,0.8902,M
|
||||
0.4467,0.7732,M
|
||||
0.5835,1.377,M
|
||||
0.3063,1.002,M
|
||||
0.2976,1.599,M
|
||||
0.3795,1.187,M
|
||||
0.5058,0.9849,M
|
||||
0.9555,3.568,M
|
||||
0.4033,1.078,M
|
||||
0.2121,1.169,M
|
||||
0.37,1.033,M
|
||||
0.4727,1.24,M
|
||||
0.5692,1.073,M
|
||||
0.7582,1.017,M
|
||||
0.2699,0.7886,B
|
||||
0.1852,0.7477,B
|
||||
0.2773,0.9768,B
|
||||
0.4388,0.7096,M
|
||||
0.6917,1.127,M
|
||||
0.8068,0.9017,M
|
||||
1.046,0.976,M
|
||||
0.2545,0.9832,M
|
||||
0.8529,1.849,M
|
||||
0.439,1.012,M
|
||||
0.6003,0.8225,M
|
||||
0.8307,1.466,M
|
||||
0.4825,1.03,M
|
||||
0.6009,1.398,M
|
||||
0.5558,0.6062,M
|
||||
0.334,0.6857,M
|
||||
0.4615,0.9197,M
|
||||
0.286,1.019,M
|
||||
0.1839,2.342,B
|
||||
1.214,2.188,M
|
||||
0.213,0.5914,M
|
||||
0.2385,0.8265,M
|
||||
0.2366,1.428,M
|
||||
0.9811,1.666,M
|
||||
0.3704,0.8249,M
|
||||
0.1938,0.6123,M
|
||||
0.6289,0.6633,M
|
||||
0.1563,0.9567,B
|
||||
0.2871,0.8937,M
|
||||
0.2636,0.7294,B
|
||||
0.2338,1.353,B
|
||||
0.4062,1.21,B
|
||||
0.1872,0.9234,B
|
||||
0.2273,0.6329,B
|
||||
0.8337,1.593,M
|
||||
0.3105,0.8339,M
|
||||
0.3249,0.9591,B
|
||||
0.7275,1.193,M
|
||||
0.4226,1.15,M
|
||||
0.404,1.214,B
|
||||
0.1559,0.5796,B
|
||||
0.5158,1.441,B
|
||||
0.3582,2.067,B
|
||||
0.7036,1.268,M
|
||||
0.4098,2.265,B
|
||||
0.4255,1.178,M
|
||||
0.3577,1.281,M
|
||||
0.2351,2.011,B
|
||||
0.2727,0.9429,B
|
||||
0.3274,1.194,B
|
||||
0.2368,0.8732,B
|
||||
0.7888,0.7975,M
|
||||
0.5262,0.8522,B
|
||||
0.5907,1.041,M
|
||||
0.2787,0.6205,M
|
||||
0.2505,1.025,B
|
||||
0.7474,1.016,M
|
||||
0.4101,1.014,B
|
||||
0.9806,0.5505,M
|
||||
0.9317,1.885,M
|
||||
0.2655,1.095,B
|
||||
0.3251,2.174,B
|
||||
0.286,1.016,B
|
||||
0.8973,1.474,M
|
||||
0.519,2.91,M
|
||||
0.2271,1.255,B
|
||||
0.6997,1.475,M
|
||||
0.4204,2.22,M
|
||||
0.5495,0.6636,M
|
||||
0.2978,1.502,B
|
||||
0.5115,0.7372,B
|
||||
0.3721,1.111,B
|
||||
0.3129,0.8413,M
|
||||
0.4057,1.153,B
|
||||
0.295,1.373,B
|
||||
0.4768,0.9644,M
|
||||
0.7576,1.509,M
|
||||
0.3661,1.511,B
|
||||
0.335,2.043,B
|
||||
0.2315,0.5391,B
|
||||
0.2895,1.851,M
|
||||
0.4565,1.29,M
|
||||
0.2241,1.508,B
|
||||
0.1924,1.571,B
|
||||
0.1803,1.222,B
|
||||
0.355,1.534,B
|
||||
0.3908,0.9238,M
|
||||
0.306,1.657,B
