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Solve IML task 1a

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David Doebel
2026-03-11 21:34:43 +01:00
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IML Projects/Task 1a/data/results.csv Normal file
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5.503638303110
5.480400276223
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5.931931132834
6.243346500145
1 5.503638303110
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3 5.469885552772
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5 6.243346500145

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13.145520802298
9.601847478657
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14.511194012770
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2 9.601847478657
3 6.087285844829
4 14.511194012770
5 22.066089946694

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IML Projects/Task 1a/data/template_solution.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### General guidance\n",
"\n",
"This serves as a template which will guide you through the implementation of this task. It is advised\n",
"to first read the whole template and get a sense of the overall structure of the code before trying to fill in any of the TODO gaps.\n",
"This is the jupyter notebook version of the template. For the python file version, please refer to the file `template_solution.py`.\n",
"\n",
"First, we import necessary libraries:"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2026-03-11T20:25:46.118936Z",
"start_time": "2026-03-11T20:25:46.115270Z"
}
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.model_selection import KFold\n",
"\n",
"# Add any additional imports here (however, the task is solvable without using \n",
"# any additional imports)\n",
"# import ..."
],
"outputs": [],
"execution_count": 55
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" #### Loading data"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2026-03-11T20:25:46.151114Z",
"start_time": "2026-03-11T20:25:46.143378Z"
}
},
"source": [
"data = pd.read_csv(\"train.csv\")\n",
"y = data[\"y\"].to_numpy()\n",
"data = data.drop(columns=\"y\")\n",
"# print a few data samples\n",
"print(data.head())"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 \\\n",
"0 0.06724 0.0 3.24 0.0 0.460 6.333 17.2 5.2146 4.0 430.0 16.9 \n",
"1 9.23230 0.0 18.10 0.0 0.631 6.216 100.0 1.1691 24.0 666.0 20.2 \n",
"2 0.11425 0.0 13.89 1.0 0.550 6.373 92.4 3.3633 5.0 276.0 16.4 \n",
"3 24.80170 0.0 18.10 0.0 0.693 5.349 96.0 1.7028 24.0 666.0 20.2 \n",
"4 0.05646 0.0 12.83 0.0 0.437 6.232 53.7 5.0141 5.0 398.0 18.7 \n",
"\n",
" x12 x13 \n",
"0 375.21 7.34 \n",
"1 366.15 9.53 \n",
"2 393.74 10.50 \n",
"3 396.90 19.77 \n",
"4 386.40 12.34 \n"
]
}
],
"execution_count": 56
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Calculating the average RMSE"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2026-03-11T20:25:46.175778Z",
"start_time": "2026-03-11T20:25:46.173181Z"
}
},
"source": [
"def calculate_RMSE(w, X, y):\n",
" \"\"\"This function takes test data points (X and y), and computes the empirical RMSE of \n",
" predicting y from X using a linear model with weights w. \n",
"\n",
" Parameters\n",
" ----------\n",
" w: array of floats: dim = (13,), optimal parameters of ridge regression \n",
" X: matrix of floats, dim = (15,13), inputs with 13 features\n",
" y: array of floats, dim = (15,), input labels\n",
"\n",
" Returns\n",
" ----------\n",
" rmse: float: dim = 1, RMSE value\n",
" \"\"\"\n",
" rmse = 0\n",
" n = X.shape[0]\n",
" rmse = np.sqrt(1/n * np.sum( (y - X @ w)**2 ))\n",
" assert np.isscalar(rmse)\n",
" return rmse"
],
"outputs": [],
"execution_count": 57
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Fitting the regressor"
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": [
"The fitting can be carried out by solving the normal equations:\n",
"$$\n",
"(\\lambda \\cdot \\mathbf{I} + \\mathbf{X}) \\mathbf{w} = \\mathbf{X}^{T}\\cdot \\mathbf{y}\n",
"$$\n"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2026-03-11T20:25:46.200893Z",
"start_time": "2026-03-11T20:25:46.197877Z"
}
},
"source": [
"def fit(X, y, lam):\n",
" \"\"\"\n",
" This function receives training data points, then fits the ridge regression on this data\n",
" with regularization hyperparameter lambda. The weights w of the fitted ridge regression\n",
" are returned. \n",
"\n",
" Parameters\n",
" ----------\n",
" X: matrix of floats, dim = (135,13), inputs with 13 features\n",
" y: array of floats, dim = (135,), input labels\n",
" lam: float. lambda parameter, used in regularization term\n",
"\n",
" Returns\n",
" ----------\n",
" w: array of floats: dim = (13,), optimal parameters of ridge regression\n",
" \"\"\"\n",
" weights = np.zeros((13,))\n",
" A = lam * np.identity(13) + np.transpose(X) @ X\n",
" b = X.T @ y\n",
" weights = np.linalg.solve(A, b)\n",
"\n",
" assert weights.shape == (13,)\n",
" return weights"
],
"outputs": [],
"execution_count": 58
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Performing computation"
]
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2026-03-11T20:27:41.325447Z",
"start_time": "2026-03-11T20:27:41.305479Z"
}
},
"source": [
"\"\"\"\n",
"Main cross-validation loop, implementing 10-fold CV. In every iteration \n",
"(for every train-test split), the RMSE for every lambda is calculated, \n",
"and then averaged over iterations.\n",
"\n",
"Parameters\n",
"---------- \n",
"X: matrix of floats, dim = (150, 13), inputs with 13 features\n",
"y: array of floats, dim = (150, ), input labels\n",
"lambdas: list of floats, len = 5, values of lambda for which ridge regression is fitted and RMSE estimated\n",
"n_folds: int, number of folds (pieces in which we split the dataset), parameter K in KFold CV\n",
"\n",
"Compute\n",
"----------\n",
"avg_RMSE: array of floats: dim = (5,), average RMSE value for every lambda\n",
"\"\"\"\n",
"X = data.to_numpy()\n",
"# The function calculating the average RMSE\n",
"lambdas = [0.1, 1, 10, 100, 200]\n",
"n_folds = 10\n",
"\n",
"RMSE_mat = np.zeros((n_folds, len(lambdas)))\n",
"\n",
"\n",
"# and fill all entries in the matrix 'RMSE_mat'\n",
"for k in range(n_folds):\n",
" fold = 150 // n_folds\n",
" X_train = np.concatenate((X[:k*fold,:], X[(k+1)*fold:,:]),axis=0) # leave out the validation set\n",
" X_validation = X[k*fold:(k+1)*fold,:]\n",
" y_train = np.concatenate((y[:k*fold], y[(k+1)*fold:]))\n",
" y_validation = y[k*fold:(k+1)*fold]\n",
" for i in range(len(lambdas)):\n",
" w = fit(X_train, y_train, lambdas[i])\n",
" RMSE_mat[k,i] = calculate_RMSE(w, X_validation, y_validation)\n",
"avg_RMSE = np.mean(RMSE_mat, axis=0) # avg_RMSE: array of floats: dim = (5,), average RMSE value for every lambda\n",
"assert avg_RMSE.shape == (5,)"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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]
}
],
"execution_count": 64
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-03-11T20:25:46.251095Z",
"start_time": "2026-03-11T20:25:46.248154Z"
}
},
"cell_type": "code",
"source": [
"A = np.array([[1,2],[3,4]])\n",
"#print(A[:,:1])\n",
"#print(np.transpose(A), A, np.transpose(A) @ A)\n",
"print(np.concatenate((A,A),axis=1))"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 2 1 2]\n",
" [3 4 3 4]]\n"
]
}
],
"execution_count": 60
},
{
"cell_type": "code",
"metadata": {
"ExecuteTime": {
"end_time": "2026-03-11T20:25:46.295067Z",
"start_time": "2026-03-11T20:25:46.292066Z"
}
},
"source": [
"# Save results in the required format\n",
"np.savetxt(\"./results.csv\", avg_RMSE, fmt=\"%.12f\")"
],
"outputs": [],
"execution_count": 61
},
{
"metadata": {
"ExecuteTime": {
"end_time": "2026-03-11T20:25:46.321558Z",
"start_time": "2026-03-11T20:25:46.319798Z"
}
},
"cell_type": "code",
"source": "# Validate",
"outputs": [],
"execution_count": 62
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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# This serves as a template which will guide you through the implementation of this task. It is advised
# to first read the whole template and get a sense of the overall structure of the code before trying to fill in any of the TODO gaps.
