Задача.

Загрузить набор данных Wine.

Первый атрибут -- номер класса.

Построить изображения проекций (на одном рисунке) набора данных на избранные пары значений признаков (выбрать некоторым образом 12 различных пар признаков).

Методом главных компонент постройте сокращенный набор, содержащий наиболее существенные признаки (главные компоненты).

Построить изображения проекций (на одном рисунке) набора данных по всем парам выбранных новых признаков

import random as rnd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

attrs = """class_id
Alcohol
Malic acid
Ash
Alcalinity of ash
Magnesium
Total phenols
Flavanoids
Nonflavanoid phenols
Proanthocyanins
Color intensity
Hue
OD280/OD315 of diluted wines
Proline""".split('\n')
print(attrs)

['class_id', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280/OD315 of diluted wines', 'Proline']

wine_df=pd.read_csv("data/wine.data", sep=",", names=attrs)

wine_df

List=[]
for i in range(13):
    for j in range(i+1,13):
        List.append([i+1,j+1])

pair=[]
i=0
while i < 12:
    tmp = rnd.choice(List)
    if tmp not in pair:
        pair.append(tmp)
        i=i+1
    else:
        continue
print(pair)

[[4, 7], [7, 13], [5, 11], [2, 8], [1, 10], [10, 13], [3, 8], [6, 7], [7, 11], [4, 10], [4, 11], [8, 11]]

plt.figure(figsize=(18.0, 10.0))
for i in range(12):
    plt.subplot(3, 4, i+1)
    plt.scatter(wine_df[attrs[pair[i][0]]], wine_df[attrs[pair[i][1]]], s=25, c=wine_df[attrs[0]])
    plt.xlabel(attrs[pair[i][0]])
    plt.ylabel(attrs[pair[i][1]])
    plt.grid(1)
plt.tight_layout()
plt.show()

cols = [wine_df[attr] for attr in attrs[:]]
print(cols)

