Mostly clean ups
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2951ca4088
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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], zorth=False, 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=False):
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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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@ -80,11 +80,11 @@ def lpls_val(X, Y, Z, a_max=2, nsets=None,alpha=.5, mean_ctr=[2,0,2], zorth=Fals
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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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xi = X[val] - X[val].mean(1)[:,newaxis]
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xi = X[val] - X[cal].mean(1)[:,newaxis]
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if mean_ctr[2] != 1:
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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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ym = Y[cal].mean(1)[:,newaxis]
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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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@ -113,7 +113,7 @@ def lpls_val(X, Y, Z, a_max=2, nsets=None,alpha=.5, mean_ctr=[2,0,2], zorth=Fals
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def pca_jk(a, aopt, n_blocks=None):
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"""Returns jack-knife segements from PCA.
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Parameters:
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*Parameters*:
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a : {array}
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data matrix (n x m)
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@ -122,21 +122,15 @@ def pca_jk(a, aopt, n_blocks=None):
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nsets : {integer}
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number of segments
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Returns:
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*Returns*:
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Pcv : {array}
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Loadings collected in a three way matrix (n_segments, m, aopt)
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Notes:
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- The loadings are scaled with the (1/samples)*eigenvalues.
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*Notes*:
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- Crossvalidation method is currently set to random blocks of samples.
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- todo: add support for T
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- fixme: more efficient to add this in validation loop?
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"""
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if nsets == None:
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nsets = a.shape[0]
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@ -305,6 +299,7 @@ def cv(N, K, randomise=True, sequential=False):
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of length ~N/K, *without* replacement.
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*Parameters*:
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N : {integer}
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Total number of samples
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K : {integer}
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@ -12,13 +12,14 @@ minimum
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from numpy.linalg import inv,svd
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from scipy.sandbox import arpack
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def pca(X, aopt, scale='scores', mode='normal', center_axis=0):
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""" Principal Component Analysis.
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PCA is a low rank bilinear aprroximation to a data matrix that sequentially
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extracts orthogonal components of maximum variance.
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Parameters:
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*Parameters*:
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X : {array}
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Data measurement matrix, (samples x variables)
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@ -27,7 +28,7 @@ def pca(X, aopt, scale='scores', mode='normal', center_axis=0):
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center_axis : {integer}
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Center along given axis. If neg.: no centering (-inf,..., matrix modes)
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Returns:
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*Returns*:
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T : {array}
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Scores, (samples, components)
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@ -47,7 +48,7 @@ def pca(X, aopt, scale='scores', mode='normal', center_axis=0):
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leverage : {array}
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Leverages, (samples,)
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OtherParameters:
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*OtherParameters*:
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scale : {string}, optional
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Where to put the weights [['scores'], 'loadings']
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@ -55,7 +56,7 @@ def pca(X, aopt, scale='scores', mode='normal', center_axis=0):
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Amount of info retained, [['normal'], 'fast', 'detailed']
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:SeeAlso:
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*SeeAlso*:
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`center` : Data centering
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@ -78,9 +79,11 @@ def pca(X, aopt, scale='scores', mode='normal', center_axis=0):
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"""
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m, n = X.shape
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assert(aopt<=min(m,n))
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min_aopt = min(m, n)
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if center_axis >= 0:
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X = X - expand_dims(X.mean(center_axis), center_axis)
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min_aopt = min_aopt - 1
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assert(aopt <= min_aopt)
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if m > (n+100) or n > (m+100):
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u, s, v = esvd(X, aopt)
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else:
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@ -139,7 +142,7 @@ def pcr(a, b, aopt, scale='scores',mode='normal',center_axis=0):
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Performs PCR on given matrix and returns results in a dictionary.
