A few updates
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@ -12,7 +12,7 @@ from numpy.random import shuffle
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from engines import nipals_lpls as lpls
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def lpls_val(X, Y, Z, a_max=2, nsets=None,alpha=.5, mean_ctr=[2,0,2],verbose=True):
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def lpls_val(X, Y, Z, a_max=2, nsets=None,alpha=.5, mean_ctr=[2,0,2], zorth=False, verbose=True):
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"""Performs crossvalidation for generalisation error in lpls.
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The L-PLS crossvalidation is estimated just like an ordinary pls
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@ -42,6 +42,8 @@ def lpls_val(X, Y, Z, a_max=2, nsets=None,alpha=.5, mean_ctr=[2,0,2],verbose=Tru
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0 : row center
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1 : column center
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2 : double center
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zorth : {boolean}
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If true, Require orthogonal latent components in Z.
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verbose : {boolean}, optional
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Verbosity of console output. For use in debugging.
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@ -70,7 +72,11 @@ def lpls_val(X, Y, Z, a_max=2, nsets=None,alpha=.5, mean_ctr=[2,0,2],verbose=Tru
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Yhat = empty((a_max, k, l), 'd')
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for cal, val in cv(nsets, k):
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dat = lpls(X[cal],Y[cal],Z,a_max=a_max,alpha=alpha,mean_ctr=mean_ctr,verbose=verbose)
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# do the training model
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dat = lpls(X[cal], Y[cal], Z, a_max=a_max, alpha=alpha,
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mean_ctr=mean_ctr, zorth=zorth, verbose=verbose)
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# center test data
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if mean_ctr[0] != 1:
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xi = X[val,:] - dat['mnx']
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else:
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@ -79,14 +85,24 @@ def lpls_val(X, Y, Z, a_max=2, nsets=None,alpha=.5, mean_ctr=[2,0,2],verbose=Tru
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ym = dat['mny']
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else:
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ym = Y[val].mean(1)[:,newaxis] #???: check this
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# predictions
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for a in range(a_max):
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Yhat[a,val,:] = atleast_2d(ym + dot(xi, dat['B'][a]))
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#if permute:
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# xcal = X[cal]
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# for a in range(1,a_max,1):
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# for n in range(10):
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# shuffle(cal)
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# dat = lpls(xcal, Y[cal], Z, a_max=a_max, alpha=alpha,
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# mean_ctr=mean_ctr, verbose=verbose)
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# todo: need a better support for classification error
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y_is_class = Y.dtype.char.lower() in ['i','p', 'b', 'h','?']
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if y_is_class:
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Yhat, err = class_error(Yhat,Y)
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return Yhat, err
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pass
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#Yhat, err = class_error(Yhat, Y)
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#return Yhat, err
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sep = (Y - Yhat)**2
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rmsep = sqrt(sep.mean(1)).T
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@ -317,8 +333,8 @@ def cv(N, K, randomise=True, sequential=False):
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otherwise interleaved ordering is used.
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"""
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if K>N:
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raise ValueError, "You cannot divide a list of %d samples into more than %d segments. Yout tried: %s" %(N,N,K)
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if N>K:
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raise ValueError, "You cannot divide a list of %d samples into more than %d segments. Yout tried: %s" %(K, K, N)
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index = xrange(N)
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if randomise:
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from random import shuffle
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@ -411,7 +411,7 @@ def pls(X, Y, aopt=2, scale='scores', mode='normal', center_axis=-1):
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'evx': expvarx, 'evy': expvary, 'ssqx': ssqx, 'ssqy': ssqy,
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'leverage': leverage, 'mnx': mnx, 'mny': mny}
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def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], scale='scores', verbose=False):
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def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 2], scale='scores', zorth = False, verbose=False):
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""" L-shaped Partial Least Sqaures Regression by the nipals algorithm.
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An L-shaped low rank model aproximates three matrices in a hyploid
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@ -475,10 +475,14 @@ def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], scale='scores', ve
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scale : {'scores', 'loads'}, optional
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Option to decide on where the scale goes.
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zorth : {False, boolean}, optional
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Option to force orthogonality between latent components
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in Z
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verbose : {boolean}, optional
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Verbosity of console output. For use in debugging.
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*References*
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Saeboe et al., LPLS-regression: a method for improved prediction and
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classification through inclusion of background information on
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predictor variables, J. of chemometrics and intell. laboratory syst.
