Python numpy.subtract() Examples

def calWeights(self, img, kernel, y, x):
        wmax = 0
        sweight = 0
        average = 0
        for j in range(2 * self.Ds + 1 - 2 * self.ds - 1):
            for i in range(2 * self.Ds + 1 - 2 * self.ds - 1):
                start_y = y - self.Ds + self.ds + j
                start_x = x - self.Ds + self.ds + i
                neighbour_w = img[start_y - self.ds:start_y + self.ds + 1, start_x - self.ds:start_x + self.ds + 1]
                center_w = img[y-self.ds:y+self.ds+1, x-self.ds:x+self.ds+1]
                if j != y or i != x:
                    sub = np.subtract(neighbour_w, center_w)
                    dist = np.sum(np.multiply(kernel, np.multiply(sub, sub)))
                    w = np.exp(-dist/pow(self.h, 2))    # replaced by look up table
                    if w > wmax:
                        wmax = w
                    sweight = sweight + w
                    average = average + w * img[start_y, start_x]
        return sweight, average, wmax 

def load_image(img_path, net_input_shape):
    imgBGR = cv2.imread(img_path)
    img = cv2.resize(imgBGR, net_input_shape)
    # BGR -> RGB
    #img = img[:,:, (2, 1, 0)]

    ## Method 1
    # imgT = np.transpose(img, (2, 0, 1))  # c,w,h
    # imgF = np.asarray(imgT, dtype=np.float32)
    # mean = [[[88.159309]], [[97.966286]], [[103.66106]]] # Caffe image mean
    # imgS = np.subtract(imgF,mean)

    ## Method 2
    imgF = np.asarray(img, dtype=np.float32)
    mean = [128.0, 128.0, 128.0] # Caffe image mean
    # mean = [88.159309, 97.966286, 103.66106] # Caffe image mean
    imgSS = np.subtract(imgF, mean)/128.0
    imgS = np.transpose(imgSS, (2, 0, 1))  # c,w,h

    # RGB_MEAN_PIXELS = np.array([88.159309, 97.966286, 103.66106]).reshape((1,1,1,3)).astype(np.float32)

    return imgBGR, np.ascontiguousarray(imgS, dtype=np.float32) # avoid error: ndarray is not contiguous 

def load_image(img_path, net_input_shape):
    img = cv2.resize(cv2.imread(img_path), net_input_shape)
    # BGR -> RGB
    #img = img[:,:, (2, 1, 0)]

    ## Method 1
    # imgT = np.transpose(img, (2, 0, 1))  # c,w,h
    # imgF = np.asarray(imgT, dtype=np.float32)
    # mean = [[[88.159309]], [[97.966286]], [[103.66106]]] # Caffe image mean
    # imgS = np.subtract(imgF,mean)

    ## Method 2
    imgF = np.asarray(img, dtype=np.float32)
    mean = [88.159309, 97.966286, 103.66106] # Caffe image mean
    imgSS = np.subtract(imgF, mean)
    imgS = np.transpose(imgSS, (2, 0, 1))  # CHW

    # RGB_MEAN_PIXELS = np.array([88.159309, 97.966286, 103.66106]).reshape((1,1,1,3)).astype(np.float32)

    return np.ascontiguousarray(imgS, dtype=np.float32) # avoid error: ndarray is not contiguous 

def load_image(img_path, net_input_shape):
    imgBGR = cv2.imread(img_path)
    img = cv2.resize(imgBGR, net_input_shape)
    # BGR -> RGB
    #img = img[:,:, (2, 1, 0)]

    ## Method 1
    # imgT = np.transpose(img, (2, 0, 1))  # c,w,h
    # imgF = np.asarray(imgT, dtype=np.float32)
    # mean = [[[88.159309]], [[97.966286]], [[103.66106]]] # Caffe image mean
    # imgS = np.subtract(imgF,mean)

