Updates on metric, whitespace
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@ -96,10 +96,11 @@ def procrustes(A, B, strict=True, center=False, verbose=False):
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else:
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return b_rot
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def expl_var_x(X, T):
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"""Returns explained variance of X."""
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# centered X,Y
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exp_var_x = diag(dot(T.T, T))*100/(sum(X**2))
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def expl_var_x(Xc, T):
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"""Returns explained variance of X.
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T should carry variance in length, Xc has zero col-mean.
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"""
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exp_var_x = diag(dot(T.T, T))*100/(sum(Xc**2))
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return exp_var_x
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def expl_var_y(Y, T, Q):
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@ -139,26 +140,28 @@ def pls_qvals(a, b, aopt=None, alpha=.3,
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n_false = zeros((n, n_iter), dtype='<f8')
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#full model
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if metric!=None:
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a = dot(a, metric)
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if algo=='bridge':
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dat = bridge(a, b, aopt, 'loads', 'fast')
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else:
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dat = pls(a, b, aopt, 'loads', 'fast')
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Wcv = pls_jkW(a, b, aopt, n_blocks=None, algo=algo)
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Wcv = pls_jkW(a, b, aopt, n_blocks=None, algo=algo, metric=metric)
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tsq_full = hotelling(Wcv, dat['W'], p_center=p_center,
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alpha=alpha, crot=crot, strict=strict,
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cov_center=cov_center, metric=metric)
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cov_center=cov_center)
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t0 = time.time()
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Vs = shuffle_1d(b, n_iter)
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for i,b_shuff in enumerate(Vs):
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for i, b_shuff in enumerate(Vs):
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t1 = time.time()
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if algo=='bridge':
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dat = bridge(a, b_shuff, aopt, 'loads','fast')
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else:
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dat = pls(a, b, aopt, 'loads', 'fast')
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Wcv = pls_jkW(a, b_shuff, aopt, n_blocks=None, algo=algo)
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Wcv = pls_jkW(a, b_shuff, aopt, n_blocks=None, algo=algo, metric=metric)
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TSQ[:,i] = hotelling(Wcv, dat['W'], p_center=p_center,
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alpha=alpha, crot=crot, strict=strict,
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cov_center=cov_center, metric=metric)
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cov_center=cov_center)
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print time.time() - t1
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sort_index = argsort(tsq_full)[::-1]
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back_sort_index = sort_index.argsort()
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@ -210,7 +213,7 @@ def leverage(aopt=1,*args):
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lev.append(lev_u)
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return lev
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def variances(a,t,p):
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def variances(a, t, p):
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"""Returns explained variance and ind. var from blm-params.
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input:
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a -- full centered matrix
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