[0      1
1      1
2      1
3      1
4      1
5      1
6      1
7      1
8      1
9      1
10     1
11     1
12     1
13     1
14     1
15     1
16     1
17     1
18     1
19     1
20     1
21     1
22     1
23     1
24     1
25     1
26     1
27     1
28     1
29     1
      ..
148    3
149    3
150    3
151    3
152    3
153    3
154    3
155    3
156    3
157    3
158    3
159    3
160    3
161    3
162    3
163    3
164    3
165    3
166    3
167    3
168    3
169    3
170    3
171    3
172    3
173    3
174    3
175    3
176    3
177    3
Name: class_id, dtype: int64, 0      14.23
1      13.20
2      13.16
3      14.37
4      13.24
5      14.20
6      14.39
7      14.06
8      14.83
9      13.86
10     14.10
11     14.12
12     13.75
13     14.75
14     14.38
15     13.63
16     14.30
17     13.83
18     14.19
19     13.64
20     14.06
21     12.93
22     13.71
23     12.85
24     13.50
25     13.05
26     13.39
27     13.30
28     13.87
29     14.02
       ...  
148    13.32
149    13.08
150    13.50
151    12.79
152    13.11
153    13.23
154    12.58
155    13.17
156    13.84
157    12.45
158    14.34
159    13.48
160    12.36
161    13.69
162    12.85
163    12.96
164    13.78
165    13.73
166    13.45
167    12.82
168    13.58
169    13.40
170    12.20
171    12.77
172    14.16
173    13.71
174    13.40
175    13.27
176    13.17
177    14.13
Name: Alcohol, dtype: float64, 0      1.71
1      1.78
2      2.36
3      1.95
4      2.59
5      1.76
6      1.87
7      2.15
8      1.64
9      1.35
10     2.16
11     1.48
12     1.73
13     1.73
14     1.87
15     1.81
16     1.92
17     1.57
18     1.59
19     3.10
20     1.63
21     3.80
22     1.86
23     1.60
24     1.81
25     2.05
26     1.77
27     1.72
28     1.90
29     1.68
       ... 
148    3.24
149    3.90
150    3.12
151    2.67
152    1.90
153    3.30
154    1.29
155    5.19
156    4.12
157    3.03
158    1.68
159    1.67
160    3.83
161    3.26
162    3.27
163    3.45
164    2.76
165    4.36
166    3.70
167    3.37
168    2.58
169    4.60
170    3.03
171    2.39
172    2.51
173    5.65
174    3.91
175    4.28
176    2.59
177    4.10
Name: Malic acid, dtype: float64, 0      2.43
1      2.14
2      2.67
3      2.50
4      2.87
5      2.45
6      2.45
7      2.61
8      2.17
9      2.27
10     2.30
11     2.32
12     2.41
13     2.39
14     2.38
15     2.70
16     2.72
17     2.62
18     2.48
19     2.56
20     2.28
21     2.65
22     2.36
23     2.52
24     2.61
25     3.22
26     2.62
27     2.14
28     2.80
29     2.21
       ... 
148    2.38
149    2.36
150    2.62
151    2.48
152    2.75
153    2.28
154    2.10
155    2.32
156    2.38
157    2.64
158    2.70
159    2.64
160    2.38
161    2.54
162    2.58
163    2.35
164    2.30
165    2.26
166    2.60
167    2.30
168    2.69
169    2.86
170    2.32
171    2.28
172    2.48
173    2.45
174    2.48
175    2.26
176    2.37
177    2.74
Name: Ash, dtype: float64, 0      15.6
1      11.2
2      18.6
3      16.8
4      21.0
5      15.2
6      14.6
7      17.6
8      14.0
9      16.0
10     18.0
11     16.8
12     16.0
13     11.4
14     12.0
15     17.2
16     20.0
17     20.0
18     16.5
19     15.2
20     16.0
21     18.6
22     16.6
23     17.8
24     20.0
25     25.0
26     16.1
27     17.0
28     19.4
29     16.0
       ... 
148    21.5
149    21.5
150    24.0
151    22.0
152    25.5
153    18.5
154    20.0
155    22.0
156    19.5
157    27.0
158    25.0
159    22.5
160    21.0
161    20.0
162    22.0
163    18.5
164    22.0
165    22.5
166    23.0
167    19.5
168    24.5
169    25.0
170    19.0
171    19.5
172    20.0
173    20.5
174    23.0
