Python numpy.nan_to_num方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nan_to_num [as 別名]
def observe(self) -> np.array:
        """Returns the rows to be observed by the agent."""
        rows = self.rows.copy()

        if len(rows) < self.window_size:
            size = self.window_size - len(rows)
            padding = np.zeros((size, rows.shape[1]))
            padding = pd.DataFrame(padding, columns=self.rows.columns)
            rows = pd.concat([padding, rows], ignore_index=True, sort=False)

        if isinstance(rows, pd.DataFrame):
            rows = rows.fillna(0, axis=1)
            rows = rows.values

        rows = np.nan_to_num(rows)

        return rows 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nan_to_num [as 別名]
def tfIdf(dtm):
  nDoc = dtm.shape[0]
  nTerm = dtm.shape[1]
  dtmNorm = dtm/dtm.sum(axis=1, keepdims=True) # Normalize tf to unit weight, tf/line word count
  dtmNorm = np.nan_to_num(dtmNorm)
  tfIdfMat = np.zeros((nDoc,nTerm))
  
  for j in range(nTerm):
    tfVect = dtmNorm[:, j]
    nExist = np.sum(tfVect > 0.0) # if tfVect is 0.0, word is not in current doc
    idf = 0.0
    # int32
    if (nExist > 0):
      idf = np.log(nDoc/nExist)/np.log(2) # log2()
    else:
      idf = 0.0
    tfIdfMat[:,j] = tfVect * idf
  
  return tfIdfMat 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nan_to_num [as 別名]
def pagerank(nDim, adjMat, d, K):
    '''
    Args:
    d: damping factor, 
    K: iteration Number
    '''
    P = np.ones((nDim, 1)) * (1/nDim)
    
    # normalize adjacency Matrix
    B = adjMat/adjMat.sum(axis=1, keepdims=True)
    B = np.nan_to_num(B)
    
    U = np.ones((nDim, nDim)) * (1/nDim)
    
    M = d * B + (1-d) * U
    
    for i in range(K):
        P = np.dot(M.T, P)
    score = P.tolist()
    return P 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nan_to_num [as 別名]
def lf_overlaps(L, normalize_by_coverage=False):
    """Return the **fraction of items each LF labels that are also labeled by at
     least one other LF.**

    Note that the maximum possible overlap fraction for an LF is the LF's
    coverage, unless `normalize_by_coverage=True`, in which case it is 1.

    Args:
        L: an n x m scipy.sparse matrix where L_{i,j} is the label given by the
            jth LF to the ith candidate
        normalize_by_coverage: Normalize by coverage of the LF, so that it
            returns the percent of LF labels that have overlaps.
    """
    overlaps = (L != 0).T @ _overlapped_data_points(L) / L.shape[0]
    if normalize_by_coverage:
        overlaps /= lf_coverages(L)
    return np.nan_to_num(overlaps) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nan_to_num [as 別名]
def lf_conflicts(L, normalize_by_overlaps=False):
    """Return the **fraction of items each LF labels that are also given a
    different (non-abstain) label by at least one other LF.**

    Note that the maximum possible conflict fraction for an LF is the LF's
        overlaps fraction, unless `normalize_by_overlaps=True`, in which case it
        is 1.

    Args:
        L: an n x m scipy.sparse matrix where L_{i,j} is the label given by the
            jth LF to the ith candidate
        normalize_by_overlaps: Normalize by overlaps of the LF, so that it
            returns the percent of LF overlaps that have conflicts.
    """
    conflicts = (L != 0).T @ _conflicted_data_points(L) / L.shape[0]
    if normalize_by_overlaps:
        conflicts /= lf_overlaps(L)
    return np.nan_to_num(conflicts) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nan_to_num [as 別名]
def __init__(self, data, hpc_p, costs):
        self.data_x = data.iloc[:, 0:-1].astype('float32').values
        self.data_n = np.isnan(self.data_x)
        self.data_x = np.nan_to_num(self.data_x)

        self.data_y = data.iloc[:,   -1].astype('int32').values

        self.data_len = len(data)

        self.hpc_p = hpc_p.values
        self.costs = costs.values

        self.mask = np.zeros( (config.AGENTS, config.FEATURE_DIM), dtype=np.float32 )
        self.x    = np.zeros( (config.AGENTS, config.FEATURE_DIM), dtype=np.float32 )
        self.y    = np.zeros( config.AGENTS, dtype=np.int64 )
        self.p    = np.zeros( config.AGENTS, dtype=np.int32 )
        self.n    = np.zeros( (config.AGENTS, config.FEATURE_DIM), dtype=np.bool ) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nan_to_num [as 別名]
def nan_dot(A, B):
    """
    Returns np.dot(left_matrix, right_matrix) with the convention that
    nan * 0 = 0 and nan * x = nan if x != 0.