|
||||
0.1199,0.8944,B
|
||||
1.215,1.545,M
|
||||
0.2344,0.9861,B
|
||||
0.403,1.424,B
|
||||
0.3424,1.803,B
|
||||
0.3628,1.49,B
|
||||
0.3336,1.86,B
|
||||
0.1665,0.5864,B
|
||||
0.3118,0.9227,B
|
||||
0.3132,0.9789,B
|
||||
0.4266,0.9489,M
|
||||
0.552,1.072,M
|
||||
0.5506,1.214,M
|
||||
0.1408,0.4607,B
|
||||
0.7128,1.581,M
|
||||
1.509,3.12,M
|
||||
0.2929,0.857,B
|
||||
0.1639,1.14,B
|
||||
0.2185,0.8561,B
|
||||
0.231,1.005,M
|
||||
0.6896,1.342,M
|
||||
0.4309,1.068,B
|
||||
0.4953,1.199,M
|
||||
0.2005,0.8163,B
|
||||
0.4743,0.7859,M
|
||||
0.4332,1.265,M
|
||||
0.3117,0.8155,B
|
||||
0.5959,1.202,M
|
||||
0.2367,1.38,M
|
||||
0.4489,2.508,B
|
||||
0.1759,0.9938,B
|
||||
1.296,1.452,M
|
||||
0.3384,1.343,B
|
||||
0.1988,0.496,B
|
||||
0.7049,1.332,M
|
||||
0.2843,1.908,B
|
||||
0.2143,0.7712,B
|
||||
0.2525,1.239,B
|
||||
0.3962,0.6538,B
|
||||
0.3197,1.426,M
|
||||
0.422,1.909,B
|
||||
0.2406,0.7394,B
|
||||
0.25,0.7574,B
|
||||
0.4202,1.322,B
|
||||
0.1935,1.962,B
|
||||
0.8245,2.664,B
|
||||
0.2251,0.7815,B
|
||||
0.271,0.7927,B
|
||||
0.22,0.9823,B
|
||||
0.8113,1.4,M
|
||||
0.4789,2.06,B
|
||||
0.1822,0.7285,B
|
||||
0.2818,0.7614,B
|
||||
0.5018,1.693,B
|
||||
1,0.6336,M
|
||||
0.7364,1.048,M
|
||||
0.2949,1.656,B
|
||||
0.6642,0.8561,M
|
||||
0.184,1.065,B
|
||||
0.1728,0.4064,B
|
||||
0.599,1.391,M
|
||||
1.088,1.41,M
|
||||
0.2713,1.217,B
|
||||
0.236,0.6656,B
|
||||
0.4697,1.147,M
|
||||
0.4209,0.6583,M
|
||||
0.2073,1.805,B
|
||||
0.3309,1.925,B
|
||||
0.2204,0.7873,B
|
||||
0.4311,2.261,B
|
||||
0.3037,1.284,M
|
||||
0.1731,1.142,B
|
||||
0.2889,0.9899,B
|
||||
0.8361,1.481,M
|
||||
0.6298,0.7629,M
|
||||
0.3699,1.15,M
|
||||
0.3344,1.108,B
|
||||
0.2054,0.4956,M
|
||||
0.4245,1.268,B
|
||||
0.2577,0.4757,M
|
||||
0.2451,0.7655,B
|
||||
0.1859,1.926,B
|
||||
0.2382,0.8355,B
|
||||
0.286,2.11,M
|
||||
0.7311,1.748,B
|
||||
0.3539,4.885,B
|
||||
0.4053,1.809,M
|
||||
0.2796,0.9622,M
|
||||
0.1942,0.9086,B
|
||||
0.6191,2.112,M
|
||||
0.6362,1.305,M
|
||||
0.4357,1.073,M
|
||||
0.2092,0.6509,M
|
||||
0.3534,1.326,B
|
||||
0.3971,0.8282,M
|
||||
0.5539,1.56,M
|
||||
0.5648,1.93,M
|
||||
0.3961,1.044,B
|
||||
0.2711,0.3621,M
|
||||
0.2137,1.342,B
|
||||
0.5858,0.8554,M
|
||||