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Parameters:
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*Parameters*:
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a : array
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Data measurement matrix, (samples x variables)
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@ -148,18 +151,18 @@ def pcr(a, b, aopt, scale='scores',mode='normal',center_axis=0):
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aopt : int
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Number of components to use, aopt<=min(samples, variables)
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Returns:
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*Returns*:
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results : dict
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keys -- values, T -- scores, P -- loadings, E -- residuals,
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levx -- leverages, ssqx -- sum of squares, expvarx -- cumulative
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explained variance, aopt -- number of components used
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OtherParameters:
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*OtherParameters*:
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mode : str
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mode : {string}
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Amount of info retained, ('fast', 'normal', 'detailed')
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center_axis : int
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center_axis : {integer}
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Center along given axis. If neg.: no centering (-inf,..., matrix modes)
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SeeAlso:
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@ -284,7 +287,7 @@ def pls(X, Y, aopt=2, scale='scores', mode='normal', center_axis=-1):
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*SeeAlso*:
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`center` : data centering
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`center` - data centering
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*Notes*
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@ -311,13 +314,15 @@ def pls(X, Y, aopt=2, scale='scores', mode='normal', center_axis=-1):
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Y = atleast_2d(Y).T
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k, l = Y.shape
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assert(m == k)
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assert(aopt<min(m, n))
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mnx, mny = 0, 0
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min_aopt = min(m, n)
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if center_axis >= 0:
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mnx = expand_dims(X.mean(center_axis), center_axis)
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X = X - mnx
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min_aopt = min_aopt - 1
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mny = expand_dims(Y.mean(center_axis), center_axis)
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Y = Y - mny
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assert(aopt > 0 and aopt < min_aopt)
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W = empty((n, aopt))
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P = empty((n, aopt))
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@ -356,7 +361,7 @@ def pls(X, Y, aopt=2, scale='scores', mode='normal', center_axis=-1):
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T[:,i] = t.ravel()
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W[:,i] = w.ravel()
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if mode=='fast' and i==aopt-1:
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if mode == 'fast' and i == (aopt - 1):
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if scale == 'loads':
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tnorm = sqrt(tt)
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T = T/tnorm
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@ -495,7 +500,7 @@ def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 2], scale='scores', zo
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m, n = X.shape
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k, l = Y.shape
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u, o = Z.shape
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max_rank = min(m, n)
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max_rank = min(m, n) + 1
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assert (a_max > 0 and a_max < max_rank), "Number of comp error:\
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tried: %d, max_rank: %d" %(a_max, max_rank)
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@ -617,6 +622,20 @@ def nipals_lpls(X, Y, Z, a_max, alpha=.7, mean_ctr=[2, 0, 2], scale='scores', zo
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return {'T':T, 'W':W, 'P':P, 'Q':Q, 'U':U, 'L':L, 'K':K, 'B':B, 'E': E, 'F': F, 'G': G, 'evx':evx, 'evy':evy, 'evz':evz,'mnx': mnX, 'mny': mnY, 'mnz': mnZ}
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def lpls_predict(model_dict, x, aopt):
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"""Predict lpls reponses from existing model on new data.
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"""
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try:
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m, n = x.shape
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except:
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x = atleast_2d(x.shape)
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m, n = x.shape
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if 'B0' in model_dict.keys():
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y = model_dict['B0'] + dot()
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def vnorm(a):
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"""Returns the norm of a vector.
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@ -714,19 +733,19 @@ def _scale(a, axis):
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return a - sc, sc
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def esvd(data, a_max=None):
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""" SVD with kernel calculation
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"""SVD with kernel calculation.
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Calculate subspaces of X'X or XX' depending on the shape
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of the matrix.
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Parameters:
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*Parameters*:
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data : {array}
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Data matrix
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a_max : {integer}
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Number of components to extract
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Returns:
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*Returns*:
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u : {array}
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Right hand eigenvectors
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@ -735,9 +754,9 @@ def esvd(data, a_max=None):
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v : {array}
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Left hand eigenvectors
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notes:
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*Notes*:
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Uses Anoldi iterations (ARPACK)
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Uses Anoldi iterations for the symmetric eigendecomp (ARPACK)
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"""
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