phuzzy.shapes.truncnorm module¶
Normal distibuted membership function
TruncNorm(alpha0=[1, 3], alpha1=None, number_of_alpha_levels=15)
TruncGenNorm(alpha0=[1, 4], alpha1=None, number_of_alpha_levels=15, beta=5)
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class
phuzzy.shapes.truncnorm.
TruncGenNorm
(**kwargs)[source]¶ Bases:
phuzzy.shapes.FuzzyNumber
Truncated generalized normal distibuted membership function
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__init__
(**kwargs)[source]¶ create a TruncNorm object
Parameters: kwargs – TruncGenNorm(alpha0=[1, 3], alpha1=None, number_of_alpha_levels=17, beta=3)
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__module__
= 'phuzzy.shapes.truncnorm'¶
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discretize
(alpha0, alpha1, alpha_levels)[source]¶ discretize shape function
Parameters: - alpha0 – range at alpha=0
- alpha1 – range at alpha=1
- alpha_levels – number of alpha levels
Returns: None
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distr
¶
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loc
¶
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mean
¶
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scale
¶
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std
¶
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class
phuzzy.shapes.truncnorm.
TruncNorm
(**kwargs)[source]¶ Bases:
phuzzy.shapes.FuzzyNumber
Normal distibuted membership function
-
__init__
(**kwargs)[source]¶ create a TruncNorm object
Parameters: kwargs – TruncNorm(alpha0=[1, 3], alpha1=None, number_of_alpha_levels=17)
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__module__
= 'phuzzy.shapes.truncnorm'¶
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discretize
(alpha0, alpha1, alpha_levels)[source]¶ discretize shape function
Parameters: - alpha0 – range at alpha=0
- alpha1 – range at alpha=1
- alpha_levels – number of alpha levels
Returns: None
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distr
¶ calculate truncated normal distribution
Returns: distribution object
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loc
¶ mean value
Return type: float Returns: mean value aka location
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mean
¶ mean value
Return type: float Returns: mean value aka location
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scale
¶ standard deviation
Return type: float Returns: standard deviation
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std
¶ standard deviation
Return type: float Returns: standard deviation
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