Hybrid Diag -> Full Implemented; Made spherical_chol more efficient
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@ -79,21 +79,16 @@ def get_legal_setups(allowedEPTs=None, allowedParStrength=None, allowedCovStreng
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for cs in allowedCovStrength:
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if ps.value > cs.value:
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continue
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if ps == Strength.SCALAR and cs == Strength.FULL:
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# TODO: Maybe allow?
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continue
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if ps == Strength.DIAG and cs == Strength.FULL:
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# TODO: Implement
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continue
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if ps == Strength.NONE:
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yield (ps, cs, EnforcePositiveType.NONE, ProbSquashingType.NONE)
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else:
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for ept in allowedEPTs:
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if cs == Strength.FULL:
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for pt in allowedPTs:
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for ept in allowedEPTs:
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if cs == Strength.FULL:
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for pt in allowedPTs:
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if pt != ParametrizationType.NONE:
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yield (ps, cs, ept, pt)
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else:
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yield (ps, cs, ept, ProbSquashingType.NONE)
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else:
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yield (ps, cs, ept, ParametrizationType.NONE)
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def make_proba_distribution(
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@ -271,7 +266,8 @@ class CholNet(nn.Module):
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if self.cov_strength == Strength.NONE:
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self.chol = th.ones(self.action_dim) * std_init
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elif self.cov_strength == Strength.SCALAR:
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self.param = nn.Parameter(std_init, requires_grad=True)
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self.param = nn.Parameter(
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th.Tensor([std_init]), requires_grad=True)
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elif self.cov_strength == Strength.DIAG:
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self.params = nn.Parameter(
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th.ones(self.action_dim) * std_init, requires_grad=True)
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@ -295,11 +291,16 @@ class CholNet(nn.Module):
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self.param = nn.Parameter(
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th.ones(self.action_dim), requires_grad=True)
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elif self.par_strength == Strength.DIAG and self.cov_strength == Strength.FULL:
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# TODO
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pass
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self.stds = nn.Linear(latent_dim, self.action_dim)
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self.padder = th.nn.ZeroPad2d((0, 1, 1, 0))
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# TODO: Init Non-zero?
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self.params = nn.Parameter(
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th.ones(self._full_params_len - self.action_dim) * 0, requires_grad=True)
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elif self.par_strength == Strength.SCALAR and self.cov_strength == Strength.FULL:
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# TODO
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pass
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self.factor = nn.Linear(latent_dim, 1)
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# TODO: Init Off-axis differently?
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self.params = nn.Parameter(
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th.ones(self._full_params_len) * std_init, requires_grad=True)
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else:
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raise Exception("This Exception can't happen")
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@ -310,7 +311,7 @@ class CholNet(nn.Module):
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return self.chol
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elif self.cov_strength == Strength.SCALAR:
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return self._ensure_positive_func(
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th.ones(self.action_dim) * self.param)
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th.ones(self.action_dim) * self.param[0])
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elif self.cov_strength == Strength.DIAG:
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return self._ensure_positive_func(self.params)
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elif self.cov_strength == Strength.FULL:
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@ -328,16 +329,23 @@ class CholNet(nn.Module):
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return self._parameterize_full(params)
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else:
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if self.par_strength == Strength.SCALAR and self.cov_strength == Strength.DIAG:
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factor = self.factor(x)
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factor = self.factor(x)[0]
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diag_chol = self._ensure_positive_func(
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self.param * factor[0])
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self.param * factor)
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return diag_chol
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elif self.par_strength == Strength.DIAG and self.cov_strength == Strength.FULL:
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pass
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# TODO
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# TODO: Maybe possible to improve speed and stability by making conversion from pearson correlation + stds to cov in cholesky-form.
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stds = self.stds(x)
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smol = self._parameterize_full(self.params)
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big = self.padder(smol)
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pearson_cor_chol = big + th.eye(stds.shape[0])
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pearson_cor = pearson_cor_chol.T @ pearson_cor_chol
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cov = stds * pearson_cor * stds
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chol = th.linalg.cholesky(cov)
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return chol
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elif self.par_strength == Strength.SCALAR and self.cov_strength == Strength.FULL:
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# TODO
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pass
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factor = self.factor(x)
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return self._parameterize_full(self.params * factor[0])
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raise Exception()
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@property
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@ -356,14 +364,11 @@ class CholNet(nn.Module):
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raise Exception()
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def _chol_from_flat(self, flat_chol):
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# chol = fill_triangular(flat_chol).expand(self._flat_chol_len, -1, -1)
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chol = fill_triangular(flat_chol)
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return self._ensure_diagonal_positive(chol)
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def _chol_from_flat_sphe_chol(self, flat_sphe_chol):
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pos_flat_sphe_chol = self._ensure_positive_func(flat_sphe_chol)
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# sphe_chol = fill_triangular(pos_flat_sphe_chol).expand(
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# self._flat_chol_len, -1, -1)
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sphe_chol = fill_triangular(pos_flat_sphe_chol)
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chol = self._chol_from_sphe_chol(sphe_chol)
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return chol
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@ -377,17 +382,26 @@ class CholNet(nn.Module):
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# S[i,j] e (0, pi) where i = 2..n, j = 2..i
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# We already ensure S > 0 in _chol_from_flat_sphe_chol
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# We ensure < pi by applying tanh*pi to all applicable elements
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#import pdb
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# pdb.set_trace()
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S = sphe_chol
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n = self.action_dim
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L = th.zeros_like(sphe_chol)
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for i in range(n):
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for j in range(i):
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t = S[i, 1]
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for k in range(1, j+1):
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t *= th.sin(th.tanh(S[i, k])*pi)
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t = 1
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s = ''
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for j in range(i+1):
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maybe_cos = 1
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s_maybe_cos = ''
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if i != j:
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t *= th.cos(th.tanh(S[i, j+1])*pi)
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L[i, j] = t
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maybe_cos = th.cos(th.tanh(S[i, j+1])*pi)
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s_maybe_cos = 'cos([l_'+str(i+1)+']_'+str(j+2)+')'
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L[i, j] = S[i, 0] * t * maybe_cos
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print('[L_'+str(i+1)+']_'+str(j+1) +
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'=[l_'+str(i+1)+']_1'+s+s_maybe_cos)
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if j <= i and j < n-1 and i < n:
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t *= th.sin(th.tanh(S[i, j+1])*pi)
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s += 'sin([l_'+str(i+1)+']_'+str(j+2)+')'
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return L
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def _ensure_positive_func(self, x):
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