Python numpy.finfo方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import finfo [as 別名]
def __init__(self, renders=True):
    # start the bullet physics server
    self._renders = renders
    if (renders):
	    p.connect(p.GUI)
    else:
    	p.connect(p.DIRECT)

    observation_high = np.array([
          np.finfo(np.float32).max,
          np.finfo(np.float32).max,
          np.finfo(np.float32).max,
          np.finfo(np.float32).max])
    action_high = np.array([0.1])

    self.action_space = spaces.Discrete(9)
    self.observation_space = spaces.Box(-observation_high, observation_high)

    self.theta_threshold_radians = 1
    self.x_threshold = 2.4
    self._seed()
#    self.reset()
    self.viewer = None
    self._configure() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import finfo [as 別名]
def SpectralClustering(CKSym, n):
    # This is direct port of JHU vision lab code. Could probably use sklearn SpectralClustering.
    CKSym = CKSym.astype(float)
    N, _ = CKSym.shape
    MAXiter = 1000  # Maximum number of iterations for KMeans
    REPlic = 20  # Number of replications for KMeans

    DN = np.diag(np.divide(1, np.sqrt(np.sum(CKSym, axis=0) + np.finfo(float).eps)))
    LapN = identity(N).toarray().astype(float) - np.matmul(np.matmul(DN, CKSym), DN)
    _, _, vN = np.linalg.svd(LapN)
    vN = vN.T
    kerN = vN[:, N - n:N]
    normN = np.sqrt(np.sum(np.square(kerN), axis=1))
    kerNS = np.divide(kerN, normN.reshape(len(normN), 1) + np.finfo(float).eps)
    km = KMeans(n_clusters=n, n_init=REPlic, max_iter=MAXiter, n_jobs=-1).fit(kerNS)
    return km.labels_ 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import finfo [as 別名]
def merge(self, tiles: List[np.ndarray], dtype=np.float32):
        if len(tiles) != len(self.crops):
            raise ValueError

        channels = 1 if len(tiles[0].shape) == 2 else tiles[0].shape[2]
        target_shape = self.image_height + self.margin_bottom + self.margin_top, self.image_width + self.margin_right + self.margin_left, channels

        image = np.zeros(target_shape, dtype=np.float64)
        norm_mask = np.zeros(target_shape, dtype=np.float64)

        w = np.dstack([self.weight] * channels)

        for tile, (x, y, tile_width, tile_height) in zip(tiles, self.crops):
            # print(x, y, tile_width, tile_height, image.shape)
            image[y:y + tile_height, x:x + tile_width] += tile * w
            norm_mask[y:y + tile_height, x:x + tile_width] += w

        # print(norm_mask.min(), norm_mask.max())
        norm_mask = np.clip(norm_mask, a_min=np.finfo(norm_mask.dtype).eps, a_max=None)
        normalized = np.divide(image, norm_mask).astype(dtype)
        crop = self.crop_to_orignal_size(normalized)
        return crop 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import finfo [as 別名]
def _mutual_information_varoquaux(x, y, bins=256, sigma=1, normalized=True):
    """Based on Gael Varoquaux's implementation: https://gist.github.com/GaelVaroquaux/ead9898bd3c973c40429."""
    jh = np.histogram2d(x, y, bins=bins)[0]

    # smooth the jh with a gaussian filter of given sigma
    scipy.ndimage.gaussian_filter(jh, sigma=sigma, mode="constant", output=jh)

    # compute marginal histograms
    jh = jh + np.finfo(float).eps
    sh = np.sum(jh)
    jh = jh / sh
    s1 = np.sum(jh, axis=0).reshape((-1, jh.shape[0]))
    s2 = np.sum(jh, axis=1).reshape((jh.shape[1], -1))

    if normalized:
        mi = ((np.sum(s1 * np.log(s1)) + np.sum(s2 * np.log(s2))) / np.sum(jh * np.log(jh))) - 1
    else:
        mi = np.sum(jh * np.log(jh)) - np.sum(s1 * np.log(s1)) - np.sum(s2 * np.log(s2))

    return mi 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import finfo [as 別名]
def finish_episode(self, log_probas, saved_rewards):
        R = 0
        policy_loss = []
        rewards = []
        for r in saved_rewards:
            R = r + 0.8 * R
            rewards.insert(0, R)

        rewards = torch.Tensor(rewards)
        # update: we notice improved performance without reward normalization
        # rewards = (rewards - rewards.mean()) / (rewards.std() + np.finfo(np.float32).eps)

