Python numpy.max函数代码示例

 def max(self, axis=None, out=None, keepdims=False):
     self._prepare_out(out=out)
     try:
         value = np.max(self.value, axis=axis, out=out, keepdims=keepdims)
     except:  # numpy < 1.7
         value = np.max(self.value, axis=axis, out=out)
     return self.__quantity_instance__(value, self.unit, copy=False)

def cfl_superbee_theta(r,cfl,theta=0.95):
    r"""
    CFL-Superbee (Roe's Ultrabee) with theta parameter
    """
    a = np.empty((2,len(r)))
    b = np.zeros((2,len(r)))
    
    a[0,:] = 0.001
    a[1,:] = cfl
    cfmod1 = np.max(a,axis=0)
    a[0,:] = 0.999
    cfmod2 = np.min(a,axis=0)

    s1 = theta * 2.0 / cfmod1
    phimax = theta * 2.0 / (1.0 - cfmod2)

    a[0,:] = s1*r
    a[1,:] = phimax
    b[1,:] = np.min(a,axis=0)
    ultra = np.max(b,axis=0)
    
    a[0,:] = ultra
    b[0,:] = 1.0
    b[1,:] = r
    a[1,:] = np.max(b,axis=0)
    return np.min(a,axis=0)

def cada_torrilhon_limiter(r,cfl,epsilon=1.0e-3):
    r"""
    Cada-Torrilhon modified
    
    Additional Input:
     - *epsilon* = 
    """
    a = np.ones((2,len(r))) * 0.95
    b = np.empty((3,len(r)))

    a[0,:] = cfl
    cfl = np.min(a)
    a[1,:] = 0.05
    cfl = np.max(a)
    
    # Multiply all parts except b[0,:] by (1.0 - epsilon) as well
    b[0,:] = 1.0 + (1+cfl) / 3.0 * (r - 1)
    b[1,:] = 2.0 * np.abs(r) / (cfl + epsilon)
    b[2,:] = (8.0 - 2.0 * cfl) / (np.abs(r) * (cfl - 1.0 - epsilon)**2)
    b[1,::2] *= (1.0 - epsilon)
    a[0,:] = np.min(b)
    a[1,:] = (-2.0 * (cfl**2 - 3.0 * cfl + 8.0) * (1.0-epsilon)
                    / (np.abs(r) * (cfl**3 - cfl**2 - cfl + 1.0 + epsilon)))
    
    return np.max(a)

    def work(self):
        self.worked = True
        kwargs = dict(
                weights=self.weights,
                mus=self.mus,
                sigmas=self.sigmas,
                low=self.low,
                high=self.high,
                q=self.q,
                )
        samples = GMM1(rng=self.rng,
                size=(self.n_samples,),
                **kwargs)
        samples = np.sort(samples)
        edges = samples[::self.samples_per_bin]
        #print samples

        pdf = np.exp(GMM1_lpdf(edges[:-1], **kwargs))
        dx = edges[1:] - edges[:-1]
        y = 1 / dx / len(dx)

        if self.show:
            plt.scatter(edges[:-1], y)
            plt.plot(edges[:-1], pdf)
            plt.show()
        err = (pdf - y) ** 2
        print np.max(err)
        print np.mean(err)
        print np.median(err)
        if not self.show:
            assert np.max(err) < .1
            assert np.mean(err) < .01
            assert np.median(err) < .01

def Haffine_from_points(fp, tp):
    '''计算仿射变换的单应性矩阵H，使得tp是由fp经过仿射变换得到的'''
    if fp.shape != tp.shape:
        raise RuntimeError('number of points do not match')

    # 对点进行归一化
    # 映射起始点
    m = numpy.mean(fp[:2], axis=1)
    maxstd = numpy.max(numpy.std(fp[:2], axis=1)) + 1e-9
    C1 = numpy.diag([1/maxstd, 1/maxstd, 1])
    C1[0, 2] = -m[0] / maxstd
    C1[1, 2] = -m[1] / maxstd
    fp_cond = numpy.dot(C1, fp)

    # 映射对应点
    m = numpy.mean(tp[:2], axis=1)
    maxstd = numpy.max(numpy.std(tp[:2], axis=1)) + 1e-9
    C2 = numpy.diag([1/maxstd, 1/maxstd, 1])
    C2[0, 2] = -m[0] / maxstd
    C2[1, 2] = -m[1] / maxstd
    tp_cond = numpy.dot(C2, tp)

    # 因为归一化之后点的均值为0，所以平移量为0
    A = numpy.concatenate((fp_cond[:2], tp_cond[:2]), axis=0)
    U, S, V = numpy.linalg.svd(A.T)
    # 创建矩阵B和C
    tmp = V[:2].T
    B = tmp[:2]
    C = tmp[2:4]

    tmp2 = numpy.concatenate((numpy.dot(C, numpy.linalg.pinv(B)), numpy.zeros((2, 1))), axis=1)
    H = numpy.vstack((tmp2, [0, 0, 1]))

    H = numpy.dot(numpy.linalg.inv(C2), numpy.dot(H, C1))  # 反归一化
    return H / H[2, 2]  # 归一化，然后返回

def viterbi_decode(score, transition_params):
  """Decode the highest scoring sequence of tags outside of TensorFlow.