    Parameters
    ----------
    A, B : np.ndarrays
    """
    # Find out who should be nan due to nan * nonzero
    should_be_nan_1 = np.dot(np.isnan(A), (B != 0))
    should_be_nan_2 = np.dot((A != 0), np.isnan(B))
    should_be_nan = should_be_nan_1 + should_be_nan_2

    # Multiply after setting all nan to 0
    # This is what happens if there were no nan * nonzero conflicts
    C = np.dot(np.nan_to_num(A), np.nan_to_num(B))

    C[should_be_nan] = np.nan

    return C 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nan_to_num [as 別名]
def _average_precision(self, rec, prec):
        """
        calculate average precision, override the default one,
        special 11-point metric

        Params:
        ----------
        rec : numpy.array
            cumulated recall
        prec : numpy.array
            cumulated precision
        Returns:
        ----------
        ap as float
        """
        if rec is None or prec is None:
            return np.nan
        ap = 0.
        for t in np.arange(0., 1.1, 0.1):
            if np.sum(rec >= t) == 0:
                p = 0
            else:
                p = np.max(np.nan_to_num(prec)[rec >= t])
            ap += p / 11.
        return ap 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nan_to_num [as 別名]
def Transform(self, data_container, store_folder='', store_key=''):
        if data_container.IsEmpty():
            return data_container

        new_data_container = deepcopy(data_container)
        array = new_data_container.GetArray()
        array -= self._interception
        array /= self._slop
        array = np.nan_to_num(array)

        new_data_container.SetArray(array)
        new_data_container.UpdateFrameByData()

        if store_folder:
            assert(len(store_key) > 0)
            self.SaveNormalDataContainer(data_container, store_folder, store_key)

        return new_data_container 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nan_to_num [as 別名]
def macro_accuracy(P, Y, n_classes, bg_class=None, return_all=False, **kwargs):
    def macro_(P, Y, n_classes=None, bg_class=None, return_all=False):
        conf_matrix = sm.confusion_matrix(Y, P, labels=np.arange(n_classes))
        conf_matrix = conf_matrix / (conf_matrix.sum(0)[:, None] + 1e-5)
        conf_matrix = np.nan_to_num(conf_matrix)
        diag = conf_matrix.diagonal() * 100.

        # Remove background score
        if bg_class is not None:
            diag = np.array([diag[i] for i in range(n_classes) if i != bg_class])

        macro = diag.mean()
        if return_all:
            return macro, diag
        else:
            return macro

    if type(P) == list:
        out = [macro_(P[i], Y[i], n_classes=n_classes, bg_class=bg_class, return_all=return_all) for i in range(len(P))]
        if return_all:
            return (np.mean([o[0] for o in out]), np.mean([o[1] for o in out], 0))
        else:
            return np.mean(out)
    else:
        return macro_(P, Y, n_classes=n_classes, bg_class=bg_class, return_all=return_all) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nan_to_num [as 別名]
def rotate_around_axis(coords, Q, origin='empty'):
    '''Uses standard quaternion to rotate a vector. Q requires
    a 4-dimensional vector. coords is the 3d location of the point.
    coords can also be an N x 3 array of vectors. Happens to work
    with Q as a tuple or a np array shape 4'''
    if origin == 'empty':    
        vcV = np.cross(Q[1:], coords)
        RV = np.nan_to_num(coords + vcV * (2*Q[0]) + np.cross(Q[1:],vcV)*2)
    else:
        coords -= origin
        vcV = np.cross(Q[1:],coords)
        RV = (np.nan_to_num(coords + vcV * (2*Q[0]) + np.cross(Q[1:],vcV)*2)) + origin       
        coords += origin #undo in-place offset
    return RV 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nan_to_num [as 別名]
def barycentric_generate(hits, tris):
    '''Create scalars to be used by points and triangles'''
    # where the hit lands on the two tri vecs
    tv = tris[:, 1] - tris[:, 0]
    hv = hits - tris[:, 0]
    d1a = np.einsum('ij, ij->i', hv, tv)
    d1b = np.einsum('ij, ij->i', tv, tv)
    scalar1 = np.nan_to_num(d1a / d1b)

    t2v = tris[:, 2] - tris[:, 0]
    d2a = np.einsum('ij, ij->i', hv, t2v)
    d2b = np.einsum('ij, ij->i', t2v, t2v)
    scalar2 = np.nan_to_num(d2a / d2b)
    