0.1931,0.9223,B
|
||||
0.2134,0.3628,B
|
||||
0.8601,1.48,M
|
||||
0.2222,0.8652,B
|
||||
2.873,1.476,M
|
||||
0.5296,1.667,M
|
||||
0.4207,1.845,M
|
||||
0.2563,1.194,M
|
||||
0.2963,1.563,B
|
||||
0.3567,1.922,B
|
||||
0.9553,1.186,M
|
||||
0.7392,1.321,M
|
||||
0.2102,0.4336,B
|
||||
0.2569,0.4981,B
|
||||
0.2467,1.217,B
|
||||
0.3473,0.9209,M
|
||||
0.2927,0.8907,B
|
||||
0.522,0.8121,B
|
||||
0.1913,0.9027,B
|
||||
0.2318,0.4966,B
|
||||
0.2449,1.066,B
|
||||
0.3061,1.069,M
|
||||
0.2959,0.679,M
|
||||
0.121,0.8927,B
|
||||
0.2239,1.647,B
|
||||
0.524,1.189,M
|
||||
0.2152,0.8301,B
|
||||
0.2589,1.503,B
|
||||
1.058,0.9635,M
|
||||
0.6874,1.041,M
|
||||
0.3354,2.324,B
|
||||
0.5366,0.8561,M
|
||||
0.3242,0.6612,B
|
||||
0.1153,0.6745,B
|
||||
0.2428,1.642,B
|
||||
0.4347,1.057,B
|
||||
0.5243,1.802,M
|
||||
0.3719,2.612,B
|
||||
0.163,1.601,B
|
||||
0.2025,0.4402,B
|
||||
0.2497,1.493,B
|
||||
0.2562,1.038,B
|
||||
1.004,0.8208,M
|
||||
0.3927,0.8429,B
|
||||
0.7661,0.78,M
|
||||
0.3093,0.8568,M
|
||||
0.5959,0.6342,M
|
||||
0.425,0.8098,M
|
||||
0.8426,1.199,M
|
||||
0.6592,1.059,M
|
||||
1.292,2.454,M
|
||||
0.2419,1.278,M
|
||||
0.3977,1.033,M
|
||||
0.4007,1.317,M
|
||||
0.8348,1.633,M
|
||||
0.2298,0.9988,M
|
||||
0.4203,0.7383,M
|
||||
1.172,1.617,M
|
||||
0.4505,1.197,B
|
||||
0.338,1.916,B
|
||||
0.2345,1.219,B
|
||||
0.3198,1.489,B
|
||||
0.1302,0.7198,B
|
||||
0.1904,0.5293,B
|
||||
1.167,1.352,M
|
||||
0.2684,1.409,B
|
||||
0.4212,1.433,M
|
||||
0.6412,2.293,B
|
||||
0.2375,1.28,B
|
||||
0.3283,0.828,M
|
||||
0.258,1.166,B
|
||||
0.2479,0.9195,B
|
||||
0.6361,1.001,M
|
||||
0.5619,1.268,B
|
||||
0.4709,0.9951,M
|
||||
0.2873,0.9173,M
|
||||
0.2913,1.389,B
|
||||
0.2719,1.35,B
|
||||
0.2742,1.39,B
|
||||
0.1532,0.469,B
|
||||
0.4866,1.905,B
|
||||
0.2656,1.974,B
|
||||
0.8811,1.77,B
|
||||
0.2877,0.948,B
|
||||
0.2094,0.7636,B
|
||||
0.207,1.238,B
|
||||
0.1807,0.6931,B
|
||||
0.2191,0.6946,B
|
||||
0.1753,1.027,B
|
||||
0.645,2.105,M
|
||||
0.23,0.669,B
|
||||
0.2868,1.143,B
|
||||
1.111,1.161,M
|
||||
0.3642,1.04,B
|
||||
1.072,1.743,M
|
||||
0.1485,1.563,B
|
||||
0.3278,1.059,B
|
||||
0.2512,1.786,B
|
||||
0.1903,0.5735,B
|
||||
0.1746,1.305,B
|
||||
0.2244,0.6864,B
|
||||
0.3975,0.8285,B
|
||||