    ## Method 2
    imgF = np.asarray(img, dtype=np.float32)
    mean = [128.0, 128.0, 128.0] # Caffe image mean
    # mean = [88.159309, 97.966286, 103.66106] # Caffe image mean
    imgSS = np.subtract(imgF, mean)/128.0
    imgS = np.transpose(imgSS, (2, 0, 1))  # c,w,h

    # RGB_MEAN_PIXELS = np.array([88.159309, 97.966286, 103.66106]).reshape((1,1,1,3)).astype(np.float32)

    return imgBGR, np.ascontiguousarray(imgS, dtype=np.float32) # avoid error: ndarray is not contiguous 

def load_image(img_path, net_input_shape):
    img = cv2.resize(cv2.imread(img_path), net_input_shape)
    # BGR -> RGB
    #img = img[:,:, (2, 1, 0)]

    ## Method 1
    # imgT = np.transpose(img, (2, 0, 1))  # c,w,h
    # imgF = np.asarray(imgT, dtype=np.float32)
    # mean = [[[88.159309]], [[97.966286]], [[103.66106]]] # Caffe image mean
    # imgS = np.subtract(imgF,mean)

    ## Method 2
    imgF = np.asarray(img, dtype=np.float32)
    mean = [88.159309, 97.966286, 103.66106] # Caffe image mean
    imgSS = np.subtract(imgF, mean)
    imgS = np.transpose(imgSS, (2, 0, 1))  # CHW

    # RGB_MEAN_PIXELS = np.array([88.159309, 97.966286, 103.66106]).reshape((1,1,1,3)).astype(np.float32)

    return np.ascontiguousarray(imgS, dtype=np.float32)   # avoid error: ndarray is not contiguous 

def load_image(img_path, net_input_shape):
    imgBGR = cv2.imread(img_path)
    img = cv2.resize(imgBGR, net_input_shape)
    # BGR -> RGB
    #img = img[:,:, (2, 1, 0)]

    ## Method 1
    # imgT = np.transpose(img, (2, 0, 1))  # c,w,h
    # imgF = np.asarray(imgT, dtype=np.float32)
    # mean = [[[88.159309]], [[97.966286]], [[103.66106]]] # Caffe image mean
    # imgS = np.subtract(imgF,mean)

    ## Method 2
    imgF = np.asarray(img, dtype=np.float32)
    mean = [88.159309, 97.966286, 103.66106] # Caffe image mean
    imgSS = np.subtract(imgF, mean)
    imgS = np.transpose(imgSS, (2, 0, 1))  # c,w,h

    # RGB_MEAN_PIXELS = np.array([88.159309, 97.966286, 103.66106]).reshape((1,1,1,3)).astype(np.float32)

    return imgBGR, np.ascontiguousarray(imgS, dtype=np.float32) # avoid error: ndarray is not contiguous 

def apply_val_transform_image(image,inputRes=None):
    meanval = (104.00699, 116.66877, 122.67892)

    if inputRes is not None:
        image = sm.imresize(image, inputRes)

    image = np.array(image, dtype=np.float32)
    image = np.subtract(image, np.array(meanval, dtype=np.float32))



    if image.ndim == 2:
        image = image[:, :, np.newaxis]

    # swap color axis because
    # numpy image: H x W x C
    # torch image: C X H X W

    image = image.transpose((2, 0, 1))
    image = torch.from_numpy(image)

    return image 

def make_img_gt_pair(self, idx):
        """
        Make the image-ground-truth pair
        """
        img = cv2.imread(os.path.join(self.db_root_dir, self.img_list[idx]))
        if self.labels[idx] is not None:
            label = cv2.imread(os.path.join(self.db_root_dir, self.labels[idx]), 0)
        else:
            gt = np.zeros(img.shape[:-1], dtype=np.uint8)

        if self.inputRes is not None:
            img = imresize(img, self.inputRes)
            if self.labels[idx] is not None:
                label = imresize(label, self.inputRes, interp='nearest')

        img = np.array(img, dtype=np.float32)
        img = np.subtract(img, np.array(self.meanval, dtype=np.float32))

        if self.labels[idx] is not None:
                gt = np.array(label, dtype=np.float32)
                gt = gt/np.max([gt.max(), 1e-8])