  This should only be used at test time.

  Args:
    score: A [seq_len, num_tags] matrix of unary potentials.
    transition_params: A [num_tags, num_tags] matrix of binary potentials.

  Returns:
    viterbi: A [seq_len] list of integers containing the highest scoring tag
        indicies.
    viterbi_score: A float containing the score for the Viterbi sequence.
  """
  trellis = np.zeros_like(score)
  backpointers = np.zeros_like(score, dtype=np.int32)
  trellis[0] = score[0]

  for t in range(1, score.shape[0]):
    v = np.expand_dims(trellis[t - 1], 1) + transition_params
    trellis[t] = score[t] + np.max(v, 0)
    backpointers[t] = np.argmax(v, 0)

  viterbi = [np.argmax(trellis[-1])]
  for bp in reversed(backpointers[1:]):
    viterbi.append(bp[viterbi[-1]])
  viterbi.reverse()

  viterbi_score = np.max(trellis[-1])
  return viterbi, viterbi_score

    def _crinfo_from_specific_data (self, data, margin):
# hledáme automatický ořez, nonzero dá indexy
        nzi = np.nonzero(data)

        x1 = np.min(nzi[0]) - margin[0]
        x2 = np.max(nzi[0]) + margin[0] + 1
        y1 = np.min(nzi[1]) - margin[0]
        y2 = np.max(nzi[1]) + margin[0] + 1
        z1 = np.min(nzi[2]) - margin[0]
        z2 = np.max(nzi[2]) + margin[0] + 1 

# ošetření mezí polí
        if x1 < 0:
            x1 = 0
        if y1 < 0:
            y1 = 0
        if z1 < 0:
            z1 = 0

        if x2 > data.shape[0]:
            x2 = data.shape[0]-1
        if y2 > data.shape[1]:
            y2 = data.shape[1]-1
        if z2 > data.shape[2]:
            z2 = data.shape[2]-1

# ořez
        crinfo = [[x1, x2],[y1,y2],[z1,z2]]
        #dataout = self._crop(data,crinfo)
        #dataout = data[x1:x2, y1:y2, z1:z2]
        return crinfo

    def _get_initial_classes(self):
        images = map(lambda f: cv2.imread(path.join(self._root, f)), self._files)
        self._avg_pixels = np.array([], dtype=np.uint8)

        # extract parts from each image for all of our 6 categories
        for i in range(0, self._n_objects):
            rects = self._rects[:, i]
            
            # compute maximum rectangle
            rows = np.max(rects['f2'] - rects['f0'])
            cols = np.max(rects['f3'] - rects['f1'])

            # extract annotated rectangles
            im_rects = map(lambda (im, r): im[r[0]:r[2],r[1]:r[3],:], zip(images, rects))

            # resize all rectangles to the max size & average all the rectangles
            im_rects = np.array(map(lambda im: cv2.resize(im, (cols, rows)), im_rects), dtype=np.float)
            avgs = np.around(np.average(im_rects, axis = 0))

            # average the resulting rectangle to compute 
            mn = np.around(np.array(cv2.mean(avgs), dtype='float'))[:-1].astype('uint8')

            if(self._avg_pixels.size == 0):
                self._avg_pixels = mn
            else:
                self._avg_pixels = np.vstack((self._avg_pixels, mn))

	def get_batch(self, model, batch_size):
		len_memory = len(self.memory)
		num_actions = 6
		encouraged_actions = np.zeros(num_actions, dtype=np.int)
		predicted_actions = np.zeros(num_actions, dtype=np.int)
		inputs = np.zeros((min(len_memory, batch_size), 4, 80, 74))
		targets = np.zeros((inputs.shape[0], num_actions))
		q_list = np.zeros(inputs.shape[0])
		for i, idx in enumerate(np.random.randint(0, len_memory, size=inputs.shape[0])):
			input_t, action_t, reward_t, input_tp1 = self.memory[idx][0]
			terminal = self.memory[idx][1]