# First, we import necessary libraries:
import pandas as pd
import numpy as np
from sklearn.model_selection import KFold
# Add any additional imports here (however, the task is solvable without using
# any additional imports)
# import ...
def fit(X, y, lam):
"""
This function receives training data points, then fits the ridge regression on this data
with regularization hyperparameter lambda. The weights w of the fitted ridge regression
are returned.
Parameters
----------
X: matrix of floats, dim = (135,13), inputs with 13 features
y: array of floats, dim = (135,), input labels
lam: float. lambda parameter, used in regularization term
Returns
----------
w: array of floats: dim = (13,), optimal parameters of ridge regression
"""
weights = np.zeros((13,))
# TODO: Enter your code here
assert weights.shape == (13,)
return weights
def calculate_RMSE(w, X, y):
"""This function takes test data points (X and y), and computes the empirical RMSE of
predicting y from X using a linear model with weights w.
Parameters
----------
w: array of floats: dim = (13,), optimal parameters of ridge regression
X: matrix of floats, dim = (15,13), inputs with 13 features
y: array of floats, dim = (15,), input labels
Returns
----------
rmse: float: dim = 1, RMSE value
"""
rmse = 0
# TODO: Enter your code here
assert np.isscalar(rmse)
return rmse
def average_LR_RMSE(X, y, lambdas, n_folds):
"""
Main cross-validation loop, implementing 10-fold CV. In every iteration (for every train-test split), the RMSE for every lambda is calculated,
and then averaged over iterations.
Parameters
----------
X: matrix of floats, dim = (150, 13), inputs with 13 features
y: array of floats, dim = (150, ), input labels
lambdas: list of floats, len = 5, values of lambda for which ridge regression is fitted and RMSE estimated
n_folds: int, number of folds (pieces in which we split the dataset), parameter K in KFold CV
Returns
----------
avg_RMSE: array of floats: dim = (5,), average RMSE value for every lambda
"""
RMSE_mat = np.zeros((n_folds, len(lambdas)))
# TODO: Enter your code here. Hint: Use functions 'fit' and 'calculate_RMSE' with training and test data
# and fill all entries in the matrix 'RMSE_mat'
avg_RMSE = np.mean(RMSE_mat, axis=0)
assert avg_RMSE.shape == (5,)
return avg_RMSE
# Main function. You don't have to change this
if __name__ == "__main__":
# Data loading
data = pd.read_csv("train.csv")
y = data["y"].to_numpy()
data = data.drop(columns="y")
# print a few data samples
print(data.head())
X = data.to_numpy()
# The function calculating the average RMSE
lambdas = [0.1, 1, 10, 100, 200]
n_folds = 10
avg_RMSE = average_LR_RMSE(X, y, lambdas, n_folds)
# Save results in the required format
np.savetxt("./results.csv", avg_RMSE, fmt="%.12f")

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IML Projects/Task 1a/data/train.csv Normal file
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y,x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13
22.6,0.06724,0.0,3.24,0.0,0.46,6.333,17.2,5.2146,4.0,430.0,16.9,375.21,7.34
50.0,9.2323,0.0,18.1,0.0,0.631,6.216,100.0,1.1691,24.0,666.0,20.2,366.15,9.53
23.0,0.11425,0.0,13.89,1.0,0.55,6.373,92.4,3.3633,5.0,276.0,16.4,393.74,10.5
8.3,24.8017,0.0,18.1,0.0,0.693,5.349,96.0,1.7028,24.0,666.0,20.2,396.9,19.77
21.2,0.05646,0.0,12.83,0.0,0.437,6.232,53.7,5.0141,5.0,398.0,18.7,386.4,12.34
19.9,0.62739,0.0,8.14,0.0,0.538,5.834,56.5,4.4986,4.0,307.0,21.0,395.62,8.47
20.6,4.83567,0.0,18.1,0.0,0.583,5.905,53.2,3.1523,24.0,666.0,20.2,388.22,11.45
18.7,0.06151,0.0,5.19,0.0,0.515,5.968,58.5,4.8122,5.0,224.0,20.2,396.9,9.29
16.1,2.63548,0.0,9.9,0.0,0.544,4.973,37.8,2.5194,4.0,304.0,18.4,350.45,12.64
18.6,0.22876,0.0,8.56,0.0,0.52,6.405,85.4,2.7147,5.0,384.0,20.9,70.8,10.63
8.8,73.5341,0.0,18.1,0.0,0.679,5.957,100.0,1.8026,24.0,666.0,20.2,16.45,20.62