        for log_prob, reward in zip(log_probas, rewards):
            policy_loss.append((-log_prob * reward).unsqueeze(0))
        l = len(policy_loss)
        policy_loss = torch.cat(policy_loss).sum()
        return policy_loss / l 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import finfo [as 別名]
def test_int_int_min_max():
    # Conversion between (u)int and (u)int
    eps = np.finfo(np.float64).eps
    rtol = 1e-6
    for in_dt in IUINT_TYPES:
        iinf = np.iinfo(in_dt)
        arr = np.array([iinf.min, iinf.max], dtype=in_dt)
        for out_dt in IUINT_TYPES:
            try:
                aw = SlopeInterArrayWriter(arr, out_dt)
            except ScalingError:
                continue
            arr_back_sc = round_trip(aw)
            # integer allclose
            adiff = int_abs(arr - arr_back_sc)
            rdiff = adiff / (arr + eps)
            assert_true(np.all(rdiff < rtol)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import finfo [as 別名]
def test_int_int_slope():
    # Conversion between (u)int and (u)int for slopes only
    eps = np.finfo(np.float64).eps
    rtol = 1e-7
    for in_dt in IUINT_TYPES:
        iinf = np.iinfo(in_dt)
        for out_dt in IUINT_TYPES:
            kinds = np.dtype(in_dt).kind + np.dtype(out_dt).kind
            if kinds in ('ii', 'uu', 'ui'):
                arrs = (np.array([iinf.min, iinf.max], dtype=in_dt),)
            elif kinds == 'iu':
                arrs = (np.array([iinf.min, 0], dtype=in_dt),
                        np.array([0, iinf.max], dtype=in_dt))
            for arr in arrs:
                try:
                    aw = SlopeArrayWriter(arr, out_dt)
                except ScalingError:
                    continue
                assert_false(aw.slope == 0)
                arr_back_sc = round_trip(aw)
                # integer allclose
                adiff = int_abs(arr - arr_back_sc)
                rdiff = adiff / (arr + eps)
                assert_true(np.all(rdiff < rtol)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import finfo [as 別名]
def test_check_nmant_nexp():
    # Routine for checking number of sigificand digits and exponent
    for t in IEEE_floats:
        nmant = np.finfo(t).nmant
        maxexp = np.finfo(t).maxexp
        assert_true(_check_nmant(t, nmant))
        assert_false(_check_nmant(t, nmant - 1))
        assert_false(_check_nmant(t, nmant + 1))
        assert_true(_check_maxexp(t, maxexp))
        assert_false(_check_maxexp(t, maxexp - 1))
        assert_false(_check_maxexp(t, maxexp + 1))
    # Check against type_info
    for t in ok_floats():
        ti = type_info(t)
        if ti['nmant'] != 106: # This check does not work for PPC double pair
            assert_true(_check_nmant(t, ti['nmant']))
        assert_true(_check_maxexp(t, ti['maxexp'])) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import finfo [as 別名]
def test_rt_bias():
    # Check for bias in round trip
    # Parallel test to arraywriters
    rng = np.random.RandomState(20111214)
    mu, std, count = 100, 10, 100
    arr = rng.normal(mu, std, size=(count,))
    eps = np.finfo(np.float32).eps
    aff = np.eye(4)
    for in_dt in (np.float32, np.float64):
        arr_t = arr.astype(in_dt)
        for out_dt in IUINT_TYPES:
            img = Nifti1Image(arr_t, aff)
            img_back = round_trip(img)
            arr_back_sc = img_back.get_data()
            slope, inter = img_back.get_header().get_slope_inter()
            bias = np.mean(arr_t - arr_back_sc)
            # Get estimate for error
            max_miss = rt_err_estimate(arr_t, arr_back_sc.dtype, slope, inter)
            # Hokey use of max_miss as a std estimate
            bias_thresh = np.max([max_miss / np.sqrt(count), eps])
            assert_true(np.abs(bias) < bias_thresh) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import finfo [as 別名]
def iou(yx_min1, yx_max1, yx_min2, yx_max2, min=None):
    """
    Calculates the IoU of two bounding boxes.
    :author 申瑞瑉 (Ruimin Shen)
    :param yx_min1: The top left coordinates (y, x) of the first bounding boxe.
    :param yx_max1: The bottom right coordinates (y, x) of the first bounding boxe.
    :param yx_min2: The top left coordinates (y, x) of the second bounding boxe.
    :param yx_max2: The bottom right coordinates (y, x) of the second bounding boxe.
    :return: The IoU.
    """
    assert np.all(yx_min1 <= yx_max1)
    assert np.all(yx_min2 <= yx_max2)
    if min is None:
        min = np.finfo(yx_min1.dtype).eps
    yx_min = np.maximum(yx_min1, yx_min2)
    yx_max = np.minimum(yx_max1, yx_max2)
    intersect_area = np.multiply.reduce(np.maximum(0.0, yx_max - yx_min))
    area1 = np.multiply.reduce(yx_max1 - yx_min1)
    area2 = np.multiply.reduce(yx_max2 - yx_min2)
    assert np.all(intersect_area >= 0)
    assert np.all(intersect_area <= area1)
    assert np.all(intersect_area <= area2)
    union_area = np.maximum(area1 + area2 - intersect_area, min)
    return intersect_area / union_area 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import finfo [as 別名]
def iou_matrix(yx_min1, yx_max1, yx_min2, yx_max2, min=None):
    """
    Calculates the IoU of two lists of bounding boxes.
    :author 申瑞瑉 (Ruimin Shen)
    :param yx_min1: The top left coordinates (y, x) of the first list (size [N1, 2]) of bounding boxes.
    :param yx_max1: The bottom right coordinates (y, x) of the first list (size [N1, 2]) of bounding boxes.
    :param yx_min2: The top left coordinates (y, x) of the second list (size [N2, 2]) of bounding boxes.
    :param yx_max2: The bottom right coordinates (y, x) of the second list (size [N2, 2]) of bounding boxes.
    :return: The matrix (size [N1, N2]) of the IoU.
    """
    if min is None:
        min = np.finfo(yx_min1.dtype).eps
    assert np.all(yx_min1 <= yx_max1)
    assert np.all(yx_min2 <= yx_max2)
    intersect_area = intersection_area(yx_min1, yx_max1, yx_min2, yx_max2)
    area1 = np.expand_dims(np.multiply.reduce(yx_max1 - yx_min1, -1), 1)
    area2 = np.expand_dims(np.multiply.reduce(yx_max2 - yx_min2, -1), 0)
    assert np.all(intersect_area >= 0)
    assert np.all(intersect_area <= area1)
    assert np.all(intersect_area <= area2)
    union_area = np.maximum(area1 + area2 - intersect_area, min)
    return intersect_area / union_area 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import finfo [as 別名]
def batch_iou_pair(yx_min1, yx_max1, yx_min2, yx_max2, min=float(np.finfo(np.float32).eps)):
    """
    Pairwisely calculates the IoU of two lists (at the same size M) of bounding boxes for N independent batches.
    :author 申瑞瑉 (Ruimin Shen)
    :param yx_min1: The top left coordinates (y, x) of the first lists (size [N, M, 2]) of bounding boxes.
    :param yx_max1: The bottom right coordinates (y, x) of the first lists (size [N, M, 2]) of bounding boxes.
    :param yx_min2: The top left coordinates (y, x) of the second lists (size [N, M, 2]) of bounding boxes.
    :param yx_max2: The bottom right coordinates (y, x) of the second lists (size [N, M, 2]) of bounding boxes.
    :return: The lists (size [N, M]) of the IoU.
    """
    yx_min = torch.max(yx_min1, yx_min2)
    yx_max = torch.min(yx_max1, yx_max2)
    size = torch.clamp(yx_max - yx_min, min=0)
    intersect_area = torch.prod(size, -1)
    area1 = torch.prod(yx_max1 - yx_min1, -1)
    area2 = torch.prod(yx_max2 - yx_min2, -1)
    union_area = torch.clamp(area1 + area2 - intersect_area, min=min)
    return intersect_area / union_area 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import finfo [as 別名]
def get_fbank(voice, mfc_obj):
    # Extract log mel-spectrogra
    fbank = mfc_obj.sig2logspec(voice).astype('float32')

