# J_r 残差のヤコビアン（m×n行列、全要素ゼロで初期化）
J_r = np.zeros(( len( theFullPNs['f'] ), len( theFullPNs['b'] )))
J_r_next = copy.deepcopy( J_r )
# βの現在値のリスト
p = []
for b in theFullPNs['b']:
p.append( PARAMETERS[ b ] )
# トレーニングデータを格納した辞書
target_data_dict = {}
for FullPN, ECDFileName in CURVE_DATA_DICT.items():
aTimeCouse = ECDDataFile()
aTimeCouse.load( os.sep.join(( CURVE_DATA_DIR.rstrip( os.sep ), ECDFileName )) )
target_data_dict[ FullPN ] = getTargetDataPoints( aTimeCouse.getData(), T_START, T_END, T_INTERVAL )
"""
for FullPN, tc in target_data_dict.items():
print "\n" + FullPN
for dp in tc:
print "{} : {}".format( dp[0], dp[1] )
"""
# --------------------------------------------------------
# (3) 反復計算
# --------------------------------------------------------
beta_prev = copy.deepcopy( beta_dict )
for i in range( MAX_GENERATION ):
# J_r 残差のヤコビアン（m×n行列、全要素ゼロで初期化）
J_r = np.zeros((len(theFullPNs['f']), len(theFullPNs['b'])))
J_r_next = copy.deepcopy(J_r)
# βの現在値のリスト
p = []
for b in theFullPNs['b']:
p.append(PARAMETERS[b])
# トレーニングデータを格納した辞書
target_data_dict = {}
for FullPN, ECDFileName in CURVE_DATA_DICT.items():
aTimeCouse = ECDDataFile()
aTimeCouse.load(os.sep.join((CURVE_DATA_DIR.rstrip(os.sep), ECDFileName)))
target_data_dict[FullPN] = getTargetDataPoints(aTimeCouse.getData(),
T_START, T_END, T_INTERVAL)
"""
for FullPN, tc in target_data_dict.items():
print "\n" + FullPN
for dp in tc:
print "{} : {}".format( dp[0], dp[1] )
"""
# --------------------------------------------------------
# (3) 反復計算
# --------------------------------------------------------
LAMBDA = LAMBDA_0
beta_prev = copy.deepcopy(beta_dict)
S_prev = float('inf')