17.2,14.0507,0.0,18.1,0.0,0.597,6.657,100.0,1.5275,24.0,666.0,20.2,35.05,21.22
14.9,6.28807,0.0,18.1,0.0,0.74,6.341,96.4,2.072,24.0,666.0,20.2,318.01,17.79
10.5,24.3938,0.0,18.1,0.0,0.7,4.652,100.0,1.4672,24.0,666.0,20.2,396.9,28.28
50.0,1.83377,0.0,19.58,1.0,0.605,7.802,98.2,2.0407,5.0,403.0,14.7,389.61,1.92
29.0,0.05561,70.0,2.24,0.0,0.4,7.041,10.0,7.8278,5.0,358.0,14.8,371.58,4.74
23.0,5.82401,0.0,18.1,0.0,0.532,6.242,64.7,3.4242,24.0,666.0,20.2,396.9,10.74
33.3,0.04011,80.0,1.52,0.0,0.404,7.287,34.1,7.309,2.0,329.0,12.6,396.9,4.08
29.4,0.06664,0.0,4.05,0.0,0.51,6.546,33.1,3.1323,5.0,296.0,16.6,390.96,5.33
21.0,0.08014,0.0,5.96,0.0,0.499,5.85,41.5,3.9342,5.0,279.0,19.2,396.9,8.77
23.8,0.1676,0.0,7.38,0.0,0.493,6.426,52.3,4.5404,5.0,287.0,19.6,396.9,7.2
19.1,2.3139,0.0,19.58,0.0,0.605,5.88,97.3,2.3887,5.0,403.0,14.7,348.13,12.03
20.4,0.13117,0.0,8.56,0.0,0.52,6.127,85.2,2.1224,5.0,384.0,20.9,387.69,14.09
29.1,0.07978,40.0,6.41,0.0,0.447,6.482,32.1,4.1403,4.0,254.0,17.6,396.9,7.19
19.3,0.17142,0.0,6.91,0.0,0.448,5.682,33.8,5.1004,3.0,233.0,17.9,396.9,10.21
23.1,13.5222,0.0,18.1,0.0,0.631,3.863,100.0,1.5106,24.0,666.0,20.2,131.42,13.33
19.6,0.85204,0.0,8.14,0.0,0.538,5.965,89.2,4.0123,4.0,307.0,21.0,392.53,13.83
19.4,2.14918,0.0,19.58,0.0,0.871,5.709,98.5,1.6232,5.0,403.0,14.7,261.95,15.79
38.7,0.12083,0.0,2.89,0.0,0.445,8.069,76.0,3.4952,2.0,276.0,18.0,396.9,4.21
18.7,0.22212,0.0,10.01,0.0,0.547,6.092,95.4,2.548,6.0,432.0,17.8,396.9,17.09
14.6,10.233,0.0,18.1,0.0,0.614,6.185,96.7,2.1705,24.0,666.0,20.2,379.7,18.03
20.0,6.80117,0.0,18.1,0.0,0.713,6.081,84.4,2.7175,24.0,666.0,20.2,396.9,14.7
20.5,0.19657,22.0,5.86,0.0,0.431,6.226,79.2,8.0555,7.0,330.0,19.1,376.14,10.15
20.1,0.10612,30.0,4.93,0.0,0.428,6.095,65.1,6.3361,6.0,300.0,16.6,394.62,12.4
23.6,0.09178,0.0,4.05,0.0,0.51,6.416,84.1,2.6463,5.0,296.0,16.6,395.5,9.04
16.8,4.22239,0.0,18.1,1.0,0.77,5.803,89.0,1.9047,24.0,666.0,20.2,353.04,14.64
5.6,25.0461,0.0,18.1,0.0,0.693,5.987,100.0,1.5888,24.0,666.0,20.2,396.9,26.77
50.0,8.26725,0.0,18.1,1.0,0.668,5.875,89.6,1.1296,24.0,666.0,20.2,347.88,8.88
14.5,8.49213,0.0,18.1,0.0,0.584,6.348,86.1,2.0527,24.0,666.0,20.2,83.45,17.64
13.3,6.39312,0.0,18.1,0.0,0.584,6.162,97.4,2.206,24.0,666.0,20.2,302.76,24.1
23.9,0.08265,0.0,13.92,0.0,0.437,6.127,18.4,5.5027,4.0,289.0,16.0,396.9,8.58
20.0,0.18836,0.0,6.91,0.0,0.448,5.786,33.3,5.1004,3.0,233.0,17.9,396.9,14.15
19.8,0.04544,0.0,3.24,0.0,0.46,6.144,32.2,5.8736,4.0,430.0,16.9,368.57,9.09
13.8,8.05579,0.0,18.1,0.0,0.584,5.427,95.4,2.4298,24.0,666.0,20.2,352.58,18.14
16.5,0.02498,0.0,1.89,0.0,0.518,6.54,59.7,6.2669,1.0,422.0,15.9,389.96,8.65
21.6,0.02731,0.0,7.07,0.0,0.469,6.421,78.9,4.9671,2.0,242.0,17.8,396.9,9.14
20.3,0.14103,0.0,13.92,0.0,0.437,5.79,58.0,6.32,4.0,289.0,16.0,396.9,15.84
17.0,1.41385,0.0,19.58,1.0,0.871,6.129,96.0,1.7494,5.0,403.0,14.7,321.02,15.12
11.8,2.77974,0.0,19.58,0.0,0.871,4.903,97.8,1.3459,5.0,403.0,14.7,396.9,29.29
27.5,0.14866,0.0,8.56,0.0,0.52,6.727,79.9,2.7778,5.0,384.0,20.9,394.76,9.42
15.6,3.53501,0.0,19.58,1.0,0.871,6.152,82.6,1.7455,5.0,403.0,14.7,88.01,15.02
23.1,0.17899,0.0,9.69,0.0,0.585,5.67,28.8,2.7986,6.0,391.0,19.2,393.29,17.6
24.3,0.537,0.0,6.2,0.0,0.504,5.981,68.1,3.6715,8.0,307.0,17.4,378.35,11.65
42.8,0.36894,22.0,5.86,0.0,0.431,8.259,8.4,8.9067,7.0,330.0,19.1,396.9,3.54
15.6,0.97617,0.0,21.89,0.0,0.624,5.757,98.4,2.346,4.0,437.0,21.2,262.76,17.31
21.7,0.09378,12.5,7.87,0.0,0.524,5.889,39.0,5.4509,5.0,311.0,15.2,390.5,15.71
17.1,0.05023,35.0,6.06,0.0,0.4379,5.706,28.4,6.6407,1.0,304.0,16.9,394.02,12.43
17.2,0.06162,0.0,4.39,0.0,0.442,5.898,52.3,8.0136,3.0,352.0,18.8,364.61,12.67
15.0,51.1358,0.0,18.1,0.0,0.597,5.757,100.0,1.413,24.0,666.0,20.2,2.6,10.11
21.7,0.17446,0.0,10.59,1.0,0.489,5.96,92.1,3.8771,4.0,277.0,18.6,393.25,17.27
18.6,0.07244,60.0,1.69,0.0,0.411,5.884,18.5,10.7103,4.0,411.0,18.3,392.33,7.79