0.1601,1.43,B
|
||||
0.316,0.9115,B
|
||||
0.3265,0.6594,B
|
||||
0.1312,0.3602,B
|
||||
0.3368,2.777,B
|
||||
0.1716,0.7151,B
|
||||
0.2113,0.5996,B
|
||||
0.4041,0.5503,M
|
||||
0.4653,1.911,B
|
||||
0.3778,2.2,B
|
||||
0.3677,1.471,B
|
||||
0.5925,0.6863,M
|
||||
0.2212,1.042,B
|
||||
0.5702,1.023,M
|
||||
0.2575,0.8073,B
|
||||
0.21,0.9505,B
|
||||
0.2571,1.081,B
|
||||
0.2335,0.9097,B
|
||||
0.4375,1.232,M
|
||||
0.5706,1.457,M
|
||||
0.3371,0.7476,M
|
||||
0.2684,0.5664,B
|
||||
0.2976,1.966,B
|
||||
0.2144,0.9961,B
|
||||
0.184,1.532,B
|
||||
0.8161,2.129,M
|
||||
0.1814,0.6412,B
|
||||
0.6422,1.53,M
|
||||
0.2619,2.015,B
|
||||
1.009,0.9245,M
|
||||
0.3491,0.7706,B
|
||||
0.1844,0.9429,B
|
||||
0.1601,0.8225,B
|
||||
0.6226,2.284,M
|
||||
0.3446,0.7395,B
|
||||
0.338,2.509,B
|
||||
0.243,1.152,B
|
||||
0.3428,0.3981,B
|
||||
0.1707,0.7615,B
|
||||
0.361,1.05,B
|
||||
0.3534,0.6724,B
|
||||
0.5204,1.324,M
|
||||
0.9948,0.8509,M
|
||||
0.6534,1.506,M
|
||||
0.4222,0.8092,B
|
||||
0.3602,1.478,B
|
||||
0.3106,1.51,B
|
||||
0.2543,1.363,B
|
||||
0.5381,1.2,B
|
||||
0.5079,1.247,B
|
||||
0.3511,0.9527,B
|
||||
0.2621,1.539,B
|
||||
0.2213,1.285,B
|
||||
0.3389,1.439,B
|
||||
0.1584,0.6124,B
|
||||
0.5781,0.9168,M
|
||||
0.9761,1.892,M
|
||||
0.2527,0.7786,B
|
||||
1.207,1.051,M
|
||||
1.008,0.6999,M
|
||||
0.4312,1.022,M
|
||||
0.1783,0.4125,B
|
||||
0.3414,1.309,M
|
||||
0.6137,0.6575,M
|
||||
0.1705,0.5066,B
|
||||
0.1745,0.489,B
|
||||
0.1115,1.231,B
|
||||
0.1689,1.15,B
|
||||
0.1402,0.5417,B
|
||||
0.2114,1.027,M
|
||||
0.2562,0.9858,B
|
||||
0.1642,1.031,B
|
||||
0.1194,1.434,B
|
||||
0.2608,0.873,B
|
||||
0.1833,0.5308,B
|
||||
0.4157,1.627,M
|
||||
0.2666,0.8309,B
|
||||
0.2541,0.6218,B
|
||||
0.3305,1.067,B
|
||||
0.6107,2.836,M
|
||||
0.1911,0.5477,B
|
||||
0.5169,2.079,B
|
||||
0.647,1.331,M
|
||||
0.6242,0.9209,M
|
||||
0.2841,1.652,B
|
||||
0.1504,1.685,B
|
||||
0.2136,1.332,B
|
||||
0.3639,1.265,B
|
||||
0.1855,0.6881,B
|
||||
0.3438,1.14,B
|
||||
0.403,0.7747,M
|
||||
0.2522,1.045,B
|
||||
0.2357,1.299,B
|
||||
0.1458,0.905,B
|
||||
0.3833,0.9078,B
|
||||
0.3796,1.743,B
|
||||
0.2387,0.6372,B
|
||||
0.4993,1.798,B
|
||||
0.4537,0.8733,M
|
||||
0.3342,1.781,B
|
||||
0.1916,1.555,B
|
||||
