Exemple #1
0
def add_indep(x, varnames, dtype=None):
    '''
    construct array with independent columns

    x is either iterable (list, tuple) or instance of ndarray or a subclass of it.
    If x is an ndarray, then each column is assumed to represent a variable with
    observations in rows.
    '''
    #TODO: this needs tests for subclasses

    if isinstance(x, np.ndarray) and x.ndim == 2:
        x = x.T

    nvars_orig = len(x)
    nobs = len(x[0])
    #print 'nobs, nvars_orig', nobs, nvars_orig
    if not dtype:
        dtype = np.asarray(x[0]).dtype
    xout = np.zeros((nobs, nvars_orig), dtype=dtype)
    count = 0
    rank_old = 0
    varnames_new = []
    varnames_dropped = []
    keepindx = []
    for (xi, ni) in zip(x, varnames):
        #print xi.shape, xout.shape
        xout[:,count] = xi
        rank_new = smrank(xout)
        #print rank_new
        if  rank_new > rank_old:
            varnames_new.append(ni)
            rank_old = rank_new
            count += 1
        else:
            varnames_dropped.append(ni)

    return xout[:,:count], varnames_new
Exemple #2
0
def add_indep(x, varnames, dtype=None):
    '''
    construct array with independent columns

    x is either iterable (list, tuple) or instance of ndarray or a subclass of it.
    If x is an ndarray, then each column is assumed to represent a variable with
    observations in rows.
    '''
    #TODO: this needs tests for subclasses

    if isinstance(x, np.ndarray) and x.ndim == 2:
        x = x.T

    nvars_orig = len(x)
    nobs = len(x[0])
    #print 'nobs, nvars_orig', nobs, nvars_orig
    if not dtype:
        dtype = np.asarray(x[0]).dtype
    xout = np.zeros((nobs, nvars_orig), dtype=dtype)
    count = 0
    rank_old = 0
    varnames_new = []
    varnames_dropped = []
    keepindx = []
    for (xi, ni) in zip(x, varnames):
        #print xi.shape, xout.shape
        xout[:, count] = xi
        rank_new = smrank(xout)
        #print rank_new
        if rank_new > rank_old:
            varnames_new.append(ni)
            rank_old = rank_new
            count += 1
        else:
            varnames_dropped.append(ni)

    return xout[:, :count], varnames_new
 def check_rank(self, J):
     rank = smrank(J)
     if rank < np.size(J, axis=1):
         raise ValueError("Rank condition not met: "
                          "solution may not be unique.")
 def check_rank(self, J):
     rank = smrank(J)
     if rank < np.size(J, axis=1):
         raise ValueError("Rank condition not met: " "solution may not be unique.")