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Constrained tuning: Difference between revisions

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{{Databox| Code |
{{Databox| Code |
<syntaxhighlight lang="python">
<syntaxhighlight lang="python">
# © 2020-2022 Flora Canou | Version 0.20.0
# © 2020-2022 Flora Canou | Version 0.21.0
# This work is licensed under the GNU General Public License version 3.
# This work is licensed under the GNU General Public License version 3.


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     return main, subgroup
     return main, subgroup


def get_weight (subgroup, wtype = "tenney", *, k = 0.5):
def get_weight (subgroup, wtype = "tenney"):
     if wtype == "tenney":
     if wtype == "tenney":
         return np.diag (1/np.log2 (subgroup))
         return np.diag (1/np.log2 (subgroup))
     elif wtype == "frobenius":
     elif wtype == "frobenius":
         return np.eye (len (subgroup))
         return np.eye (len (subgroup))
    elif wtype == "inverse tenney":
        return np.diag (np.log2 (subgroup))
     elif wtype == "benedetti":
     elif wtype == "benedetti":
         return np.diag (1/np.array (subgroup))
         return np.diag (1/np.array (subgroup))
    elif wtype == "weil":
        return linalg.pinv (np.append ((1 - k)*np.diag (np.log2 (subgroup)), [k*np.log2 (subgroup)], axis = 0))
     else:
     else:
         warnings.warn ("weighter type not supported, using default (\"tenney\")")
         warnings.warn ("weighter type not supported, using default (\"tenney\")")
         return get_weight (subgroup, wtype = "tenney")
         return get_weight (subgroup, wtype = "tenney")


def weighted (main, subgroup, wtype = "tenney", *, k = 0.5):
def get_skew (subgroup, skew = 0, order = 2):
    return main @ get_weight (subgroup, wtype = wtype, k = k)
    if skew == 0:
        return np.eye (len (subgroup))
    elif order == 2:
        r = skew/(len (subgroup)*skew**2 + 1)
        return np.append (np.eye (len (subgroup)) - skew*r*np.ones ((len (subgroup), len (subgroup))),
            r*np.ones ((len (subgroup), 1)), axis = 1)
    else:
        raise NotImplementedError ("Weil skew only works with Euclidean norm as of now.")


def error (gen, map, jip, order = 2):
def weightskewed (main, subgroup, wtype = "tenney", skew = 0, order = 2):
    return main @ get_weight (subgroup, wtype) @ get_skew (subgroup, skew, order)
 
weighted = weightskewed
 
def error (gen, map, jip, order):
     return linalg.norm (gen @ map - jip, ord = order)
     return linalg.norm (gen @ map - jip, ord = order)


def optimizer_main (map, subgroup = None, wtype = "tenney", order = 2,
def optimizer_main (map, subgroup = None, wtype = "tenney", skew = 0, order = 2,
         cons_monzo_list = None, des_monzo = None, show = True, *, k = 0.5):
         cons_monzo_list = None, des_monzo = None, show = True):
     map, subgroup = get_subgroup (np.array (map), subgroup)
     map, subgroup = get_subgroup (np.array (map), subgroup)
    if wtype == "weil" and order != 2:
        warnings.warn ("The weil weighter as of now is only meant to be used for L2.")


     jip = np.log2 (subgroup)*SCALAR
     jip = np.log2 (subgroup)*SCALAR
     map_w = weighted (map, subgroup, wtype = wtype, k = k)
     map_wx = weighted (map, subgroup, wtype, skew, order)
     jip_w = weighted (jip, subgroup, wtype = wtype, k = k)
     jip_wx = weighted (jip, subgroup, wtype, skew, order)
     if order == 2 and cons_monzo_list is None: #te with no constraints, simply use lstsq for better performance
     if order == 2 and cons_monzo_list is None: #te with no constraints, simply use lstsq for better performance
         res = linalg.lstsq (map_w.T, jip_w)
         res = linalg.lstsq (map_w.T, jip_wx)
         gen = res[0]
         gen = res[0]
         print ("L2 tuning without constraints, solved using lstsq. ")
         print ("L2 tuning without constraints, solved using lstsq. ")
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         gen0 = [SCALAR]*map.shape[0] #initial guess
         gen0 = [SCALAR]*map.shape[0] #initial guess
         cons = () if cons_monzo_list is None else {'type': 'eq', 'fun': lambda gen: (gen @ map - jip) @ cons_monzo_list}
         cons = () if cons_monzo_list is None else {'type': 'eq', 'fun': lambda gen: (gen @ map - jip) @ cons_monzo_list}
         res = optimize.minimize (error, gen0, args = (map_w, jip_w, order), method = "SLSQP", constraints = cons)
         res = optimize.minimize (error, gen0, args = (map_wx, jip_wx, order), method = "SLSQP",
            options = {'ftol': 1e-9}, constraints = cons)
         print (res.message)
         print (res.message)
         if res.success:
         if res.success:
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Generators: [1200.    1896.9521] (¢)
Generators: [1200.    1896.9521] (¢)
Tuning map: [1200.    1896.9521 2787.8085 3369.5214] (¢)
Tuning map: [1200.    1896.9521 2787.8085 3369.5214] (¢)
Mistuning map: [ 0.    -5.0029  1.4948  0.6955] (¢)
</pre>
</pre>


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