0.1967,1.387,B
|
||||
0.1186,1.182,B
|
||||
0.3186,1.336,B
|
||||
0.4681,1.627,M
|
||||
0.2747,1.203,B
|
||||
0.4302,2.878,B
|
||||
1.37,1.213,M
|
||||
0.2253,0.6457,B
|
||||
0.2865,1.678,B
|
||||
0.2864,1.44,B
|
||||
0.5462,1.511,B
|
||||
0.2456,0.7339,B
|
||||
0.2244,0.895,B
|
||||
0.6965,1.747,B
|
||||
0.1851,1.341,B
|
||||
0.3276,1.127,B
|
||||
0.3077,1.621,B
|
||||
0.1415,0.9671,B
|
||||
0.2954,0.8836,B
|
||||
0.253,0.8749,M
|
||||
0.1767,1.46,B
|
||||
0.4331,1.001,M
|
||||
0.8191,1.931,M
|
||||
0.3028,0.6683,B
|
||||
0.2208,0.9533,M
|
||||
0.3665,0.7693,B
|
||||
0.3892,1.046,B
|
||||
0.3419,1.678,B
|
||||
0.2142,0.6549,B
|
||||
0.2574,1.376,B
|
||||
0.51,1.679,M
|
||||
0.3563,0.4833,B
|
||||
0.1818,2.542,B
|
||||
0.2986,0.5906,M
|
||||
0.2623,1.204,B
|
||||
0.3897,1.077,M
|
||||
0.2204,0.6221,B
|
||||
0.2535,1.354,B
|
||||
0.6643,1.361,M
|
||||
0.256,1.554,B
|
||||
0.4674,1.375,M
|
||||
0.1912,1.705,B
|
||||
0.306,0.7213,B
|
||||
0.1692,0.6674,B
|
||||
0.3408,1.924,B
|
||||
0.3135,2.426,B
|
||||
0.2084,1.35,B
|
||||
0.2621,1.232,B
|
||||
0.1781,1.687,B
|
||||
0.9291,1.152,M
|
||||
2.547,1.306,M
|
||||
0.2315,0.9112,B
|
||||
0.1816,0.7656,B
|
||||
0.2023,0.685,B
|
||||
0.281,0.8135,B
|
||||
0.3152,0.7884,B
|
||||
0.3416,1.312,B
|
||||
0.9289,1.465,M
|
||||
0.4101,1.74,B
|
||||
0.3776,1.35,B
|
||||
0.6061,2.643,B
|
||||
0.2446,0.4334,B
|
||||
0.4455,3.647,B
|
||||
0.1482,0.538,B
|
||||
0.1499,0.4875,B
|
||||
0.3478,1.018,B
|
||||
0.1555,0.5762,B
|
||||
0.2034,1.166,B
|
||||
0.3147,0.9857,M
|
||||
0.2194,1.19,B
|
||||
0.3316,0.9264,B
|
||||
0.1588,0.5733,B
|
||||
0.2431,0.9462,B
|
||||
0.163,0.3871,B
|
||||
0.3921,1.207,B
|
||||
0.2204,1.006,B
|
||||
0.5659,1.408,M
|
||||
0.3713,1.154,B
|
||||
0.2473,0.5679,M
|
||||
0.2239,1.139,B
|
||||
0.4834,1.046,B
|
||||
0.7548,1.288,M
|
||||
0.3276,1.486,B
|
||||
0.3237,1.473,B
|
||||
0.2323,1.636,B
|
||||
0.2324,0.6332,B
|
||||
0.1532,0.781,B
|
||||
0.7923,1.045,M
|
||||
0.5904,1.216,M
|
||||
0.372,0.8423,B
|
||||
0.4751,1.528,M
|
||||
0.2577,1.095,B
|
||||
1.291,0.7452,M
|
||||
0.4076,1.093,B
|
||||
0.2744,1.39,B
|
||||
0.1811,0.7959,B
|
||||
0.1779,1.03,B
|
||||
0.2067,0.4706,B
|
||||
0.3331,1.961,M
|
||||
0.1924,0.6417,B
|
||||
0.2182,0.6232,B
|
||||