        return img, gt 

def VOCap(rec,prec):

    mpre = np.zeros([1,2+len(prec)])
    mpre[0,1:len(prec)+1] = prec
    mrec = np.zeros([1,2+len(rec)])
    mrec[0,1:len(rec)+1] = rec
    mrec[0,len(rec)+1] = 1.0

    for i in range(mpre.size-2,-1,-1):
        mpre[0,i] = max(mpre[0,i],mpre[0,i+1])

    i = np.argwhere( ~np.equal( mrec[0,1:], mrec[0,:mrec.shape[1]-1]) )+1
    i = i.flatten()

    # compute area under the curve
    ap = np.sum( np.multiply( np.subtract( mrec[0,i], mrec[0,i-1]), mpre[0,i] ) )

    return ap 

def test_NotImplemented_not_returned(self):
        # See gh-5964 and gh-2091. Some of these functions are not operator
        # related and were fixed for other reasons in the past.
        binary_funcs = [
            np.power, np.add, np.subtract, np.multiply, np.divide,
            np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
            np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
            np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
            np.logical_and, np.logical_or, np.logical_xor, np.maximum,
            np.minimum, np.mod,
            np.greater, np.greater_equal, np.less, np.less_equal,
            np.equal, np.not_equal]

        a = np.array('1')
        b = 1
        c = np.array([1., 2.])
        for f in binary_funcs:
            assert_raises(TypeError, f, a, b)
            assert_raises(TypeError, f, c, a) 

def test_pi_ops_nat(self):
        idx = PeriodIndex(['2011-01', '2011-02', 'NaT', '2011-04'],
                          freq='M', name='idx')
        expected = PeriodIndex(['2011-03', '2011-04', 'NaT', '2011-06'],
                               freq='M', name='idx')

        self._check(idx, lambda x: x + 2, expected)
        self._check(idx, lambda x: 2 + x, expected)
        self._check(idx, lambda x: np.add(x, 2), expected)

        self._check(idx + 2, lambda x: x - 2, idx)
        self._check(idx + 2, lambda x: np.subtract(x, 2), idx)

        # freq with mult
        idx = PeriodIndex(['2011-01', '2011-02', 'NaT', '2011-04'],
                          freq='2M', name='idx')
        expected = PeriodIndex(['2011-07', '2011-08', 'NaT', '2011-10'],
                               freq='2M', name='idx')

        self._check(idx, lambda x: x + 3, expected)
        self._check(idx, lambda x: 3 + x, expected)
        self._check(idx, lambda x: np.add(x, 3), expected)

        self._check(idx + 3, lambda x: x - 3, idx)
        self._check(idx + 3, lambda x: np.subtract(x, 3), idx) 

def test_pi_ops_array_int(self):

        idx = PeriodIndex(['2011-01', '2011-02', 'NaT', '2011-04'],
                          freq='M', name='idx')
        f = lambda x: x + np.array([1, 2, 3, 4])
        exp = PeriodIndex(['2011-02', '2011-04', 'NaT', '2011-08'],
                          freq='M', name='idx')
        self._check(idx, f, exp)

        f = lambda x: np.add(x, np.array([4, -1, 1, 2]))
        exp = PeriodIndex(['2011-05', '2011-01', 'NaT', '2011-06'],
                          freq='M', name='idx')
        self._check(idx, f, exp)

        f = lambda x: x - np.array([1, 2, 3, 4])
        exp = PeriodIndex(['2010-12', '2010-12', 'NaT', '2010-12'],
                          freq='M', name='idx')
        self._check(idx, f, exp)

        f = lambda x: np.subtract(x, np.array([3, 2, 3, -2]))
        exp = PeriodIndex(['2010-10', '2010-12', 'NaT', '2011-06'],
                          freq='M', name='idx')
        self._check(idx, f, exp) 