21.0,0.47547,0.0,9.9,0.0,0.544,6.113,58.8,4.0019,4.0,304.0,18.4,396.23,12.73
33.1,0.1,34.0,6.09,0.0,0.433,6.982,17.7,5.4917,7.0,329.0,16.1,390.43,4.86
31.5,0.44178,0.0,6.2,0.0,0.504,6.552,21.4,3.3751,8.0,307.0,17.4,380.34,3.76
20.1,13.0751,0.0,18.1,0.0,0.58,5.713,56.7,2.8237,24.0,666.0,20.2,396.9,14.76
29.8,0.12579,45.0,3.44,0.0,0.437,6.556,29.1,4.5667,5.0,398.0,15.2,382.84,4.56
15.2,5.44114,0.0,18.1,0.0,0.713,6.655,98.2,2.3552,24.0,666.0,20.2,355.29,17.73
15.0,0.22489,12.5,7.87,0.0,0.524,6.377,94.3,6.3467,5.0,311.0,15.2,392.52,20.45
27.5,14.4383,0.0,18.1,0.0,0.597,6.852,100.0,1.4655,24.0,666.0,20.2,179.36,19.78
22.6,0.13642,0.0,10.59,0.0,0.489,5.891,22.3,3.9454,4.0,277.0,18.6,396.9,10.87
20.0,0.10153,0.0,12.83,0.0,0.437,6.279,74.5,4.0522,5.0,398.0,18.7,373.66,11.97
21.4,0.09512,0.0,12.83,0.0,0.437,6.286,45.0,4.5026,5.0,398.0,18.7,383.23,8.94
23.5,0.03584,80.0,3.37,0.0,0.398,6.29,17.8,6.6115,4.0,337.0,16.1,396.9,4.67
31.2,0.03049,55.0,3.78,0.0,0.484,6.874,28.1,6.4654,5.0,370.0,17.6,387.97,4.61
23.7,5.70818,0.0,18.1,0.0,0.532,6.75,74.9,3.3317,24.0,666.0,20.2,393.07,7.74
7.4,22.5971,0.0,18.1,0.0,0.7,5.0,89.5,1.5184,24.0,666.0,20.2,396.9,31.99
48.3,0.33147,0.0,6.2,0.0,0.507,8.247,70.4,3.6519,8.0,307.0,17.4,378.95,3.95
24.4,0.22969,0.0,10.59,0.0,0.489,6.326,52.5,4.3549,4.0,277.0,18.6,394.87,10.97
22.6,0.04684,0.0,3.41,0.0,0.489,6.417,66.1,3.0923,2.0,270.0,17.8,392.18,8.81
18.3,0.26838,0.0,9.69,0.0,0.585,5.794,70.6,2.8927,6.0,391.0,19.2,396.9,14.1
23.3,0.09252,30.0,4.93,0.0,0.428,6.606,42.2,6.1899,6.0,300.0,16.6,383.78,7.37
17.1,0.35233,0.0,21.89,0.0,0.624,6.454,98.4,1.8498,4.0,437.0,21.2,394.08,14.59
27.9,11.9511,0.0,18.1,0.0,0.659,5.608,100.0,1.2852,24.0,666.0,20.2,332.09,12.13
44.8,0.31533,0.0,6.2,0.0,0.504,8.266,78.3,2.8944,8.0,307.0,17.4,385.05,4.14
50.0,0.52693,0.0,6.2,0.0,0.504,8.725,83.0,2.8944,8.0,307.0,17.4,382.0,4.63
23.0,0.30347,0.0,7.38,0.0,0.493,6.312,28.9,5.4159,5.0,287.0,19.6,396.9,6.15
21.4,0.11504,0.0,2.89,0.0,0.445,6.163,69.6,3.4952,2.0,276.0,18.0,391.83,11.34
10.2,12.2472,0.0,18.1,0.0,0.584,5.837,59.7,1.9976,24.0,666.0,20.2,24.65,15.69
23.3,1.42502,0.0,19.58,0.0,0.871,6.51,100.0,1.7659,5.0,403.0,14.7,364.31,7.39
23.2,5.29305,0.0,18.1,0.0,0.7,6.051,82.5,2.1678,24.0,666.0,20.2,378.38,18.76
18.9,0.0136,75.0,4.0,0.0,0.41,5.888,47.6,7.3197,3.0,469.0,21.1,396.9,14.8
13.4,11.1604,0.0,18.1,0.0,0.74,6.629,94.6,2.1247,24.0,666.0,20.2,109.85,23.27
21.9,0.04819,80.0,3.64,0.0,0.392,6.108,32.0,9.2203,1.0,315.0,16.4,392.89,6.57
24.8,0.04417,70.0,2.24,0.0,0.4,6.871,47.4,7.8278,5.0,358.0,14.8,390.86,6.07
11.9,0.04741,0.0,11.93,0.0,0.573,6.03,80.8,2.505,1.0,273.0,21.0,396.9,7.88
24.3,0.33983,22.0,5.86,0.0,0.431,6.108,34.9,8.0555,7.0,330.0,19.1,390.18,9.16
13.8,18.4982,0.0,18.1,0.0,0.668,4.138,100.0,1.137,24.0,666.0,20.2,396.9,37.97
24.7,0.02055,85.0,0.74,0.0,0.41,6.383,35.7,9.1876,2.0,313.0,17.3,396.9,5.77
14.1,4.75237,0.0,18.1,0.0,0.713,6.525,86.5,2.4358,24.0,666.0,20.2,50.92,18.13
18.7,0.14932,25.0,5.13,0.0,0.453,5.741,66.2,7.2254,8.0,284.0,19.7,395.11,13.15
28.1,0.14052,0.0,10.59,0.0,0.489,6.375,32.3,3.9454,4.0,277.0,18.6,385.81,9.38
19.8,0.12802,0.0,8.56,0.0,0.52,6.474,97.1,2.4329,5.0,384.0,20.9,395.24,12.27
26.7,0.35809,0.0,6.2,1.0,0.507,6.951,88.5,2.8617,8.0,307.0,17.4,391.7,9.71
21.7,0.15876,0.0,10.81,0.0,0.413,5.961,17.5,5.2873,4.0,305.0,19.2,376.94,9.88
22.0,0.11329,30.0,4.93,0.0,0.428,6.897,54.3,6.3361,6.0,300.0,16.6,391.25,11.38
22.9,0.08829,12.5,7.87,0.0,0.524,6.012,66.6,5.5605,5.0,311.0,15.2,395.6,12.43
10.4,25.9406,0.0,18.1,0.0,0.679,5.304,89.1,1.6475,24.0,666.0,20.2,127.36,26.64
21.9,3.69695,0.0,18.1,0.0,0.718,4.963,91.4,1.7523,24.0,666.0,20.2,316.03,14.0
20.6,0.04527,0.0,11.93,0.0,0.573,6.12,76.7,2.2875,1.0,273.0,21.0,396.9,9.08
26.4,0.09266,34.0,6.09,0.0,0.433,6.495,18.4,5.4917,7.0,329.0,16.1,383.61,8.67