0.3906,0.9306,M
|
||||
0.4165,0.6237,B
|
||||
0.386,1.198,M
|
||||
0.243,1.01,B
|
||||
0.5449,0.9225,M
|
||||
0.5079,0.8737,M
|
||||
0.4426,1.169,B
|
||||
0.3834,1.003,B
|
||||
0.3538,1.13,B
|
||||
0.9915,0.9004,M
|
||||
0.1344,1.083,B
|
||||
0.3191,1.249,B
|
||||
0.2498,1.216,B
|
||||
0.1267,0.6793,B
|
||||
0.1998,0.6068,B
|
||||
0.1166,0.4957,B
|
||||
0.5461,2.635,B
|
||||
0.2513,0.504,B
|
||||
0.4384,1.907,B
|
||||
0.2067,0.8745,B
|
||||
0.2047,0.4801,B
|
||||
0.8336,1.736,M
|
||||
0.1507,1.583,B
|
||||
0.6986,0.9901,M
|
||||
0.2027,1.851,M
|
||||
0.2957,1.978,B
|
||||
0.3777,1.462,B
|
||||
0.2196,1.479,B
|
||||
0.2784,1.768,B
|
||||
0.2542,1.079,B
|
||||
0.3031,1.385,B
|
||||
0.2351,1.597,B
|
||||
0.272,1.047,B
|
||||
0.346,1.336,B
|
||||
0.2104,0.967,B
|
||||
0.1144,1.023,B
|
||||
0.2957,1.363,B
|
||||
0.5196,1.918,B
|
||||
0.3163,1.304,B
|
||||
0.28,1.467,B
|
||||
0.2409,1.367,B
|
||||
0.3013,1.879,B
|
||||
0.2116,1.36,B
|
||||
0.2199,2.239,B
|
||||
0.2441,2.09,B
|
||||
0.5375,2.927,B
|
||||
0.2254,1.108,B
|
||||
0.2388,2.904,B
|
||||
0.3645,1.492,B
|
||||
0.3141,3.896,B
|
||||
0.2602,1.205,M
|
||||
0.9622,1.026,M
|
||||
1.176,1.256,M
|
||||
0.7655,2.463,M
|
||||
0.4564,1.075,M
|
||||
0.726,1.595,M
|
||||
0.3857,1.428,B
|
||||
|
@ -0,0 +1,24 @@
|
||||
fertility rate,worker percent
|
||||
3.71,28.33
|
||||
3.59,28.72
|
||||
3.48,29.18
|
||||
3.37,29.67
|
||||
3.27,30.17
|
||||
3.17,30.66
|
||||
3.07,31.02
|
||||
2.97,31.40
|
||||
2.88,31.77
|
||||
2.78,32.13
|
||||
2.69,32.50
|
||||
2.60,32.74
|
||||
2.52,33.50
|
||||
2.45,34.29
|
||||
2.38,35.10
|
||||
2.32,35.94
|
||||
2.27,36.77
|
||||
2.23,37.62
|
||||
2.19,38.49
|
||||
2.15,39.38
|
||||
2.12,40.29
|
||||
2.09,41.21
|
||||
2.06,44.04
|
||||
|
@ -0,0 +1,300 @@
|
||||
0.26509709797904785,-0.12434856136462093,1
|
||||
-0.8412847365149924,0.5865136249412204,0
|
||||
0.25124059511615654,-0.08269139382609397,1
|
||||
0.9001457018988898,-0.4489791718832927,1
|
||||
-0.6247595873234049,0.6036359153701123,0
|
||||
-0.9149387329894879,0.4516184879666203,0
|
||||
0.00607859405075889,0.17681945346362193,1
|
||||
-0.027780442469483732,1.0983215593418716,0
|
||||
1.8929868968339174,0.1076921319809737,1
|
||||
1.413980538449715,-0.49120937169888984,1
|
||||