			inputs[i] = input_t

			targets[i] = model.predict(input_t.reshape(1, 4, 80, 74))[0]
			q_next = np.max(model.predict(input_tp1.reshape(1, 4, 80, 74))[0])

			q_list[i] = np.max(targets[i])
			predicted_actions[np.argmax(targets[i])] += 1

			targets[i, action_t] =  (1. - terminal) * self.discount * q_next + reward_t

			if reward_t > 0. or terminal:
				print "Action %d rewarded with %f (sample #%d)"%(action_t, targets[i, action_t], idx)

			encouraged_actions[np.argmax(targets[i])] += 1

		return inputs, targets, encouraged_actions, predicted_actions, np.average(q_list)

    def quantify(self):
        """Quantify shape of the contours."""
        four_pi = 4. * np.pi
        for edge in self.edges:
            # Positions
            x = edge['x']
            y = edge['y']

            A, perimeter, x_center, y_center, distances = \
                self.get_shape_factor(x, y)

            # Set values.
            edge['area'] = A
            edge['perimeter'] = perimeter
            edge['x_center'] = x_center
            edge['y_center'] = y_center
            # Circle is 1. Rectangle is 0.78. Thread-like is close to zero.
            edge['shape_factor'] = four_pi * edge['area'] / \
                                   edge['perimeter'] ** 2.

            # We assume that the radius of the edge
            # as the median value of the distances from the center.
            radius = np.median(distances)
            edge['radius_deviation'] = np.std(distances - radius) / radius

            edge['x_min'] = np.min(x)
            edge['x_max'] = np.max(x)
            edge['y_min'] = np.min(y)
            edge['y_max'] = np.max(y)

 def get_num_samples(self, idx):
     """
     Number of samples needed to estimate the population variance within the tolerance limit
     Sample variance is normally distributed http://stats.stackexchange.com/a/105338/71884
     (see warning below).
     Var(s^2) /approx 1/n * (\mu_4 - \sigma^4)
     Adjust n as per the tolerance needed to estimate the sample variance
     warning: does not work for some distributions like bernoulli - https://stats.stackexchange.com/a/104911
     use the min_samples for explicitly controlling the number of samples to be drawn
     """
     if self.min_samples:
         return self.min_samples
     min_samples = 1000
     tol = 10.0
     required_precision = self.prec / tol
     if not self.scipy_dist:
         return min_samples
     args, kwargs = self.scipy_arg_fn(**self.get_dist_params(idx, wrap_tensor=False))
     try:
         fourth_moment = np.max(self.scipy_dist.moment(4, *args, **kwargs))
         var = np.max(self.scipy_dist.var(*args, **kwargs))
         min_computed_samples = int(math.ceil((fourth_moment - math.pow(var, 2)) / required_precision))
     except (AttributeError, ValueError):
         return min_samples
     return max(min_samples, min_computed_samples)

def gm_assign_to_cluster(X, center_list, cov_list, p_k):
    """Assigns each sample to one of the Gaussian clusters given.
    
    Returns an array with numbers, 0 corresponding to the first cluster in the
    cluster list.
    """
    # Reused code from E-step, should be unified somehow:
    samples = X.shape[0]
    K = len(center_list)
    log_p_Xn_mat = np.zeros((samples, K))
    for k in range(K):
        log_p_Xn_mat[:, k] = logmulnormpdf(X, center_list[k], cov_list[k]) + np.log(p_k[k])
    pmax = np.max(log_p_Xn_mat, axis=1)
    log_p_Xn = pmax + np.log(np.sum(np.exp(log_p_Xn_mat.T - pmax), axis=0).T)
    logL = np.sum(log_p_Xn)

    log_p_nk = np.zeros((samples, K))
    for k in range(K):
        # log_p_nk[:,k] = logmulnormpdf(X, center_list[k], cov_list[k]) + np.log(p_k[k]) - log_p_Xn
        log_p_nk[:, k] = log_p_Xn_mat[:, k] - log_p_Xn

    print log_p_nk
    # Assign to cluster:
    maxP_k = np.c_[np.max(log_p_nk, axis=1)] == log_p_nk
    # print np.max(log_p_nk, axis=1)
    maxP_k = maxP_k * (np.array(range(K)) + 1)
    return np.sum(maxP_k, axis=1) - 1

def makeThresholdMap(image, findCars, scales=[1.5], percentOfHeapmapToToss=.5):
    print("scales:", scales, ", type:", type(scales), "image.shape:", image.shape, ", dtype:", image.dtype, ", percentOfHeapmapToToss:", percentOfHeapmapToToss)
    boundingBoxList=[]
    boundingBoxWeights=[]
    for scale in scales:
        listOfBoundingBoxes, listOfWeights = findCars(image, scale)
        boundingBoxList+=listOfBoundingBoxes
        boundingBoxWeights+=listOfWeights