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@ -522,18 +526,22 @@ def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], scale='scores', ve
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var_y = empty((a_max,))
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var_z = empty((a_max,))
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MAX_ITER = 450
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MAX_ITER = 4500
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LIM = finfo(X.dtype).resolution
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is_rd = False
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for a in range(a_max):
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if verbose:
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print "\nWorking on comp. %s" %a
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u = F[:,:1]
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w = E[:1,:].T
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l = G[:,:1]
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diff = 1
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niter = 0
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while (diff>LIM and niter<MAX_ITER):
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niter += 1
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u1 = u.copy()
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w1 = w.copy()
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l1 = l.copy()
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w = dot(E.T, u)
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wn = msqrt(dot(w.T, w))
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if wn < LIM:
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@ -553,19 +561,24 @@ def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], scale='scores', ve
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c = c/msqrt(dot(c.T, c))
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u = dot(F, c)
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diff = dot((u - u1).T, (u - u1))
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if verbose:
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print "Converged after %s iterations" %niter
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if niter==MAX_ITER:
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print "Maximum nunber of iterations reached!"
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print "Iterations: %d " %niter
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print "Error: %.2E" %diff
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if is_rd:
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print "Hei og haa ... rank deficient, this should really not happen"
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break
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tt = dot(t.T, t)
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p = dot(X.T, t)/tt
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q = dot(Y.T, t)/tt
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l = dot(Z, w)
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#k = dot(Z.T, l)/dot(l.T, l)
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p = dot(E.T, t)/tt
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q = dot(F.T, t)/tt
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if zorth:
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k = dot(G.T, l)/dot(l.T, l)
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else:
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k = w
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l = dot(G, w)
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U[:,a] = u.ravel()
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W[:,a] = w.ravel()
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@ -575,10 +588,10 @@ def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 1], scale='scores', ve
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L[:,a] = l.ravel()
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K[:,a] = k.ravel()
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# rank-one deflations
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E = E - dot(t, p.T)
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F = F - dot(t, q.T)
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G = (G.T - dot(k, l.T)).T
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G = G - dot(l, k.T)
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var_x[a] = pow(E, 2).sum()
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var_y[a] = pow(F, 2).sum()
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@ -1,6 +1,6 @@
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"""Bilinear models"""
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from numpy import expand_dims
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from numpy import expand_dims,ones
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from engines import pca
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@ -14,8 +14,11 @@ def scale(x, axis=0):
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#scale = 1./x.std(axis)
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return expand_dims(scale, axis)
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class Model(object):
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def __init__(name="johndoe"):
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"""All underscored attributes are properties.
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"""
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def __init__(self, name="johndoe"):
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self.name = name
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self.options = {}
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@ -27,8 +30,8 @@ class Model(object):
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def clear(self):
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for param in self.__dict__.keys():
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if param.startswith("_") and param[1]!="_":
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exec "del self." + param
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if param.startswith("_") and param[1:5]!="core":
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exec "del self." + param[1:]
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def clear_core(self):
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for param in self.__dict__.keys():
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@ -78,29 +81,29 @@ class PCA(Model):
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def scores():
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doc = "pca scores"
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def fget(self):
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if not hasattr(self, "_scores"):
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u, s, v, tot_var = pcaengine(self.xw, self.amax)
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self._scores = u
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self.singvals = s
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self.loadings = v
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self.tot_var = tot_var
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return self._scores[:,:self.amax]
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if not hasattr(self, "_core_scores"):
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result= pca(self.xw, self.amax)
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self._core_scores = result['T']
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self.singvals = result['eigvals']
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self.loadings = result['P']
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self.tot_var = 120.
