我有以下脚本：
import pandas as pd
import numpy as np
import os
import scipy.stats as sp
raw = pd.read_csv("ELT_RawData.csv")
raw["weight"] = raw["RATE"] / np.sum(raw["RATE"])
MU = np.sum(raw["RATE"])
poisson = sp.poisson(mu=MU)
n_simulations = 10000
xs = 5e6
lim = 10e6
result = np.empty((n_simulations))
for i in range(n_simulations):
no_clm = poisson.rvs(size=1)
#simulate beta distribution
sampled_events = raw.sample(n=no_clm,weights=raw["weight"])
#calculate params for beta distribution
a_1 = ( (sampled_events["PERSPVALUE"] / (sampled_events["STDDEVI"]+sampled_events["STDDEVC"]))**2*(1 - sampled_events["PERSPVALUE"]/sampled_events["EXPVALUE"]) - \
sampled_events["PERSPVALUE"] / sampled_events["EXPVALUE"] )
b_1 = a_1*(sampled_events["EXPVALUE"]/sampled_events["PERSPVALUE"]-1)
#simulate from said distribution
clm_sev = np.array(sp.beta(a_1,b_1).rvs(no_clm)*sampled_events["EXPVALUE"])
#apply features
xol_loss = np.clip(a=clm_sev-xs,a_min=0,a_max=lim)
#store result
result[i] = xol_loss.sum()
result.mean()
rawraw = pd.DataFrame.from_dict({'EVENTID': {0: 'A_0', 1: 'A_1', 2: 'A_2', 3: 'A_3', 4: 'A_4', 5: 'A_5', 6: 'A_6', 7: 'A_7', 8: 'A_8', 9: 'A_9', 10: 'A_10', 11: 'A_11', 12: 'A_12', 13: 'A_13', 14: 'A_14', 15: 'A_15', 16: 'A_16', 17: 'A_17', 18: 'A_18', 19: 'A_19', 20: 'A_20', 21: 'A_21', 22: 'A_22', 23: 'A_23', 24: 'A_24', 25: 'A_25', 26: 'A_26', 27: 'A_27', 28: 'A_28', 29: 'A_29', 30: 'A_30', 31: 'A_31', 32: 'A_32', 33: 'A_33', 34: 'A_34', 35: 'A_35', 36: 'A_36', 37: 'A_37', 38: 'A_38', 39: 'A_39', 40: 'A_40', 41: 'A_41', 42: 'A_42', 43: 'A_43', 44: 'A_44', 45: 'A_45', 46: 'A_46', 47: 'A_47', 48: 'A_48', 49: 'A_49', 50: 'A_50', 51: 'A_51', 52: 'A_52', 53: 'A_53', 54: 'A_54', 55: 'A_55', 56: 'A_56', 57: 'A_57', 58: 'A_58', 59: 'A_59', 60: 'A_60', 61: 'A_61', 62: 'A_62', 63: 'A_63', 64: 'A_64', 65: 'A_65', 66: 'A_66', 67: 'A_67', 68: 'A_68', 69: 'A_69', 70: 'A_70', 71: 'A_71', 72: 'A_72', 73: 'A_73', 74: 'A_74', 75: 'A_75', 76: 'A_76', 77: 'A_77', 78: 'A_78', 79: 'A_79', 80: 'A_80', 81: 'A_81', 82: 'A_82', 83: 'A_83', 84: 'A_84', 85: 'A_85', 86: 'A_86', 87: 'A_87', 88: 'A_88', 89: 'A_89', 90: 'A_90', 91: 'A_91', 92: 'A_92', 93: 'A_93', 94: 'A_94', 95: 'A_95', 96: 'A_96', 97: 'A_97', 98: 'A_98', 99: 'A_99', 100: 'A_100', 101: 'A_101', 102: 'A_102', 103: 'A_103', 104: 'A_104', 105: 'A_105', 106: 'A_106', 107: 'A_107', 108: 'A_108', 109: 'A_109', 110: 'A_110', 111: 'A_111', 112: 'A_112', 113: 'A_113', 114: 'A_114', 115: 'A_115', 116: 'A_116', 117: 'A_117', 118: 'A_118', 119: 'A_119', 120: 'A_120', 121: 'A_121', 122: 'A_122', 123: 'A_123', 124: 'A_124', 125: 'A_125', 126: 'A_126', 127: 'A_127', 128: 'A_128', 129: 'A_129', 130: 'A_130', 131: 'A_131', 132: 'A_132', 133: 'A_133', 134: 'A_134', 135: 'A_135', 136: 'A_136', 137: 'A_137', 138: 'A_138', 139: 'A_139', 140: 'A_140', 141: 'A_141', 142: 'A_142', 143: 'A_143', 144: 'A_144', 145: 'A_145', 146: 'A_146', 147: 'A_147', 148: 'A_148', 149: 'A_149', 150: 'A_150', 151: 'A_151', 152: 'A_152', 153: 'A_153', 154: 'A_154', 155: 'A_155', 156: 'A_156', 157: 'A_157', 158: 'A_158', 159: 'A_159', 160: 'A_160', 161: 'A_161', 162: 'A_162', 163: 'A_163', 164: 'A_164', 165: 'A_165', 166: 'A_166', 167: 'A_167', 168: 'A_168', 169: 'A_169', 170: 'A_170', 171: 'A_171', 172: 'A_172', 173: 'A_173', 174: 'A_174', 175: 'A_175', 176: 'A_176', 177: 'A_177', 178: 'A_178', 179: 'A_179', 180: 'A_180', 181: 'A_181', 182: 'A_182', 183: 'A_183', 184: 'A_184', 185: 'A_185', 186: 'A_186', 187: 'A_187', 188: 'A_188', 189: 'A_189', 190: 'A_190', 191: 'A_191', 192: 'A_192', 193: 'A_193', 194: 'A_194', 195: 'A_195', 196: 'A_196', 197: 'A_197'}, 'RATE': {0: 0.001013797, 1: 0.001013797, 2: 0.001013797, 3:
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该脚本通常运行良好，运行时间不到 10 秒左右 - 但是我希望能够为
xslim表达式
no_clm = poisson.rvs(size=1)diff --git a/sim.py b/sim.py
index c4dd0c5..5b4a006 100644
--- a/sim.py
+++ b/sim.py
@@ -50,10 +50,12 @@ n_simulations = 10000
xs = 5e6
lim = 10e6
-result = np.empty((n_simulations))
+result = np.zeros((n_simulations))
for i in range(n_simulations):
no_clm = poisson.rvs(size=1)
+ if no_clm[0] == 0:
+ continue
#simulate beta distribution