175    20.0
176    20.0
177    24.5
Name: Alcalinity of ash, dtype: float64, 0      127
1      100
2      101
3      113
4      118
5      112
6       96
7      121
8       97
9       98
10     105
11      95
12      89
13      91
14     102
15     112
16     120
17     115
18     108
19     116
20     126
21     102
22     101
23      95
24      96
25     124
26      93
27      94
28     107
29      96
      ... 
148     92
149    113
150    123
151    112
152    116
153     98
154    103
155     93
156     89
157     97
158     98
159     89
160     88
161    107
162    106
163    106
164     90
165     88
166    111
167     88
168    105
169    112
170     96
171     86
172     91
173     95
174    102
175    120
176    120
177     96
Name: Magnesium, dtype: int64, 0      2.80
1      2.65
2      2.80
3      3.85
4      2.80
5      3.27
6      2.50
7      2.60
8      2.80
9      2.98
10     2.95
11     2.20
12     2.60
13     3.10
14     3.30
15     2.85
16     2.80
17     2.95
18     3.30
19     2.70
20     3.00
21     2.41
22     2.61
23     2.48
24     2.53
25     2.63
26     2.85
27     2.40
28     2.95
29     2.65
       ... 
148    1.93
149    1.41
150    1.40
151    1.48
152    2.20
153    1.80
154    1.48
155    1.74
156    1.80
157    1.90
158    2.80
159    2.60
160    2.30
161    1.83
162    1.65
163    1.39
164    1.35
165    1.28
166    1.70
167    1.48
168    1.55
169    1.98
170    1.25
171    1.39
172    1.68
173    1.68
174    1.80
175    1.59
176    1.65
177    2.05
Name: Total phenols, dtype: float64, 0      3.06
1      2.76
2      3.24
3      3.49
4      2.69
5      3.39
6      2.52
7      2.51
8      2.98
9      3.15
10     3.32
11     2.43
12     2.76
13     3.69
14     3.64
15     2.91
16     3.14
17     3.40
18     3.93
19     3.03
20     3.17
21     2.41
22     2.88
23     2.37
24     2.61
25     2.68
26     2.94
27     2.19
28     2.97
29     2.33
       ... 
148    0.76
149    1.39
150    1.57
151    1.36
152    1.28
153    0.83
154    0.58
155    0.63
156    0.83
157    0.58
158    1.31
159    1.10
160    0.92
161    0.56
162    0.60
163    0.70
164    0.68
165    0.47
166    0.92
167    0.66
168    0.84
169    0.96
170    0.49
171    0.51
172    0.70
173    0.61
174    0.75
175    0.69
176    0.68
177    0.76
Name: Flavanoids, dtype: float64, 0      0.28
1      0.26
2      0.30
3      0.24
4      0.39
5      0.34
6      0.30
7      0.31
8      0.29
9      0.22
10     0.22
11     0.26
12     0.29
13     0.43
14     0.29
15     0.30
16     0.33
17     0.40
18     0.32
19     0.17
20     0.24
21     0.25
22     0.27
23     0.26
24     0.28
25     0.47
26     0.34
27     0.27
28     0.37
29     0.26
       ... 
148    0.45
149    0.34
150    0.22
151    0.24
152    0.26
153    0.61
154    0.53
155    0.61
156    0.48
157    0.63
158    0.53
159    0.52
160    0.50
161    0.50
162    0.60
163    0.40
164    0.41
165    0.52
166    0.43
167    0.40
168    0.39
169    0.27
170    0.40
171    0.48
172    0.44
173    0.52
174    0.43
175    0.43
176    0.53
177    0.56
Name: Nonflavanoid phenols, dtype: float64, 0      2.29
1      1.28
2      2.81
3      2.18
4      1.82
5      1.97
6      1.98
7      1.25
8      1.98
9      1.85
10     2.38
11     1.57
12     1.81
13     2.81
14     2.96
15     1.46
16     1.97
17     1.72
18     1.86
19     1.66
20     2.10
21     1.98
22     1.69
23     1.46
24     1.66
25     1.92
26     1.45
27     1.35
28     1.76
29     1.98
       ... 
148    1.25
149    1.14
150    1.25
151    1.26
152    1.56
153    1.87
154    1.40
155    1.55
156    1.56
157    1.14
158    2.70
159    2.29
160    1.04
