LogisticModel¶
- class LogisticModel(t0=1790.0, y0=3900000.0, a=0.03134, b=1.5887e-10, populationFactor=1000000.0)¶
Data class for the logistic model.
In the physical model, the inputs and parameters are ordered as presented in the next table. Notice that there are no parameters in the physical model.
Index
Input variable
0
t1
1
t2
…
…
21
t22
22
a
23
c
Examples
>>> from openturns.usecases import logistic_model >>> # Load the logistic model >>> lm = logistic_model.LogisticModel() >>> print(lm.data[:5]) [ Time U.S. Population ] 0 : [ 1790 3.9 ] 1 : [ 1800 5.3 ] 2 : [ 1810 7.2 ] 3 : [ 1820 9.6 ] 4 : [ 1830 13 ] >>> print("Inputs:", lm.model.getInputDescription()) Inputs: [t0,t1,t2,t3,t4,t5,t6,t7,t8,t9,t10,t11,t12,t13,t14,t15,t16,t17,t18,t19,t20,t21,a,c]#24 >>> print("Outputs:", lm.model.getOutputDescription()) Outputs: [z0,z1,z2,z3,z4,z5,z6,z7,z8,z9,z10,z11,z12,z13,z14,z15,z16,z17,z18,z19,z20,z21]#22
- Attributes:
- t0float, optional
Initial time. The default is 1790.
- y0float, optional
Initial population (at t0). The default is 3.9e6.
- afloat, optional
8Parameter of the model. The default is 0.03134.
- bfloat, optional
Parameter of the model. The default is 1.5887e-10.
- populationFactorfloat, optional
The multiplication factor to scale the population. The default is 1.0e6.
- distY0
Normal
distribution ot.Normal(y0, 0.1 * y0)
- distA
Normal
distribution ot.Normal(a, 0.3 * a)
- distB
Normal
distribution ot.Normal(b, 0.3 * b)
- distX
JointDistribution
The joint distribution of the input parameters.
- model
PythonFunction
The logistic model of growth. The input has input dimension 24 and output dimension 22. More precisely, we have and .
- data
Sample
of size 22 and dimension 2 A data set containing 22 dates from 1790 to 2000. First marginal represents dates and second marginal the population in millions.
- __init__(t0=1790.0, y0=3900000.0, a=0.03134, b=1.5887e-10, populationFactor=1000000.0)¶
Examples using the class¶
Calibration of the logistic model