Question

我有一个数据集，假设是beta分布。

我从
知道 docs of Scipy认为Beta发行版的 PDF 等于：

Brand | Model    | Year | Cost  | Mean   | Tax    |  Result
------------------------------------------------------------ 
Apple | iPhone 7 | 2017 | $1000 | $900   | $100   |  $1000

Apple | iphone 7 | 2018 | $800  | $900   | $80    |  $980

Xiomi | Note 5   | 2017 | $300  | $250   | $30    |  $280

Xiomi | Note 5   | 2018 | $200  | $250   | $25    |  $275

接下来，我使用beta.fit-function检查哪些参数值最适合我的数据集：

[username@hostname ~]$ nodetool tpstats
Pool Name                         Active   Pending      Completed   Blocked  All time blocked
ReadStage                              0         0      110336013         0                 0
ContinuousPagingStage                  0         0             31         0                 0
MiscStage                              0         0              0         0                 0
CompactionExecutor                     0         0        4244757         0                 0
MutationStage                          0         0       25309020         0                 0
GossipStage                            0         0        2484700         0                 0
RequestResponseStage                   0         0       46705216         0                 0
ReadRepairStage                        0         0        2193356         0                 0
CounterMutationStage                   0         0        3563130         0                 0
MemtablePostFlush                      0         0         117717         0                 0
ValidationExecutor                     1         1         111176         0                 0
MemtableFlushWriter                    0         0          23843         0                 0
ViewMutationStage                      0         0              0         0                 0
CacheCleanupExecutor                   0         0              0         0                 0
Repair#1953                            1         3              1         0                 0
MemtableReclaimMemory                  0         0          28251         0                 0
PendingRangeCalculator                 0         0              6         0                 0
AntiCompactionExecutor                 0         0              0         0                 0
SecondaryIndexManagement               0         0              0         0                 0
HintsDispatcher                        0         0             29         0                 0
Native-Transport-Requests              0         0      110953286         0                 0
MigrationStage                         0         0             19         0                 0
PerDiskMemtableFlushWriter_0           0         0          27853         0                 0
Sampler                                0         0              0         0                 0
InternalResponseStage                  0         0          21264         0                 0
AntiEntropyStage                       0         0         350913         0                 0

Message type           Dropped                  Latency waiting in queue (micros)                                    
                                             50%               95%               99%               Max
READ                         0              0.00              0.00              0.00          10090.81
RANGE_SLICE                  0              0.00              0.00          10090.81          10090.81
_TRACE                       0               N/A               N/A               N/A               N/A
HINT                         0              0.00              0.00              0.00              0.00
MUTATION                     0              0.00              0.00              0.00          10090.81
COUNTER_MUTATION             0              0.00              0.00              0.00          10090.81
BATCH_STORE                  0              0.00              0.00              0.00              0.00
BATCH_REMOVE                 0              0.00              0.00              0.00              0.00
REQUEST_RESPONSE             0              0.00              0.00              0.00          12108.97
PAGED_RANGE                  0               N/A               N/A               N/A               N/A
READ_REPAIR                  0              0.00              0.00              0.00              0.00```

这给了我以下输出：

pdf = (gamma(a + b) * x ** (a - 1) * (1 - x) ** (b - 1)) / (gamma(a) * gamma(b))

因此， a 等于〜4.66， b 等于〜36.10。

现在，我想编写自己的拟合函数：

from scipy.stats import beta as beta_stats

parameters_beta = beta_stats.fit(players['Conversion'], floc=0, fscale=1)
print(parameters_beta)

运行此命令可获得以下结果：

(4.6572518152560525, 36.09719571353918, 0, 1)

因此， a 等于0， b 等于〜426。为什么差异如此之大？我在脚本中做错了吗？

谢谢！

Scipy的beta.fit和自己的功能之间的区别

0 个答案: