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Submodule rai
updated
48 files
Submodule rai-tutorials
added at
2529d5
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,131 @@ | ||
| import numpy as np | ||
|
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||
| class OT(): | ||
| none = 0 | ||
| f = 1 | ||
| sos = 2 | ||
| ineq = 3 | ||
| eq = 4 | ||
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| class NLP(): | ||
| """ | ||
| Non-Linear Mathematical Program | ||
| min_x 1^T phi_f(x) + phi_sos(x)^T phi_sos(x) | ||
| s.t. phi_eq(x) = 0 | ||
| phi_ineq(x) <= 0 | ||
| B[0] <= x <= B[1] | ||
| where: | ||
| x is a continous variable, in vector space R^n | ||
| phi_f is a vector of cost terms | ||
| phi_sos is a vector of sum-of-square cost terms | ||
| phi_eq is a vector of equality constraints | ||
| phi_ineq is a vector of inequality constraints | ||
| B[0] and B[1] in R^n are, respectively, the lower and upper box bounds | ||
| See Also: | ||
| ----- | ||
| NLPTraced | ||
| """ | ||
|
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| def __init__(self, *args, **kwargs): | ||
| pass | ||
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| def evaluate(self, x): | ||
| """ | ||
| query the NLP at a point x; returns the tuple (phi,J), which is | ||
| the feature vector and its Jacobian; features define cost terms, | ||
| sum-of-square (sos) terms, inequalities, and equalities depending | ||
| on 'getFeatureTypes'. | ||
| If the returned Jacobian has 3 columns and #rows non-equal to | ||
| dim(phi) it should be interpreted by solvers as sparse in | ||
| triple format; the C++ solvers exploit such sparse Jacobians, | ||
| e.g., to efficiently compute Gauss-Newton steps. | ||
| Parameters | ||
| ------ | ||
| x: np.array, 1-D | ||
| Returns | ||
| ------ | ||
| phi: np.array 1-D | ||
| J: np.array 2-D. J[i,j] is derivative of feature i w.r.t variable j | ||
| """ | ||
| raise NotImplementedError() | ||
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| def getDimension(self): | ||
| """ | ||
| return the dimensionality of x | ||
| Returns | ||
| ----- | ||
| output: integer | ||
| """ | ||
| raise NotImplementedError() | ||
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| def getBounds(self): | ||
| """ | ||
| returns 2-times-n array, where n is the dimensionality of x | ||
| B[0] is the lower and B[1] the upper bounds, so that B[0] <= x <= B[1] | ||
| Returns | ||
| ------ | ||
| B: np.array 2D (2-times-n) | ||
| """ | ||
| n = self.getDimension() | ||
| return np.tile([[-np.Inf], [+np.Inf]], (1,n)) | ||
|
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| def getFeatureTypes(self): | ||
| """ | ||
| returns | ||
| ----- | ||
| output: list of feature Types | ||
| """ | ||
| return [OT.f] | ||
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| def getFHessian(self, x): | ||
| """ | ||
| Returns Hessian of the $f$ objective | ||
| Default: all zeros | ||
| Returns | ||
| ----- | ||
| hessian: np.array 2D | ||
| """ | ||
| n = self.getDimension() | ||
| return np.zeros((n, n)) | ||
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| def getInitializationSample(self): | ||
| """ | ||
| returns a sample (e.g. uniform within bounds) to initialize an optimization -- not necessarily feasible | ||
| Returns | ||
| ----- | ||
| x: np.array 1-D | ||
| """ | ||
| return np.ones(self.getDimension()) | ||
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| def report(self, verbose): | ||
| """ | ||
| displays semantic information on the last query | ||
| Parameters | ||
| ---- | ||
| Returns | ||
| ---- | ||
| output: string | ||
| """ | ||
| raise NotImplementedError() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1 +1 @@ | ||
| __version__ = '0.1.9b' | ||
| __version__ = '0.1.11' |
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