    if USEBOUNDINGBOXWEIGHTS:
        unNormalizedHeatMap=addWeightedHeat(image.shape, boundingBoxList, boundingBoxWeights)
    else:
        unNormalizedHeatMap=addHeat(image.shape, boundingBoxList)

    if USESTACKOFHEATMAPS:
        unNormalizedHeatMap,_=totalHeatmapStack(unNormalizedHeatMap)


    unNormalizedHeatMapCounts=np.unique(unNormalizedHeatMap, return_counts=True)
    if TESTING: print("makeThresholdMap-unNormalizedHeatMapCounts:", unNormalizedHeatMapCounts, ", len(unNormalizedHeatMapCounts):", len(unNormalizedHeatMapCounts), ", len(unNormalizedHeatMapCounts[0]):", len(unNormalizedHeatMapCounts[0]))
    unNormalizedHeatMapMidpoint=unNormalizedHeatMapCounts[0][int(round(len(unNormalizedHeatMapCounts[0])*percentOfHeapmapToToss))]
    thresholdMap=applyThreshold(unNormalizedHeatMap, unNormalizedHeatMapMidpoint)
    print("makeThresholdMap-max(thresholdMap):", np.max(thresholdMap), ", min(thresholdMap):", np.min(thresholdMap))
    if TESTING: print("makeThresholdMap-thresholdMap counts:", (np.unique(thresholdMap, return_counts=True)), ", len(thresholdMap):", len(thresholdMap), ", len(thresholdMap[0]):", len(thresholdMap[0]))
    normalizedMap=normalizeMap(thresholdMap)
    if TESTING: print("makeThresholdMap-normalizedMap counts:", (np.unique(normalizedMap, return_counts=True)), ", len(normalizedMap):", len(normalizedMap), ", len(normalizedMap[0]):", len(normalizedMap[0]))
    print("makeThresholdMap-max(normalizedMap):", np.max(normalizedMap), ", min(normalizedMap):", np.min(normalizedMap))
    return normalizedMap, boundingBoxList, unNormalizedHeatMap, boundingBoxWeights

    def diff_dist_matrix(self, res_range=None, scaled=False):
        if res_range != None: assert(len(res_range) == 2)
        
        dist_matrices = []
        for pdb in self.get_next_pdb():
            ca_xyz = pdb.get_ca_xyz_matrix()
            if res_range != None: ca_xyz = ca_xyz[res_range[0]-1:res_range[1], :]
            dist_matrix = calc_distance_matrix(ca_xyz)
            dist_matrices.append(dist_matrix)

        scaled_diff_dist_matrix = num.zeros(dist_matrices[0].shape, 'd')
        count = 0
        for i in range(len(dist_matrices)):
            for j in range(i+1, len(dist_matrices)):
                diff_dist_matrix = num.abs(dist_matrices[i] - dist_matrices[j])
                if scaled:
                    scale = num.max(diff_dist_matrix)
                    if scale == 0: continue
                    diff_dist_matrix /= scale
                scaled_diff_dist_matrix += diff_dist_matrix
                count += 1
        #print >> sys.stderr, count
        scaled_diff_dist_matrix /= count
        if scaled:
            scaled_diff_dist_matrix /= num.max(scaled_diff_dist_matrix)
        return scaled_diff_dist_matrix

def grid_xyz(xyz, n_x, n_y, **kwargs):
    """ Grid data as a list of X,Y,Z coords into a 2D array

    Parameters
    ----------
    xyz: np.array
        Numpy array of X,Y,Z values, with shape (n_points, 3)
    n_x: int
        Number of points in x direction (fastest varying!)
    n_y: int
        Number of points in y direction

    Returns
    -------
    gridded_data: np.array
        2D array of gridded data, with shape (n_x, n_y)

    Notes
    -----
    'x' is the inner dimension, i.e. image dimensions are (n_y, n_x). This is
    counterintuitive (to me at least) but in line with numpy definitions.
    """
    x, y, z = xyz[:, 0], xyz[:, 1], xyz[:, 2]
    x_ax = np.linspace(np.min(x), np.max(x), n_x)
    y_ax = np.linspace(np.min(y), np.max(y), n_y)

    xg, yg = np.meshgrid(x_ax, y_ax)

    data = griddata(xyz[:, :2], z, (xg, yg), **kwargs)
    return data