41.3,1.22358,0.0,19.58,0.0,0.605,6.943,97.4,1.8773,5.0,403.0,14.7,363.43,4.59
17.2,7.40389,0.0,18.1,0.0,0.597,5.617,97.9,1.4547,24.0,666.0,20.2,314.64,26.4
27.1,0.14455,12.5,7.87,0.0,0.524,6.172,96.1,5.9505,5.0,311.0,15.2,396.9,19.15
20.4,0.13058,0.0,10.01,0.0,0.547,5.872,73.1,2.4775,6.0,432.0,17.8,338.63,15.37
16.5,0.21124,12.5,7.87,0.0,0.524,5.631,100.0,6.0821,5.0,311.0,15.2,386.63,29.93
24.4,0.13587,0.0,10.59,1.0,0.489,6.064,59.1,4.2392,4.0,277.0,18.6,381.32,14.66
8.4,13.6781,0.0,18.1,0.0,0.74,5.935,87.9,1.8206,24.0,666.0,20.2,68.95,34.02
23.0,0.59005,0.0,21.89,0.0,0.624,6.372,97.9,2.3274,4.0,437.0,21.2,385.76,11.12
9.7,11.5779,0.0,18.1,0.0,0.7,5.036,97.0,1.77,24.0,666.0,20.2,396.9,25.68
50.0,0.01501,90.0,1.21,1.0,0.401,7.923,24.8,5.885,1.0,198.0,13.6,395.52,3.16
30.5,0.06911,45.0,3.44,0.0,0.437,6.739,30.8,6.4798,5.0,398.0,15.2,389.71,4.69
12.3,7.99248,0.0,18.1,0.0,0.7,5.52,100.0,1.5331,24.0,666.0,20.2,396.9,24.56
19.4,0.21977,0.0,6.91,0.0,0.448,5.602,62.0,6.0877,3.0,233.0,17.9,396.9,16.2
21.2,0.23912,0.0,9.69,0.0,0.585,6.019,65.3,2.4091,6.0,391.0,19.2,396.9,12.92
20.3,0.3494,0.0,9.9,0.0,0.544,5.972,76.7,3.1025,4.0,304.0,18.4,396.24,9.97
18.8,0.09849,0.0,25.65,0.0,0.581,5.879,95.8,2.0063,2.0,188.0,19.1,379.38,17.58
33.4,0.07503,33.0,2.18,0.0,0.472,7.42,71.9,3.0992,7.0,222.0,18.4,396.9,6.47
18.5,0.19133,22.0,5.86,0.0,0.431,5.605,70.2,7.9549,7.0,330.0,19.1,389.13,18.46
19.6,0.10328,25.0,5.13,0.0,0.453,5.927,47.2,6.932,8.0,284.0,19.7,396.9,9.22
33.2,0.10469,40.0,6.41,1.0,0.447,7.267,49.0,4.7872,4.0,254.0,17.6,389.25,6.05
13.1,8.71675,0.0,18.1,0.0,0.693,6.471,98.8,1.7257,24.0,666.0,20.2,391.98,17.12
7.5,10.8342,0.0,18.1,0.0,0.679,6.782,90.8,1.8195,24.0,666.0,20.2,21.57,25.79
13.6,0.10574,0.0,27.74,0.0,0.609,5.983,98.8,1.8681,4.0,711.0,20.1,390.11,18.07
17.4,1.20742,0.0,19.58,0.0,0.605,5.875,94.6,2.4259,5.0,403.0,14.7,292.29,14.43
8.4,11.8123,0.0,18.1,0.0,0.718,6.824,76.5,1.794,24.0,666.0,20.2,48.45,22.74
35.4,0.01311,90.0,1.22,0.0,0.403,7.249,21.9,8.6966,5.0,226.0,17.9,395.93,4.81
24.0,0.33045,0.0,6.2,0.0,0.507,6.086,61.5,3.6519,8.0,307.0,17.4,376.75,10.88
13.4,3.32105,0.0,19.58,1.0,0.871,5.403,100.0,1.3216,5.0,403.0,14.7,396.9,26.82
26.2,0.19073,22.0,5.86,0.0,0.431,6.718,17.5,7.8265,7.0,330.0,19.1,393.74,6.56
7.2,18.0846,0.0,18.1,0.0,0.679,6.434,100.0,1.8347,24.0,666.0,20.2,27.25,29.05
13.1,23.6482,0.0,18.1,0.0,0.671,6.38,96.2,1.3861,24.0,666.0,20.2,396.9,23.69
24.5,0.27957,0.0,9.69,0.0,0.585,5.926,42.6,2.3817,6.0,391.0,19.2,396.9,13.59
37.2,0.0578,0.0,2.46,0.0,0.488,6.98,58.4,2.829,3.0,193.0,17.8,396.9,5.04
25.0,0.0536,21.0,5.64,0.0,0.439,6.511,21.1,6.8147,4.0,243.0,16.8,396.9,5.28
24.1,0.07896,0.0,12.83,0.0,0.437,6.273,6.0,4.2515,5.0,398.0,18.7,394.92,6.78
16.6,0.67191,0.0,8.14,0.0,0.538,5.813,90.3,4.682,4.0,307.0,21.0,376.88,14.81
32.9,0.01778,95.0,1.47,0.0,0.403,7.135,13.9,7.6534,3.0,402.0,17.0,384.3,4.45
36.2,0.06905,0.0,2.18,0.0,0.458,7.147,54.2,6.0622,3.0,222.0,18.7,396.9,5.33
11.0,7.36711,0.0,18.1,0.0,0.679,6.193,78.1,1.9356,24.0,666.0,20.2,96.73,21.52
7.2,16.8118,0.0,18.1,0.0,0.7,5.277,98.1,1.4261,24.0,666.0,20.2,396.9,30.81
1 y x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13
2 22.6 0.06724 0.0 3.24 0.0 0.46 6.333 17.2 5.2146 4.0 430.0 16.9 375.21 7.34
3 50.0 9.2323 0.0 18.1 0.0 0.631 6.216 100.0 1.1691 24.0 666.0 20.2 366.15 9.53
4 23.0 0.11425 0.0 13.89 1.0 0.55 6.373 92.4 3.3633 5.0 276.0 16.4 393.74 10.5
5 8.3 24.8017 0.0 18.1 0.0 0.693 5.349 96.0 1.7028 24.0 666.0 20.2 396.9 19.77
6 21.2 0.05646 0.0 12.83 0.0 0.437 6.232 53.7 5.0141 5.0 398.0 18.7 386.4 12.34
7 19.9 0.62739 0.0 8.14 0.0 0.538 5.834 56.5 4.4986 4.0 307.0 21.0 395.62 8.47
8 20.6 4.83567 0.0 18.1 0.0 0.583 5.905 53.2 3.1523 24.0 666.0 20.2 388.22 11.45
9 18.7 0.06151 0.0 5.19 0.0 0.515 5.968 58.5 4.8122 5.0 224.0 20.2 396.9 9.29