def test_mutate_all():
    df = pd.DataFrame({
        'alpha': list('aaabbb'),
        'beta': list('babruq'),
        'theta': list('cdecde'),
        'x': [1, 2, 3, 4, 5, 6],
        'y': [6, 5, 4, 3, 2, 1],
        'z': [7, 9, 11, 8, 10, 12]
    })

    result = (df
              >> group_by('alpha')
              >> select('x', 'y', 'z')
              >> mutate_all((np.add, np.subtract), 10)
              )
    assert 'alpha' in result 

def app_entropy(x, order=2, metric='chebyshev'):
    """Approximate Entropy
    Parameters
    ----------
    x : list or np.array
        One-dimensional time series of shape (n_times)
    order : int (default: 2)
        Embedding dimension.
    metric : str (default: chebyshev)
        Name of the metric function used with
        :class:`~sklearn.neighbors.KDTree`. The list of available
        metric functions is given by: ``KDTree.valid_metrics``.
    Returns
    -------
    ae : float
        Approximate Entropy.
 
    """
    phi = _app_samp_entropy(x, order=order, metric=metric, approximate=True)
    return np.subtract(phi[0], phi[1]) 

def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:  # pragma: no cover
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 

def triplet_loss(anchor, positive, negative, alpha):
    """Calculate the triplet loss according to the FaceNet paper
    
    Args:
      anchor: the embeddings for the anchor images.
      positive: the embeddings for the positive images.
      negative: the embeddings for the negative images.
  
    Returns:
      the triplet loss according to the FaceNet paper as a float tensor.
    """
    with tf.variable_scope('triplet_loss'):
        pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), 1)
        neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), 1)
        
        basic_loss = tf.add(tf.subtract(pos_dist,neg_dist), alpha)
        loss = tf.reduce_mean(tf.maximum(basic_loss, 0.0), 0)
      
    return loss 

def triplet_loss(anchor, positive, negative, alpha):
    """Calculate the triplet loss according to the FaceNet paper
    
    Args:
      anchor: the embeddings for the anchor images.
      positive: the embeddings for the positive images.
      negative: the embeddings for the negative images.
  
    Returns:
      the triplet loss according to the FaceNet paper as a float tensor.
    """
    with tf.variable_scope('triplet_loss'):
        pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), 1)
        neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), 1)
        
        basic_loss = tf.add(tf.subtract(pos_dist,neg_dist), alpha)
        loss = tf.reduce_mean(tf.maximum(basic_loss, 0.0), 0)
      
    return loss 

def triplet_loss(anchor, positive, negative, alpha):
    """Calculate the triplet loss according to the FaceNet paper
    
    Args:
      anchor: the embeddings for the anchor images.
      positive: the embeddings for the positive images.
      negative: the embeddings for the negative images.
  
    Returns:
      the triplet loss according to the FaceNet paper as a float tensor.
    """
    with tf.variable_scope('triplet_loss'):
        pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), 1)
        neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), 1)
        
        basic_loss = tf.add(tf.subtract(pos_dist,neg_dist), alpha)
        loss = tf.reduce_mean(tf.maximum(basic_loss, 0.0), 0)
      
    return loss 

def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 

def update(self, embeddings0, embeddings1, labels):
        embeddings0, embeddings1, labels = map(
            to_numpy, (embeddings0, embeddings1, labels))
        if self.dist_type is 'euclidean':
            diff = np.subtract(embeddings0, embeddings1)
            dists = np.sqrt(np.sum(np.square(diff), 1))
        else:
            dists = 1 - np.sum(np.multiply(embeddings0,
                                           embeddings1),
                               axis=1) / (np.linalg.norm(embeddings0,
                                                         axis=1) * np.linalg.norm(embeddings1,
                                                                                  axis=1))

        self.dists.extend(dists)
        self.issame.extend(labels) 

def __sub__(self, other):
        if np.isscalar(other):
            other = Constant(other, "Constant({})".format(other))
            name = "Subtract({},{})".format(self.name, other.name)
            return BinOp(np.subtract, name)(self, other)
        assert isinstance(other, Node)
        name = "Subtract({},{})".format(self.name, other.name)
        return BinOp(np.subtract, name)(self, other) 