161    0.80
162    0.96
163    0.94
164    1.03
165    1.15
166    1.46
167    0.97
168    1.54
169    1.11
170    0.73
171    0.64
172    1.24
173    1.06
174    1.41
175    1.35
176    1.46
177    1.35
Name: Proanthocyanins, dtype: float64, 0       5.640000
1       4.380000
2       5.680000
3       7.800000
4       4.320000
5       6.750000
6       5.250000
7       5.050000
8       5.200000
9       7.220000
10      5.750000
11      5.000000
12      5.600000
13      5.400000
14      7.500000
15      7.300000
16      6.200000
17      6.600000
18      8.700000
19      5.100000
20      5.650000
21      4.500000
22      3.800000
23      3.930000
24      3.520000
25      3.580000
26      4.800000
27      3.950000
28      4.500000
29      4.700000
         ...    
148     8.420000
149     9.400000
150     8.600000
151    10.800000
152     7.100000
153    10.520000
154     7.600000
155     7.900000
156     9.010000
157     7.500000
158    13.000000
159    11.750000
160     7.650000
161     5.880000
162     5.580000
163     5.280000
164     9.580000
165     6.620000
166    10.680000
167    10.260000
168     8.660000
169     8.500000
170     5.500000
171     9.899999
172     9.700000
173     7.700000
174     7.300000
175    10.200000
176     9.300000
177     9.200000
Name: Color intensity, dtype: float64, 0      1.04
1      1.05
2      1.03
3      0.86
4      1.04
5      1.05
6      1.02
7      1.06
8      1.08
9      1.01
10     1.25
11     1.17
12     1.15
13     1.25
14     1.20
15     1.28
16     1.07
17     1.13
18     1.23
19     0.96
20     1.09
21     1.03
22     1.11
23     1.09
24     1.12
25     1.13
26     0.92
27     1.02
28     1.25
29     1.04
       ... 
148    0.55
149    0.57
150    0.59
151    0.48
152    0.61
153    0.56
154    0.58
155    0.60
156    0.57
157    0.67
158    0.57
159    0.57
160    0.56
161    0.96
162    0.87
163    0.68
164    0.70
165    0.78
166    0.85
167    0.72
168    0.74
169    0.67
170    0.66
171    0.57
172    0.62
173    0.64
174    0.70
175    0.59
176    0.60
177    0.61
Name: Hue, dtype: float64, 0      3.92
1      3.40
2      3.17
3      3.45
4      2.93
5      2.85
6      3.58
7      3.58
8      2.85
9      3.55
10     3.17
11     2.82
12     2.90
13     2.73
14     3.00
15     2.88
16     2.65
17     2.57
18     2.82
19     3.36
20     3.71
21     3.52
22     4.00
23     3.63
24     3.82
25     3.20
26     3.22
27     2.77
28     3.40
29     3.59
       ... 
148    1.62
149    1.33
150    1.30
151    1.47
152    1.33
153    1.51
154    1.55
155    1.48
156    1.64
157    1.73
158    1.96
159    1.78
160    1.58
161    1.82
162    2.11
163    1.75
164    1.68
165    1.75
166    1.56
167    1.75
168    1.80
169    1.92
170    1.83
171    1.63
172    1.71
173    1.74
174    1.56
175    1.56
176    1.62
177    1.60
Name: OD280/OD315 of diluted wines, dtype: float64, 0      1065
1      1050
2      1185
3      1480
4       735
5      1450
6      1290
7      1295
8      1045
9      1045
10     1510
11     1280
12     1320
13     1150
14     1547
15     1310
16     1280
17     1130
18     1680
19      845
20      780
21      770
22     1035
23     1015
24      845
25      830
26     1195
27     1285
28      915
29     1035
       ... 
148     650
149     550
150     500
151     480
152     425
153     675
154     640
155     725
156     480
157     880
158     660
159     620
160     520
161     680
162     570
163     675
164     615
165     520
166     695
167     685
168     750
169     630
170     510
171     470
172     660
173     740
174     750
175     835
176     840
177     560
Name: Proline, dtype: int64]