    # Mean and variance normalization of each mel-frequency 
    fbank = fbank - fbank.mean(axis=0)
    fbank = fbank / (fbank.std(axis=0)+np.finfo(np.float32).eps)

    # If the duration of a voice recording is less than 10 seconds (1000 frames),
    # repeat the recording until it is longer than 10 seconds and crop.
    full_frame_number = 1000
    init_frame_number = fbank.shape[0]
    while fbank.shape[0] < full_frame_number:
          fbank = np.append(fbank, fbank[0:init_frame_number], axis=0)
          fbank = fbank[0:full_frame_number,:]
    return fbank 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import finfo [as 別名]
def do_precision_lower_bound(self, float_small, float_large):
        eps = np.finfo(float_large).eps

        arr = np.array([1.0], float_small)
        range = np.array([1.0 + eps, 2.0], float_large)

        # test is looking for behavior when the bounds change between dtypes
        if range.astype(float_small)[0] != 1:
            return

        # previously crashed
        count, x_loc = np.histogram(arr, bins=1, range=range)
        assert_equal(count, [1])

        # gh-10322 means that the type comes from arr - this may change
        assert_equal(x_loc.dtype, float_small) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import finfo [as 別名]
def do_precision_upper_bound(self, float_small, float_large):
        eps = np.finfo(float_large).eps

        arr = np.array([1.0], float_small)
        range = np.array([0.0, 1.0 - eps], float_large)

        # test is looking for behavior when the bounds change between dtypes
        if range.astype(float_small)[-1] != 1:
            return

        # previously crashed
        count, x_loc = np.histogram(arr, bins=1, range=range)
        assert_equal(count, [1])

        # gh-10322 means that the type comes from arr - this may change
        assert_equal(x_loc.dtype, float_small)