size_multivariate.py

import numpy as np

# 共分散行列
#   身長          バスト　　　　ウエスト　　　ヒップ　　　　肩　　　　　　足　　　　　　体重
cov = np.array([
    [25.3009,     0.063088785, 0.087173585, 0.095501117, 4.170708501, 14.37817704, 0.294065688],
    [0.063088785, 0.004163135, 0.003628662, 0.002353277, 0.034573308, 0.027373251, 0.006175157],
    [0.087173585, 0.003628662, 0.005121,    0.002843,    0.050031,    0.041155,    0.007291], 
    [0.095501117, 0.002353277, 0.002843,    0.002701,    0.0439,      0.035186,    0.005578],
    [4.170708501, 0.034573308, 0.050031,    0.0439,      4.4521,      1.936558,    0.110692],
    [14.37817704, 0.027373251, 0.041155,    0.035186,    1.936558,    12.1801,     0.13921],
    [0.294065688, 0.006175157, 0.007291,    0.005578,    0.110692,    0.13921,     0.01494]
])
# 各項目の平均値
mu = np.array([158.46, 83.45, 62.8, 90.57, 39.23, 70.99, 51.35])
# 対数変換した後の平均値を入れるべき？
# mu = np.array([158.46, 4.422166082, 4.137394, 4.504773, 39.23, 70.99, 3.931195])

print( np.random.multivariate_normal(mu, cov, size=10) )

# →multivariate_normalで出てきた値をさらに指数変換する

# result
# [[157.17653868  83.52152686  62.82307351  90.5356509   37.4999695
#    67.84633902  51.32360976]
#  [156.40902894  83.62107797  62.94272436  90.678572    38.84426543
#    64.91747625  51.55203028]
#  [160.8320308   83.47769574  62.7833447   90.58030217  39.30731665
#    74.27452667  51.35459517]
#  [163.09097556  83.38081934  62.81326899  90.57274512  42.05391359
#    74.87221416  51.36223029]
#  [159.30544013  83.41298845  62.78960707  90.62564372  35.49619009
#    68.90771185  51.4218314 ]
#  [168.4264203   83.4412245   62.79086057  90.58902808  40.03612539
#    75.78174051  51.33472428]
#  [156.16951822  83.43898476  62.83653992  90.54572375  40.54825291
#    69.64772774  51.29420731]
#  [166.2083519   83.51764631  62.8186502   90.54347489  39.66573045
#    76.32497089  51.35023325]
#  [166.0839105   83.44015962  62.90650903  90.61517912  40.17319839
#    77.88834217  51.58731256]
#  [158.87783237  83.3038592   62.70456824  90.52824499  34.72611335
#    69.38327928  51.20675053]]