    # closest point on edge segment between the two points created above
    cp1 = tv * np.expand_dims(scalar1, axis=1)
    cp2 = t2v * np.expand_dims(scalar2, axis=1)
    cpvec = cp2 - cp1
    cp1_at = tris[:,0] + cp1
    hcp = hits - cp1_at # this is cp3 above. Not sure what's it's for yet
    dhcp = np.einsum('ij, ij->i', hcp, cpvec)
    d3b = np.einsum('ij, ij->i', cpvec, cpvec)
    hcp_scalar = np.nan_to_num(dhcp / d3b)
    hcp_vec = cpvec * np.expand_dims(hcp_scalar, axis=1)    
    
    # base of tri on edge between first two points
    d3 = np.einsum('ij, ij->i', -cp1, cpvec)
    scalar3 = np.nan_to_num(d3 / d3b)
    base_cp_vec = cpvec * np.expand_dims(scalar3, axis=1)
    base_on_span = cp1_at + base_cp_vec

    # Where the point occurs on the edge between the base of the triangle
    #   and the cpoe of the base of the triangle on the cpvec    
    base_vec = base_on_span - tris[:,0]
    dba = np.einsum('ij, ij->i', hv, base_vec)
    dbb = np.einsum('ij, ij->i', base_vec, base_vec)
    scalar_final = np.nan_to_num(dba / dbb)
    p_on_bv = base_vec * np.expand_dims(scalar_final, axis=1)
    perp = (p_on_bv) - (cp1 + base_cp_vec)
    return scalar1, scalar2, hcp_scalar, scalar3, scalar_final 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nan_to_num [as 別名]
def project_points(points, tri_coords):
    '''Using this to get the points off the surface
    Takes the average length of two vecs off triangles
    and applies it to the length of the normals.
    This way the normal scales with the mesh and with
    changes to the individual triangle vectors'''
    t0 = tri_coords[:, 0]
    t1 = tri_coords[:, 1]
    t2 = tri_coords[:, 2]
    tv1 = t1 - t0
    tv2 = t2 - t0
    cross = np.cross(tv1, tv2)
    
    # get the average length of the two vectors and apply it to the cross product
    sq = np.sqrt(np.einsum('ij,ij->i', cross, cross))
    x1 = np.einsum('ij,ij->i', tv1, tv1)
    x2 = np.einsum('ij,ij->i', tv2, tv2)
    av_root = np.sqrt((x1 + x2) / 2)
    cr_root = (cross / np.expand_dims(sq, axis=1)) * np.expand_dims(av_root, axis=1)    
     
    v1 = points - t0
    v1_dots = np.einsum('ij,ij->i', cr_root, v1)
    n_dots = np.einsum('ij,ij->i', cr_root, cr_root)
    scale = np.nan_to_num(v1_dots / n_dots)
    offset = cr_root * np.expand_dims(scale, axis=1)
    drop = points - offset # The drop is used by the barycentric generator as points in the triangles
    return drop, scale 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nan_to_num [as 別名]
def decayCoefObjectiveFn(x, Y, EX2):
	"""
	Computes the objective function for terms involving lambda in the M-step.
	Checked.
	Input:
	x: value of lambda
	Y: the matrix of observed values
	EX2: the matrix of values of EX2 estimated in the E-step.
	Returns:
	obj: value of objective function
	grad: gradient
	"""
	with warnings.catch_warnings():
		warnings.simplefilter("ignore")

		y_squared = Y ** 2
		Y_is_zero = np.abs(Y) < 1e-6
		exp_Y_squared = np.exp(-x * y_squared)
		log_exp_Y = np.nan_to_num(np.log(1 - exp_Y_squared))
		exp_ratio = np.nan_to_num(exp_Y_squared / (1 - exp_Y_squared))
		obj = sum(sum(Y_is_zero * (-EX2 * x) + (1 - Y_is_zero) * log_exp_Y))
		grad = sum(sum(Y_is_zero * (-EX2) + (1 - Y_is_zero) * y_squared * exp_ratio))

		if (type(obj) is not np.float64) or (type(grad) is not np.float64):
			raise Exception("Unexpected behavior in optimizing decay coefficient lambda. Please contact emmap1@cs.stanford.edu.")
		if type(obj) is np.float64:
			obj = -np.array([obj])
		if type(grad) is np.float64:
			grad = -np.array([grad])

		return obj, grad 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import nan_to_num [as 別名]
def forward(y_hat, y):
        y_hat = _cutoff(y_hat)
        y = _cutoff(y)
        return -np.mean(np.sum(np.nan_to_num(y * np.log(y_hat) + (1 - y) * np.log(1 - y_hat)), axis=1))