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return self._core_scores[:,:self.amax]
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def fset(self, t):
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self._scores = t
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self._core_scores = t
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def fdel(self):
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del self._scores
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return locals() # credit: David Niergarth
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del self._core_scores
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return locals()
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scores = property(**scores())
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def loadings():
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doc = "pca loadings"
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def fget(self):
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if not hasattr(self, "_loadings"):
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u, s, v, tot_var = pcaengine(self.xw, self.amax)
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self._loadings = v
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self.scores = u
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self.singvals = s
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self.tot_var = tot_var
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result = pca(self.xw, self.amax)
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self.loadings = result['P']
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self.scores = result['T']
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self.singvals = result['eigvals']
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self.tot_var = 120
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return self._loadings[:,:self.amax]
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def fdel(self):
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del self._loadings
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@ -113,11 +116,11 @@ class PCA(Model):
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doc = "Singular values"
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def fget(self):
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if not hasattr(self, "_singvals"):
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u, s, v, tot_var = pcaengine(self.xw, self.amax)
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self._singvals = s
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self.scores = u
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self.loadings = v
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self.tot_var = tot_var
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result = pca(self.xw, self.amax)
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self._singvals = result['eigvals']
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self.scores = result['T']
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self.loadings = result['P']
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self.tot_var = 120
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return self._singvals[:self.amax]
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def fset(self, w):
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self._singvals = w
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@ -139,7 +142,7 @@ class PCA(Model):
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doc = "column means"
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def fget(self):
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if not hasattr(self, "_xadd"):
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self._xadd = center(self.x, axis=0)
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self._xadd = mean_center(self.x, axis=0)
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return self._xadd
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def fset(self, mnx):
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if hasattr(self, "_xc"):
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@ -153,7 +156,7 @@ class PCA(Model):
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xadd = property(**xadd())
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def xc():
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doc = "centered input data"
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doc = "mean_centered input data"
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def fget(self):
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if not hasattr(self, "_xc"):
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self._xc = self.x + self.xadd
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@ -161,6 +164,9 @@ class PCA(Model):
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def fset(self, xc):
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self._xc = xc
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def fdel(self):
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print "a"
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if hasattr(self, "_xc"):
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print "del"
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del self._xc
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return locals()
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xc = property(**xc())
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@ -237,7 +243,7 @@ class PCA(Model):
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def fdel(self):
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del self._row_metric
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if hasattr(self, "_xd"):
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del self.xd
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del self._xd
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return locals()
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row_metric = property(**row_metric())
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@ -254,7 +260,7 @@ class PCA(Model):
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def fdel(self):
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del self._column_metric
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if hasattr(self, "_xd"):
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del self.xd
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del self._xd
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return locals()
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column_metric = property(**column_metric())
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@ -273,10 +279,12 @@ class PCA(Model):
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def delete_rows(self, index):
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pass
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def reweight(self, )
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def reweight(self, w):
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pass
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if __name__ == "__main__":
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X = random.rand(4,10)
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from numpy.random import rand
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X = rand(4,10)
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pcaobj = PCA(X)
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print "explained variance" + str(pcaobj.explained_variance)
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@ -115,8 +115,8 @@ def procrustes(a, b, strict=True, center=False, verbose=False):
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*Reference*:
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Schonemann, A generalized solution of the orthogonal Procrustes problem,
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Psychometrika, 1966
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Schonemann, A generalized solution of the orthogonal Procrustes
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problem, Psychometrika, 1966
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"""
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if center:
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@ -131,9 +131,9 @@ def procrustes(a, b, strict=True, center=False, verbose=False):
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Cm = _ensure_strict(Cm)
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b_rot = dot(b, Cm)
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if verbose:
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print Cm.round()
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fit = sum(ravel(b - b_rot)**2)
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print "Error: %.3E" %fit
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fit = ((b - b_rot)**2).sum()
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fit2 = (dot(a, a.T) + dot(b, b.T) - 2*diag(s)).trace()
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print "Error: %.2E , %.2E" %(fit, fit2)
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if center:
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return mn_b + b_rot
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else:
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@ -159,7 +159,9 @@ def _ensure_strict(C, only_flips=True):
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*Notes*:
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This function is not ready for use. Use (only_flips=True)
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This function is not ready for use. Use (only_flips=True).
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That is, for more than two components, the rotation matrix
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has a tendency to be unstable (det(Cm)>1), when rounding is used.
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"""
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if only_flips:
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@ -279,6 +281,16 @@ def _fdr(tsq, tsqp, loc_method=median):
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fdr : {array}
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False discovery rate
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*Notes*:
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This is an internal function for use in fdr estimation of jack-knifed
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perturbated blm parameters.
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*Reference*:
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Gidskehaug et al., A framework for significance analysis of
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gene expression data using dimension reduction methods, BMC
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bioinformatics, 2007
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"""
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n, = tsq.shape
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k, m = tsqp.shape
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