10 16.1 2.63548 0.0 9.9 0.0 0.544 4.973 37.8 2.5194 4.0 304.0 18.4 350.45 12.64
11 18.6 0.22876 0.0 8.56 0.0 0.52 6.405 85.4 2.7147 5.0 384.0 20.9 70.8 10.63
12 8.8 73.5341 0.0 18.1 0.0 0.679 5.957 100.0 1.8026 24.0 666.0 20.2 16.45 20.62
13 17.2 14.0507 0.0 18.1 0.0 0.597 6.657 100.0 1.5275 24.0 666.0 20.2 35.05 21.22
14 14.9 6.28807 0.0 18.1 0.0 0.74 6.341 96.4 2.072 24.0 666.0 20.2 318.01 17.79
15 10.5 24.3938 0.0 18.1 0.0 0.7 4.652 100.0 1.4672 24.0 666.0 20.2 396.9 28.28
16 50.0 1.83377 0.0 19.58 1.0 0.605 7.802 98.2 2.0407 5.0 403.0 14.7 389.61 1.92
17 29.0 0.05561 70.0 2.24 0.0 0.4 7.041 10.0 7.8278 5.0 358.0 14.8 371.58 4.74
18 23.0 5.82401 0.0 18.1 0.0 0.532 6.242 64.7 3.4242 24.0 666.0 20.2 396.9 10.74
19 33.3 0.04011 80.0 1.52 0.0 0.404 7.287 34.1 7.309 2.0 329.0 12.6 396.9 4.08
20 29.4 0.06664 0.0 4.05 0.0 0.51 6.546 33.1 3.1323 5.0 296.0 16.6 390.96 5.33
21 21.0 0.08014 0.0 5.96 0.0 0.499 5.85 41.5 3.9342 5.0 279.0 19.2 396.9 8.77
22 23.8 0.1676 0.0 7.38 0.0 0.493 6.426 52.3 4.5404 5.0 287.0 19.6 396.9 7.2
23 19.1 2.3139 0.0 19.58 0.0 0.605 5.88 97.3 2.3887 5.0 403.0 14.7 348.13 12.03
24 20.4 0.13117 0.0 8.56 0.0 0.52 6.127 85.2 2.1224 5.0 384.0 20.9 387.69 14.09
25 29.1 0.07978 40.0 6.41 0.0 0.447 6.482 32.1 4.1403 4.0 254.0 17.6 396.9 7.19
26 19.3 0.17142 0.0 6.91 0.0 0.448 5.682 33.8 5.1004 3.0 233.0 17.9 396.9 10.21
27 23.1 13.5222 0.0 18.1 0.0 0.631 3.863 100.0 1.5106 24.0 666.0 20.2 131.42 13.33
28 19.6 0.85204 0.0 8.14 0.0 0.538 5.965 89.2 4.0123 4.0 307.0 21.0 392.53 13.83
29 19.4 2.14918 0.0 19.58 0.0 0.871 5.709 98.5 1.6232 5.0 403.0 14.7 261.95 15.79
30 38.7 0.12083 0.0 2.89 0.0 0.445 8.069 76.0 3.4952 2.0 276.0 18.0 396.9 4.21
31 18.7 0.22212 0.0 10.01 0.0 0.547 6.092 95.4 2.548 6.0 432.0 17.8 396.9 17.09
32 14.6 10.233 0.0 18.1 0.0 0.614 6.185 96.7 2.1705 24.0 666.0 20.2 379.7 18.03
33 20.0 6.80117 0.0 18.1 0.0 0.713 6.081 84.4 2.7175 24.0 666.0 20.2 396.9 14.7
34 20.5 0.19657 22.0 5.86 0.0 0.431 6.226 79.2 8.0555 7.0 330.0 19.1 376.14 10.15
35 20.1 0.10612 30.0 4.93 0.0 0.428 6.095 65.1 6.3361 6.0 300.0 16.6 394.62 12.4
36 23.6 0.09178 0.0 4.05 0.0 0.51 6.416 84.1 2.6463 5.0 296.0 16.6 395.5 9.04
37 16.8 4.22239 0.0 18.1 1.0 0.77 5.803 89.0 1.9047 24.0 666.0 20.2 353.04 14.64
38 5.6 25.0461 0.0 18.1 0.0 0.693 5.987 100.0 1.5888 24.0 666.0 20.2 396.9 26.77
39 50.0 8.26725 0.0 18.1 1.0 0.668 5.875 89.6 1.1296 24.0 666.0 20.2 347.88 8.88
40 14.5 8.49213 0.0 18.1 0.0 0.584 6.348 86.1 2.0527 24.0 666.0 20.2 83.45 17.64
41 13.3 6.39312 0.0 18.1 0.0 0.584 6.162 97.4 2.206 24.0 666.0 20.2 302.76 24.1
42 23.9 0.08265 0.0 13.92 0.0 0.437 6.127 18.4 5.5027 4.0 289.0 16.0 396.9 8.58
43 20.0 0.18836 0.0 6.91 0.0 0.448 5.786 33.3 5.1004 3.0 233.0 17.9 396.9 14.15
44 19.8 0.04544 0.0 3.24 0.0 0.46 6.144 32.2 5.8736 4.0 430.0 16.9 368.57 9.09
45 13.8 8.05579 0.0 18.1 0.0 0.584 5.427 95.4 2.4298 24.0 666.0 20.2 352.58 18.14
46 16.5 0.02498 0.0 1.89 0.0 0.518 6.54 59.7 6.2669 1.0 422.0 15.9 389.96 8.65
47 21.6 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0 17.8 396.9 9.14
48 20.3 0.14103 0.0 13.92 0.0 0.437 5.79 58.0 6.32 4.0 289.0 16.0 396.9 15.84
49 17.0 1.41385 0.0 19.58 1.0 0.871 6.129 96.0 1.7494 5.0 403.0 14.7 321.02 15.12
50 11.8 2.77974 0.0 19.58 0.0 0.871 4.903 97.8 1.3459 5.0 403.0 14.7 396.9 29.29
51 27.5 0.14866 0.0 8.56 0.0 0.52 6.727 79.9 2.7778 5.0 384.0 20.9 394.76 9.42
52 15.6 3.53501 0.0 19.58 1.0 0.871 6.152 82.6 1.7455 5.0 403.0 14.7 88.01 15.02
53 23.1 0.17899 0.0 9.69 0.0 0.585 5.67 28.8 2.7986 6.0 391.0 19.2 393.29 17.6
54 24.3 0.537 0.0 6.2 0.0 0.504 5.981 68.1 3.6715 8.0 307.0 17.4 378.35 11.65
55 42.8 0.36894 22.0 5.86 0.0 0.431 8.259 8.4 8.9067 7.0 330.0 19.1 396.9 3.54
56 15.6 0.97617 0.0 21.89 0.0 0.624 5.757 98.4 2.346 4.0 437.0 21.2 262.76 17.31
57 21.7 0.09378 12.5 7.87 0.0 0.524 5.889 39.0 5.4509 5.0 311.0 15.2 390.5 15.71
