SymILO/utils.py at main · NetSysOpt/SymILO · GitHub

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import numpy as np
import torch
import re
import math

def PF(d,D,j):

    if j//2 == 0:
        pe = np.sin(j/20**(2*d/D))
    else:
        pe = np.cos(j/20**(2*d/D))

    return pe


def reorderSMSP(names):

    nItem = max([int(re.findall('\d+', name)[0]) for name in names if 'X' in name])+1
    nCap = max([int(re.findall('\d+', name)[0]) for name in names if 'Y' in name])+1

    XOrder = torch.Tensor(nItem+nCap,nItem)

    for ind,name in enumerate(names):
        ss = re.findall('\d+',name)
        a,b = int(ss[0]),int(ss[1])
        if 'X' in name:
            XOrder[a,b] = ind
        elif 'Y' in name:
            XOrder[a+nItem,b] = ind


    return {
        'reorderInds':XOrder,
        'nGroup':nItem,
        'nElement':nItem+nCap
    }


def addPosFeatureSMSP(features):

    vf = features.varFeatures
    vn = features.varNames
    D1 = 16
    D2 = 16
    groupPos = np.zeros((vf.shape[0],D1))
    elementPos = np.zeros((vf.shape[0],D2))
    for ind,var in enumerate(vn):
        ss = re.findall('\d+',var)
        a,b = int(ss[0]),int(ss[1])
        for d in range(D1):
            groupPos[ind, d] = PF(d, D1, b)
        if 'X' in var:
            for d in range(D2):
                elementPos[ind,d] = PF(d, D2, a)
        elif 'Y' in var:
            for d in range(D2):
                elementPos[ind,d] = PF(d, D2, a+111)

    features.groupFeatures = np.concatenate([ groupPos, elementPos], axis=-1)


    return features

def reorderIP(names):
    nItem = max([int(re.findall('\d+', name)[0]) for name in names if 'place' in name]) + 1
    nBin = max([int(re.findall('\d+', name)[1]) for name in names if 'place' in name]) + 1

    XOrder = torch.Tensor(nItem , nBin)

    for ind, name in enumerate(names):
        ss = re.findall('\d+', name)
        a, b = int(ss[0]), int(ss[1])
        if 'place' in name:
            XOrder[a, b] = ind

    return {
        'reorderInds': XOrder,
        'nGroup': nBin,
        'nElement': nItem
    }


def addPosFeatureIP(features):
    vf = features.varFeatures
    vn = features.varNames

    nItem = max([int(re.findall('\d+', name)[0]) for name in vn if 'place' in name]) + 1
    nBin = max([int(re.findall('\d+', name)[1]) for name in vn if 'place' in name]) + 1

    D1 = 16
    D2 = 16
    groupPos = np.zeros((vf.shape[0], D1))
    elementPos = np.zeros((vf.shape[0], D2))
    for ind, var in enumerate(vn):
        if 'place' not in var:
            continue
        ss = re.findall('\d+', var)
        a, b = int(ss[0]), int(ss[1])
        for d in range(D1):
            groupPos[ind,d] = PF(d,D1,b)
        for d in range(D2):
            elementPos[ind,d] = PF(d,D2,a)
    features.groupFeatures = np.concatenate([ groupPos, elementPos], axis=-1)
    # features.varFeatures = np.concatenate([groupPos, elementPos], axis=-1)

    return features