def __rsub__(self, other):
        if not np.isscalar(other):
            raise Exception("Invalid node operation.")
        other = Constant(other, "Constant({})".format(other))
        name = "Subtract({},{})".format(other.name, self.name)
        return BinOp(np.subtract, name)(other, self) 

def entropy_app_entropy(x, order=2, metric="chebyshev"):
    phi = entropy_app_samp_entropy(x, order=order, metric=metric, approximate=True)
    return np.subtract(phi[0], phi[1]) 

def draw(self, *args):
        super(ContourPlot, self).draw(*args)
        data = self.data
        xdim, ydim = data.shape

        # Find the minimum and maximum z values
        zmax = data.max()
        zmin = data.min()
        rgb_scale_factor = 1.0 / (zmax - zmin) * 255
        # Scale the z values into RGB data
        buf = np.array(data, dtype=float, copy=True)
        np.subtract(buf, zmin, out=buf)
        np.multiply(buf, rgb_scale_factor, out=buf)
        # Duplicate into 3 dimensions (RGB) and convert to byte array
        buf = np.asarray(buf, dtype=np.uint8)
        buf = np.expand_dims(buf, axis=2)
        buf = np.concatenate((buf, buf, buf), axis=2)
        buf = np.reshape(buf, (xdim, ydim, 3))

        charbuf = bytearray(np.reshape(buf, (buf.size)))
        self._texture = Texture.create(size=(xdim, ydim), colorfmt='rgb')
        self._texture.blit_buffer(charbuf, colorfmt='rgb', bufferfmt='ubyte')
        image = self._image
        image.texture = self._texture

        x_px = self.x_px()
        y_px = self.y_px()
        bl = x_px(self.xrange[0]), y_px(self.yrange[0])
        tr = x_px(self.xrange[1]), y_px(self.yrange[1])
        image.pos = bl
        w = tr[0] - bl[0]
        h = tr[1] - bl[1]
        image.size = (w, h) 

def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10):
    assert(embeddings1.shape[0] == embeddings2.shape[0])
    assert(embeddings1.shape[1] == embeddings2.shape[1])
    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
    nrof_thresholds = len(thresholds)
    k_fold = LFold(n_splits=nrof_folds, shuffle=False)
    
    val = np.zeros(nrof_folds)
    far = np.zeros(nrof_folds)
    
    diff = np.subtract(embeddings1, embeddings2)
    dist = np.sum(np.square(diff),1)
    indices = np.arange(nrof_pairs)
    
    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
      
        # Find the threshold that gives FAR = far_target
        far_train = np.zeros(nrof_thresholds)
        for threshold_idx, threshold in enumerate(thresholds):
            _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])
        if np.max(far_train)>=far_target:
            f = interpolate.interp1d(far_train, thresholds, kind='slinear')
            threshold = f(far_target)
        else:
            threshold = 0.0
    
        val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])
  
    val_mean = np.mean(val)
    far_mean = np.mean(far)
    val_std = np.std(val)
    return val_mean, val_std, far_mean 

def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10):
    assert(embeddings1.shape[0] == embeddings2.shape[0])
    assert(embeddings1.shape[1] == embeddings2.shape[1])
    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
    nrof_thresholds = len(thresholds)
    k_fold = KFold(n_splits=nrof_folds, shuffle=False)
    
    val = np.zeros(nrof_folds)
    far = np.zeros(nrof_folds)
    
    diff = np.subtract(embeddings1, embeddings2)
    dist = np.sum(np.square(diff),1)
    indices = np.arange(nrof_pairs)
    