58 17.1 0.05023 35.0 6.06 0.0 0.4379 5.706 28.4 6.6407 1.0 304.0 16.9 394.02 12.43
59 17.2 0.06162 0.0 4.39 0.0 0.442 5.898 52.3 8.0136 3.0 352.0 18.8 364.61 12.67
60 15.0 51.1358 0.0 18.1 0.0 0.597 5.757 100.0 1.413 24.0 666.0 20.2 2.6 10.11
61 21.7 0.17446 0.0 10.59 1.0 0.489 5.96 92.1 3.8771 4.0 277.0 18.6 393.25 17.27
62 18.6 0.07244 60.0 1.69 0.0 0.411 5.884 18.5 10.7103 4.0 411.0 18.3 392.33 7.79
63 21.0 0.47547 0.0 9.9 0.0 0.544 6.113 58.8 4.0019 4.0 304.0 18.4 396.23 12.73
64 33.1 0.1 34.0 6.09 0.0 0.433 6.982 17.7 5.4917 7.0 329.0 16.1 390.43 4.86
65 31.5 0.44178 0.0 6.2 0.0 0.504 6.552 21.4 3.3751 8.0 307.0 17.4 380.34 3.76
66 20.1 13.0751 0.0 18.1 0.0 0.58 5.713 56.7 2.8237 24.0 666.0 20.2 396.9 14.76
67 29.8 0.12579 45.0 3.44 0.0 0.437 6.556 29.1 4.5667 5.0 398.0 15.2 382.84 4.56
68 15.2 5.44114 0.0 18.1 0.0 0.713 6.655 98.2 2.3552 24.0 666.0 20.2 355.29 17.73
69 15.0 0.22489 12.5 7.87 0.0 0.524 6.377 94.3 6.3467 5.0 311.0 15.2 392.52 20.45
70 27.5 14.4383 0.0 18.1 0.0 0.597 6.852 100.0 1.4655 24.0 666.0 20.2 179.36 19.78
71 22.6 0.13642 0.0 10.59 0.0 0.489 5.891 22.3 3.9454 4.0 277.0 18.6 396.9 10.87
72 20.0 0.10153 0.0 12.83 0.0 0.437 6.279 74.5 4.0522 5.0 398.0 18.7 373.66 11.97
73 21.4 0.09512 0.0 12.83 0.0 0.437 6.286 45.0 4.5026 5.0 398.0 18.7 383.23 8.94
74 23.5 0.03584 80.0 3.37 0.0 0.398 6.29 17.8 6.6115 4.0 337.0 16.1 396.9 4.67
75 31.2 0.03049 55.0 3.78 0.0 0.484 6.874 28.1 6.4654 5.0 370.0 17.6 387.97 4.61
76 23.7 5.70818 0.0 18.1 0.0 0.532 6.75 74.9 3.3317 24.0 666.0 20.2 393.07 7.74
77 7.4 22.5971 0.0 18.1 0.0 0.7 5.0 89.5 1.5184 24.0 666.0 20.2 396.9 31.99
78 48.3 0.33147 0.0 6.2 0.0 0.507 8.247 70.4 3.6519 8.0 307.0 17.4 378.95 3.95
79 24.4 0.22969 0.0 10.59 0.0 0.489 6.326 52.5 4.3549 4.0 277.0 18.6 394.87 10.97
80 22.6 0.04684 0.0 3.41 0.0 0.489 6.417 66.1 3.0923 2.0 270.0 17.8 392.18 8.81
81 18.3 0.26838 0.0 9.69 0.0 0.585 5.794 70.6 2.8927 6.0 391.0 19.2 396.9 14.1
82 23.3 0.09252 30.0 4.93 0.0 0.428 6.606 42.2 6.1899 6.0 300.0 16.6 383.78 7.37
83 17.1 0.35233 0.0 21.89 0.0 0.624 6.454 98.4 1.8498 4.0 437.0 21.2 394.08 14.59
84 27.9 11.9511 0.0 18.1 0.0 0.659 5.608 100.0 1.2852 24.0 666.0 20.2 332.09 12.13
85 44.8 0.31533 0.0 6.2 0.0 0.504 8.266 78.3 2.8944 8.0 307.0 17.4 385.05 4.14
86 50.0 0.52693 0.0 6.2 0.0 0.504 8.725 83.0 2.8944 8.0 307.0 17.4 382.0 4.63
87 23.0 0.30347 0.0 7.38 0.0 0.493 6.312 28.9 5.4159 5.0 287.0 19.6 396.9 6.15
88 21.4 0.11504 0.0 2.89 0.0 0.445 6.163 69.6 3.4952 2.0 276.0 18.0 391.83 11.34
89 10.2 12.2472 0.0 18.1 0.0 0.584 5.837 59.7 1.9976 24.0 666.0 20.2 24.65 15.69
90 23.3 1.42502 0.0 19.58 0.0 0.871 6.51 100.0 1.7659 5.0 403.0 14.7 364.31 7.39
91 23.2 5.29305 0.0 18.1 0.0 0.7 6.051 82.5 2.1678 24.0 666.0 20.2 378.38 18.76
92 18.9 0.0136 75.0 4.0 0.0 0.41 5.888 47.6 7.3197 3.0 469.0 21.1 396.9 14.8
93 13.4 11.1604 0.0 18.1 0.0 0.74 6.629 94.6 2.1247 24.0 666.0 20.2 109.85 23.27
94 21.9 0.04819 80.0 3.64 0.0 0.392 6.108 32.0 9.2203 1.0 315.0 16.4 392.89 6.57
95 24.8 0.04417 70.0 2.24 0.0 0.4 6.871 47.4 7.8278 5.0 358.0 14.8 390.86 6.07
96 11.9 0.04741 0.0 11.93 0.0 0.573 6.03 80.8 2.505 1.0 273.0 21.0 396.9 7.88
97 24.3 0.33983 22.0 5.86 0.0 0.431 6.108 34.9 8.0555 7.0 330.0 19.1 390.18 9.16
98 13.8 18.4982 0.0 18.1 0.0 0.668 4.138 100.0 1.137 24.0 666.0 20.2 396.9 37.97
99 24.7 0.02055 85.0 0.74 0.0 0.41 6.383 35.7 9.1876 2.0 313.0 17.3 396.9 5.77
100 14.1 4.75237 0.0 18.1 0.0 0.713 6.525 86.5 2.4358 24.0 666.0 20.2 50.92 18.13
101 18.7 0.14932 25.0 5.13 0.0 0.453 5.741 66.2 7.2254 8.0 284.0 19.7 395.11 13.15
102 28.1 0.14052 0.0 10.59 0.0 0.489 6.375 32.3 3.9454 4.0 277.0 18.6 385.81 9.38
103 19.8 0.12802 0.0 8.56 0.0 0.52 6.474 97.1 2.4329 5.0 384.0 20.9 395.24 12.27
104 26.7 0.35809 0.0 6.2 1.0 0.507 6.951 88.5 2.8617 8.0 307.0 17.4 391.7 9.71
105 21.7 0.15876 0.0 10.81 0.0 0.413 5.961 17.5 5.2873 4.0 305.0 19.2 376.94 9.88