x=cols[1].values
for i in range(2,14):
    x=np.c_[x,cols[i].values]
y = cols[0].values
print(x)
print(y)

[[  1.42300000e+01   1.71000000e+00   2.43000000e+00 ...,   1.04000000e+00
    3.92000000e+00   1.06500000e+03]
 [  1.32000000e+01   1.78000000e+00   2.14000000e+00 ...,   1.05000000e+00
    3.40000000e+00   1.05000000e+03]
 [  1.31600000e+01   2.36000000e+00   2.67000000e+00 ...,   1.03000000e+00
    3.17000000e+00   1.18500000e+03]
 ..., 
 [  1.32700000e+01   4.28000000e+00   2.26000000e+00 ...,   5.90000000e-01
    1.56000000e+00   8.35000000e+02]
 [  1.31700000e+01   2.59000000e+00   2.37000000e+00 ...,   6.00000000e-01
    1.62000000e+00   8.40000000e+02]
 [  1.41300000e+01   4.10000000e+00   2.74000000e+00 ...,   6.10000000e-01
    1.60000000e+00   5.60000000e+02]]
[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 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3]

import sklearn.decomposition as decomposition

pca = decomposition.PCA()

pca.fit(x)

PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False)

pca.explained_variance_ratio_

array([  9.98091230e-01,   1.73591562e-03,   9.49589576e-05,
         5.02173562e-05,   1.23636847e-05,   8.46213034e-06,
         2.80681456e-06,   1.52308053e-06,   1.12783044e-06,
         7.21415811e-07,   3.78060267e-07,   2.12013755e-07,
         8.25392788e-08])

plt.figure(figsize=(10,8))
plt.bar(range(1,14),pca.explained_variance_ratio_)
plt.grid(1)
plt.minorticks_on()
plt.show()

pca.n_components = 2
U = pca.fit_transform(x)
print(U.shape)
print(pca.noise_variance_)