    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
      
        # Find the threshold that gives FAR = far_target
        far_train = np.zeros(nrof_thresholds)
        for threshold_idx, threshold in enumerate(thresholds):
            _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])
        if np.max(far_train)>=far_target:
            f = interpolate.interp1d(far_train, thresholds, kind='slinear')
            threshold = f(far_target)
        else:
            threshold = 0.0
    
        val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])
  
    val_mean = np.mean(val)
    far_mean = np.mean(far)
    val_std = np.std(val)
    return val_mean, val_std, far_mean 

def is_same_id(self, source_img, target_img_list):
    source_face = self.get_aligned_face(source_img, True)
    print('source face', source_face.shape)
    target_face_list = []
    pp = 0
    for img in target_img_list:
      target_force = False
      if pp==len(target_img_list)-1 and len(target_face_list)==0:
        target_force = True
      target_face = self.get_aligned_face(img, target_force)
      if target_face is not None:
        target_face_list.append(target_face)
      pp+=1
    print('target face', len(target_face_list)) 
    source_feature = self.get_feature(source_face, True)
    target_feature = None
    for target_face in target_face_list:
      _feature = self.get_feature(target_face, False)
      if target_feature is None:
        target_feature = _feature
      else:
        target_feature += _feature
    target_feature = sklearn.preprocessing.normalize(target_feature)
    #sim = np.dot(source_feature, target_feature.T)
    diff = np.subtract(source_feature, target_feature)
    dist = np.sum(np.square(diff),1)
    print('dist', dist)
    #print(sim, dist)
    if dist<=self.threshold:
      return True
    else:
      return False 

def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10):
    assert(embeddings1.shape[0] == embeddings2.shape[0])
    assert(embeddings1.shape[1] == embeddings2.shape[1])
    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
    nrof_thresholds = len(thresholds)
    k_fold = LFold(n_splits=nrof_folds, shuffle=False)
    
    val = np.zeros(nrof_folds)
    far = np.zeros(nrof_folds)
    
    diff = np.subtract(embeddings1, embeddings2)
    dist = np.sum(np.square(diff),1)
    indices = np.arange(nrof_pairs)
    
    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
      
        # Find the threshold that gives FAR = far_target
        far_train = np.zeros(nrof_thresholds)
        for threshold_idx, threshold in enumerate(thresholds):
            _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])
        if np.max(far_train)>=far_target:
            f = interpolate.interp1d(far_train, thresholds, kind='slinear')
            threshold = f(far_target)
        else:
            threshold = 0.0
    
        val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])
  
    val_mean = np.mean(val)
    far_mean = np.mean(far)
    val_std = np.std(val)
    return val_mean, val_std, far_mean 

def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, far_target, nrof_folds=10):
    assert(embeddings1.shape[0] == embeddings2.shape[0])
    assert(embeddings1.shape[1] == embeddings2.shape[1])
    nrof_pairs = min(len(actual_issame), embeddings1.shape[0])
    nrof_thresholds = len(thresholds)
    k_fold = KFold(n_splits=nrof_folds, shuffle=False)
    
    val = np.zeros(nrof_folds)
    far = np.zeros(nrof_folds)
    
    diff = np.subtract(embeddings1, embeddings2)
    dist = np.sum(np.square(diff),1)
    indices = np.arange(nrof_pairs)
    
    for fold_idx, (train_set, test_set) in enumerate(k_fold.split(indices)):
      
        # Find the threshold that gives FAR = far_target
        far_train = np.zeros(nrof_thresholds)
        for threshold_idx, threshold in enumerate(thresholds):
            _, far_train[threshold_idx] = calculate_val_far(threshold, dist[train_set], actual_issame[train_set])
        if np.max(far_train)>=far_target:
            f = interpolate.interp1d(far_train, thresholds, kind='slinear')
            threshold = f(far_target)
        else:
            threshold = 0.0
    
        val[fold_idx], far[fold_idx] = calculate_val_far(threshold, dist[test_set], actual_issame[test_set])
  
    val_mean = np.mean(val)
    far_mean = np.mean(far)
    val_std = np.std(val)
    return val_mean, val_std, far_mean 

def mse(true_path,pred_path):
    true_data,_ = librosa.load(true_path)
    true_stft = utils.stft(true_data)
    pred_data,_ = librosa.load(pred_path)
    pred_stft = utils.stft(pred_data)
    return np.mean(np.square(np.subtract(true_stft,pred_stft)))