106 22.0 0.11329 30.0 4.93 0.0 0.428 6.897 54.3 6.3361 6.0 300.0 16.6 391.25 11.38
107 22.9 0.08829 12.5 7.87 0.0 0.524 6.012 66.6 5.5605 5.0 311.0 15.2 395.6 12.43
108 10.4 25.9406 0.0 18.1 0.0 0.679 5.304 89.1 1.6475 24.0 666.0 20.2 127.36 26.64
109 21.9 3.69695 0.0 18.1 0.0 0.718 4.963 91.4 1.7523 24.0 666.0 20.2 316.03 14.0
110 20.6 0.04527 0.0 11.93 0.0 0.573 6.12 76.7 2.2875 1.0 273.0 21.0 396.9 9.08
111 26.4 0.09266 34.0 6.09 0.0 0.433 6.495 18.4 5.4917 7.0 329.0 16.1 383.61 8.67
112 41.3 1.22358 0.0 19.58 0.0 0.605 6.943 97.4 1.8773 5.0 403.0 14.7 363.43 4.59
113 17.2 7.40389 0.0 18.1 0.0 0.597 5.617 97.9 1.4547 24.0 666.0 20.2 314.64 26.4
114 27.1 0.14455 12.5 7.87 0.0 0.524 6.172 96.1 5.9505 5.0 311.0 15.2 396.9 19.15
115 20.4 0.13058 0.0 10.01 0.0 0.547 5.872 73.1 2.4775 6.0 432.0 17.8 338.63 15.37
116 16.5 0.21124 12.5 7.87 0.0 0.524 5.631 100.0 6.0821 5.0 311.0 15.2 386.63 29.93
117 24.4 0.13587 0.0 10.59 1.0 0.489 6.064 59.1 4.2392 4.0 277.0 18.6 381.32 14.66
118 8.4 13.6781 0.0 18.1 0.0 0.74 5.935 87.9 1.8206 24.0 666.0 20.2 68.95 34.02
119 23.0 0.59005 0.0 21.89 0.0 0.624 6.372 97.9 2.3274 4.0 437.0 21.2 385.76 11.12
120 9.7 11.5779 0.0 18.1 0.0 0.7 5.036 97.0 1.77 24.0 666.0 20.2 396.9 25.68
121 50.0 0.01501 90.0 1.21 1.0 0.401 7.923 24.8 5.885 1.0 198.0 13.6 395.52 3.16
122 30.5 0.06911 45.0 3.44 0.0 0.437 6.739 30.8 6.4798 5.0 398.0 15.2 389.71 4.69
123 12.3 7.99248 0.0 18.1 0.0 0.7 5.52 100.0 1.5331 24.0 666.0 20.2 396.9 24.56
124 19.4 0.21977 0.0 6.91 0.0 0.448 5.602 62.0 6.0877 3.0 233.0 17.9 396.9 16.2
125 21.2 0.23912 0.0 9.69 0.0 0.585 6.019 65.3 2.4091 6.0 391.0 19.2 396.9 12.92
126 20.3 0.3494 0.0 9.9 0.0 0.544 5.972 76.7 3.1025 4.0 304.0 18.4 396.24 9.97
127 18.8 0.09849 0.0 25.65 0.0 0.581 5.879 95.8 2.0063 2.0 188.0 19.1 379.38 17.58
128 33.4 0.07503 33.0 2.18 0.0 0.472 7.42 71.9 3.0992 7.0 222.0 18.4 396.9 6.47
129 18.5 0.19133 22.0 5.86 0.0 0.431 5.605 70.2 7.9549 7.0 330.0 19.1 389.13 18.46
130 19.6 0.10328 25.0 5.13 0.0 0.453 5.927 47.2 6.932 8.0 284.0 19.7 396.9 9.22
131 33.2 0.10469 40.0 6.41 1.0 0.447 7.267 49.0 4.7872 4.0 254.0 17.6 389.25 6.05
132 13.1 8.71675 0.0 18.1 0.0 0.693 6.471 98.8 1.7257 24.0 666.0 20.2 391.98 17.12
133 7.5 10.8342 0.0 18.1 0.0 0.679 6.782 90.8 1.8195 24.0 666.0 20.2 21.57 25.79
134 13.6 0.10574 0.0 27.74 0.0 0.609 5.983 98.8 1.8681 4.0 711.0 20.1 390.11 18.07
135 17.4 1.20742 0.0 19.58 0.0 0.605 5.875 94.6 2.4259 5.0 403.0 14.7 292.29 14.43
136 8.4 11.8123 0.0 18.1 0.0 0.718 6.824 76.5 1.794 24.0 666.0 20.2 48.45 22.74
137 35.4 0.01311 90.0 1.22 0.0 0.403 7.249 21.9 8.6966 5.0 226.0 17.9 395.93 4.81
138 24.0 0.33045 0.0 6.2 0.0 0.507 6.086 61.5 3.6519 8.0 307.0 17.4 376.75 10.88
139 13.4 3.32105 0.0 19.58 1.0 0.871 5.403 100.0 1.3216 5.0 403.0 14.7 396.9 26.82
140 26.2 0.19073 22.0 5.86 0.0 0.431 6.718 17.5 7.8265 7.0 330.0 19.1 393.74 6.56
141 7.2 18.0846 0.0 18.1 0.0 0.679 6.434 100.0 1.8347 24.0 666.0 20.2 27.25 29.05
142 13.1 23.6482 0.0 18.1 0.0 0.671 6.38 96.2 1.3861 24.0 666.0 20.2 396.9 23.69
143 24.5 0.27957 0.0 9.69 0.0 0.585 5.926 42.6 2.3817 6.0 391.0 19.2 396.9 13.59
144 37.2 0.0578 0.0 2.46 0.0 0.488 6.98 58.4 2.829 3.0 193.0 17.8 396.9 5.04
145 25.0 0.0536 21.0 5.64 0.0 0.439 6.511 21.1 6.8147 4.0 243.0 16.8 396.9 5.28
146 24.1 0.07896 0.0 12.83 0.0 0.437 6.273 6.0 4.2515 5.0 398.0 18.7 394.92 6.78
147 16.6 0.67191 0.0 8.14 0.0 0.538 5.813 90.3 4.682 4.0 307.0 21.0 376.88 14.81
148 32.9 0.01778 95.0 1.47 0.0 0.403 7.135 13.9 7.6534 3.0 402.0 17.0 384.3 4.45
149 36.2 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0 18.7 396.9 5.33
150 11.0 7.36711 0.0 18.1 0.0 0.679 6.193 78.1 1.9356 24.0 666.0 20.2 96.73 21.52
151 7.2 16.8118 0.0 18.1 0.0 0.7 5.277 98.1 1.4261 24.0 666.0 20.2 396.9 30.81

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