(178, 2)
1.55306269038

plt.figure(figsize=(5.0,10.0))
plt.scatter(U[:, 0], U[:,1], s=25, c=y)
plt.xlabel("Компонент 1")
plt.ylabel("Компонент 2")
plt.grid(1)
plt.show()

	class_id	Alcohol	Malic acid	Ash	Alcalinity of ash	Magnesium	Total phenols	Flavanoids	Nonflavanoid phenols	Proanthocyanins	Color intensity	Hue	OD280/OD315 of diluted wines	Proline
0	1	14.23	1.71	2.43	15.6	127	2.80	3.06	0.28	2.29	5.640000	1.04	3.92	1065
1	1	13.20	1.78	2.14	11.2	100	2.65	2.76	0.26	1.28	4.380000	1.05	3.40	1050
2	1	13.16	2.36	2.67	18.6	101	2.80	3.24	0.30	2.81	5.680000	1.03	3.17	1185
3	1	14.37	1.95	2.50	16.8	113	3.85	3.49	0.24	2.18	7.800000	0.86	3.45	1480
4	1	13.24	2.59	2.87	21.0	118	2.80	2.69	0.39	1.82	4.320000	1.04	2.93	735
5	1	14.20	1.76	2.45	15.2	112	3.27	3.39	0.34	1.97	6.750000	1.05	2.85	1450
6	1	14.39	1.87	2.45	14.6	96	2.50	2.52	0.30	1.98	5.250000	1.02	3.58	1290
7	1	14.06	2.15	2.61	17.6	121	2.60	2.51	0.31	1.25	5.050000	1.06	3.58	1295
8	1	14.83	1.64	2.17	14.0	97	2.80	2.98	0.29	1.98	5.200000	1.08	2.85	1045
9	1	13.86	1.35	2.27	16.0	98	2.98	3.15	0.22	1.85	7.220000	1.01	3.55	1045
10	1	14.10	2.16	2.30	18.0	105	2.95	3.32	0.22	2.38	5.750000	1.25	3.17	1510
11	1	14.12	1.48	2.32	16.8	95	2.20	2.43	0.26	1.57	5.000000	1.17	2.82	1280
12	1	13.75	1.73	2.41	16.0	89	2.60	2.76	0.29	1.81	5.600000	1.15	2.90	1320
13	1	14.75	1.73	2.39	11.4	91	3.10	3.69	0.43	2.81	5.400000	1.25	2.73	1150
14	1	14.38	1.87	2.38	12.0	102	3.30	3.64	0.29	2.96	7.500000	1.20	3.00	1547
15	1	13.63	1.81	2.70	17.2	112	2.85	2.91	0.30	1.46	7.300000	1.28	2.88	1310
16	1	14.30	1.92	2.72	20.0	120	2.80	3.14	0.33	1.97	6.200000	1.07	2.65	1280
17	1	13.83	1.57	2.62	20.0	115	2.95	3.40	0.40	1.72	6.600000	1.13	2.57	1130
18	1	14.19	1.59	2.48	16.5	108	3.30	3.93	0.32	1.86	8.700000	1.23	2.82	1680
19	1	13.64	3.10	2.56	15.2	116	2.70	3.03	0.17	1.66	5.100000	0.96	3.36	845
20	1	14.06	1.63	2.28	16.0	126	3.00	3.17	0.24	2.10	5.650000	1.09	3.71	780
21	1	12.93	3.80	2.65	18.6	102	2.41	2.41	0.25	1.98	4.500000	1.03	3.52	770
22	1	13.71	1.86	2.36	16.6	101	2.61	2.88	0.27	1.69	3.800000	1.11	4.00	1035
23	1	12.85	1.60	2.52	17.8	95	2.48	2.37	0.26	1.46	3.930000	1.09	3.63	1015
24	1	13.50	1.81	2.61	20.0	96	2.53	2.61	0.28	1.66	3.520000	1.12	3.82	845
25	1	13.05	2.05	3.22	25.0	124	2.63	2.68	0.47	1.92	3.580000	1.13	3.20	830
26	1	13.39	1.77	2.62	16.1	93	2.85	2.94	0.34	1.45	4.800000	0.92	3.22	1195
27	1	13.30	1.72	2.14	17.0	94	2.40	2.19	0.27	1.35	3.950000	1.02	2.77	1285
28	1	13.87	1.90	2.80	19.4	107	2.95	2.97	0.37	1.76	4.500000	1.25	3.40	915
29	1	14.02	1.68	2.21	16.0	96	2.65	2.33	0.26	1.98	4.700000	1.04	3.59	1035
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
148	3	13.32	3.24	2.38	21.5	92	1.93	0.76	0.45	1.25	8.420000	0.55	1.62	650
149	3	13.08	3.90	2.36	21.5	113	1.41	1.39	0.34	1.14	9.400000	0.57	1.33	550
150	3	13.50	3.12	2.62	24.0	123	1.40	1.57	0.22	1.25	8.600000	0.59	1.30	500
151	3	12.79	2.67	2.48	22.0	112	1.48	1.36	0.24	1.26	10.800000	0.48	1.47	480
152	3	13.11	1.90	2.75	25.5	116	2.20	1.28	0.26	1.56	7.100000	0.61	1.33	425
153	3	13.23	3.30	2.28	18.5	98	1.80	0.83	0.61	1.87	10.520000	0.56	1.51	675
154	3	12.58	1.29	2.10	20.0	103	1.48	0.58	0.53	1.40	7.600000	0.58	1.55	640
155	3	13.17	5.19	2.32	22.0	93	1.74	0.63	0.61	1.55	7.900000	0.60	1.48	725
156	3	13.84	4.12	2.38	19.5	89	1.80	0.83	0.48	1.56	9.010000	0.57	1.64	480
157	3	12.45	3.03	2.64	27.0	97	1.90	0.58	0.63	1.14	7.500000	0.67	1.73	880
158	3	14.34	1.68	2.70	25.0	98	2.80	1.31	0.53	2.70	13.000000	0.57	1.96	660
159	3	13.48	1.67	2.64	22.5	89	2.60	1.10	0.52	2.29	11.750000	0.57	1.78	620
160	3	12.36	3.83	2.38	21.0	88	2.30	0.92	0.50	1.04	7.650000	0.56	1.58	520
161	3	13.69	3.26	2.54	20.0	107	1.83	0.56	0.50	0.80	5.880000	0.96	1.82	680
162	3	12.85	3.27	2.58	22.0	106	1.65	0.60	0.60	0.96	5.580000	0.87	2.11	570
163	3	12.96	3.45	2.35	18.5	106	1.39	0.70	0.40	0.94	5.280000	0.68	1.75	675
164	3	13.78	2.76	2.30	22.0	90	1.35	0.68	0.41	1.03	9.580000	0.70	1.68	615
165	3	13.73	4.36	2.26	22.5	88	1.28	0.47	0.52	1.15	6.620000	0.78	1.75	520
166	3	13.45	3.70	2.60	23.0	111	1.70	0.92	0.43	1.46	10.680000	0.85	1.56	695
167	3	12.82	3.37	2.30	19.5	88	1.48	0.66	0.40	0.97	10.260000	0.72	1.75	685
168	3	13.58	2.58	2.69	24.5	105	1.55	0.84	0.39	1.54	8.660000	0.74	1.80	750
169	3	13.40	4.60	2.86	25.0	112	1.98	0.96	0.27	1.11	8.500000	0.67	1.92	630
170	3	12.20	3.03	2.32	19.0	96	1.25	0.49	0.40	0.73	5.500000	0.66	1.83	510
171	3	12.77	2.39	2.28	19.5	86	1.39	0.51	0.48	0.64	9.899999	0.57	1.63	470
172	3	14.16	2.51	2.48	20.0	91	1.68	0.70	0.44	1.24	9.700000	0.62	1.71	660
173	3	13.71	5.65	2.45	20.5	95	1.68	0.61	0.52	1.06	7.700000	0.64	1.74	740
174	3	13.40	3.91	2.48	23.0	102	1.80	0.75	0.43	1.41	7.300000	0.70	1.56	750
175	3	13.27	4.28	2.26	20.0	120	1.59	0.69	0.43	1.35	10.200000	0.59	1.56	835
176	3	13.17	2.59	2.37	20.0	120	1.65	0.68	0.53	1.46	9.300000	0.60	1.62	840
177	3	14.13	4.10	2.74	24.5	96	2.05	0.76	0.56	1.35	9.200000	0.61	1.60	560