1.711494521591537,-0.12691389874445677,1
|
||||
-0.8223509228514527,-0.03662084181259533,0
|
||||
1.9938277934159532,0.5238426791689887,1
|
||||
0.34839643377860785,0.9739230818450368,0
|
||||
-0.8144806104802478,0.6614239205886836,0
|
||||
-0.21677870040122865,1.0827226155814105,0
|
||||
1.12390972777151,0.2821893361842419,0
|
||||
0.10508475131657033,0.2075279946543494,1
|
||||
0.9812082410027939,-0.454233221004484,1
|
||||
0.9355996542489932,0.45280712194024564,0
|
||||
0.6266177470722407,0.44916056991149017,0
|
||||
-0.8512144284647087,0.578202617624754,0
|
||||
1.6308571767489748,-0.49881549740904396,1
|
||||
1.009239398300622,0.24427596687355158,0
|
||||
-0.7025572603966981,0.8609480121072615,0
|
||||
0.7421970573869348,0.28314379153108354,0
|
||||
2.0286673475020423,0.4809938626907892,1
|
||||
-0.2932200771348434,1.0160744024799104,0
|
||||
1.900211041152654,0.2886463392816714,1
|
||||
0.2552122340281108,-0.04081067898908221,1
|
||||
-0.9456761394647709,0.3434181859400779,0
|
||||
1.4896268572261304,-0.20599923056856906,1
|
||||
1.1323149003238455,-0.34767667094556554,1
|
||||
0.11300676612076083,0.9017284773201051,0
|
||||
0.457559876845872,0.8164260584058034,0
|
||||
-0.23335014519475233,0.9113062402994168,0
|
||||
-0.38131062638792435,0.9828376494483352,0
|
||||
-0.07559462759441649,0.2334459898154325,1
|
||||
-0.3254291513386371,0.9484205475346345,0
|
||||
0.8895803348935676,-0.6273806972913621,1
|
||||
0.9811594507109008,-0.5220298269220419,1
|
||||
1.0512160914873752,-0.4593091373926673,1
|
||||
0.3393833862236784,-0.2754102024838206,1
|
||||
1.1733855076409367,0.08391623353443281,0
|
||||
0.028092009445026975,0.9183186375180167,0
|
||||
0.9208170521019627,-0.38788285888792934,1
|
||||
1.3590274744791193,-0.31042779676021326,1
|
||||
0.03057084346373363,0.17487165609055938,1
|
||||
0.8235319912271322,0.3230853318476944,0
|
||||
0.7380701457311399,-0.22147485463655306,1
|
||||
0.9073309004374593,0.04018452647280662,0
|
||||
1.8708135512177184,0.26100168631766574,1
|
||||
2.1059100284145784,0.21920257010231065,1
|
||||
0.24556079604275705,0.008475346542317942,1
|
||||
2.1054677399499653,0.3684599310029875,1
|
||||
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||||
|
Loading…
Reference in New Issue