L2 normalize squared
7367
def l2norm_squared(a):
    
    value = 0
    for i in xrange(a.shape[1]):
        value += np.dot(a[:,i],a[:,i])
    return value
648310
def normalized_operator(self):
        """"""
        if self._operator.lower() == "lt" or self._operator == "<":
            return u"LT"
        elif self._operator.lower() == "gt" or self._operator == ">":
            return u"GT"
        elif self._operator.lower() == "lte" or self._operator == "<=":
            return u"LTE"
        elif self._operator.lower() == "gte" or self._operator == ">=":
            return u"GTE"
138937
def normalize(v):
    ''''''
    if 0 == np.linalg.norm(v):
        return v
    return v / np.linalg.norm(v)
689430
def apply_norm(x, norm_type, depth, epsilon, layer_collection=None):
  """"""
  if layer_collection is not None:
    assert norm_type == "layer"
  if norm_type == "layer":
    return layer_norm(
        x, filters=depth, epsilon=epsilon, layer_collection=layer_collection)
  if norm_type == "group":
    return group_norm(x, filters=depth, epsilon=epsilon)
  if norm_type == "batch":
    return layers().BatchNormalization(epsilon=epsilon)(x)
  if norm_type == "noam":
    return noam_norm(x, epsilon)
  if norm_type == "l2":
    return l2_norm(x, filters=depth, epsilon=epsilon)
  if norm_type == "none":
    return x
  raise ValueError("Parameter normalizer_fn must be one of: 'layer', 'batch',"
                   "'noam', 'lr', 'none'.")
618281
def normalize(u):
    '''
    
    '''
    u = np.asarray(u)
    unorm = np.sqrt(np.sum(u**2, axis=0))
    z = np.isclose(unorm, 0)
    c = np.logical_not(z) / (unorm + z)
    return u * c
234359
public void normalize() {
        double lenSqr = x * x + y * y + z * z;
        double err = lenSqr - 1;
        if (err > (2 * DOUBLE_PREC) || err < -(2 * DOUBLE_PREC)) {
            double len = Math.sqrt(lenSqr);
            x /= len;
            y /= len;
            z /= len;
        }
    }
782991
def normalize_UNTL(subject):
    """"""
    subject = subject.strip()
    subject = re.sub(r'[\s]+', ' ', subject)
    return subject
872784
private void normalize(final Element vertex) {
        double nx = vertex.getDouble("nx");
        double ny = vertex.getDouble("ny");
        double nz = vertex.getDouble("nz");
        double n = Math.sqrt(nx * nx + ny * ny + nz * nz);
        if (n < EPSILON) {
            vertex.setDouble("nx", 0);
            vertex.setDouble("ny", 0);
            vertex.setDouble("nz", 0);
        }
        vertex.setDouble("nx", nx / n);
        vertex.setDouble("ny", ny / n);
        vertex.setDouble("nz", nz / n);
    }
474707
def noam_norm(x, epsilon=1.0, name=None):
  """"""
  with tf.name_scope(name, default_name="noam_norm", values=[x]):
    shape = x.get_shape()
    ndims = len(shape)
    return (tf.nn.l2_normalize(x, ndims - 1, epsilon=epsilon) * tf.sqrt(
        to_float(shape[-1])))
177961
def normalize(self):
        """"""
        vlength = self.length()

        #Make sure the length isn't 0
        if vlength > 0:
            self.x /= vlength
            self.y /= vlength
            self.z /= vlength
        else:
            return Vector3(0, 0, 0)
1008793
def normalize_query(query_string,
                    findterms=re.compile(r'"([^"]+)"|(\S+)').findall,
                    normspace=re.compile(r'\s{2,}').sub):
    

    return [normspace(' ', (t[0] or t[1]).strip()) for t in findterms(query_string)]
1050282
function normalize(word) {
        var result = '';
        for (var i = 0; i < word.length; i++) {
            var kana = word[i];
            var target = transform[kana];
            if (target === false) {
                continue;
            }
            if (target) {
                kana = target;
            }
            result += kana;
        }
        return result;
    }
1188331
def _normalize(x, cmin=None, cmax=None, clip=True):
    """"""
    if not isinstance(x, np.ndarray):
        x = np.array(x)
    if cmin is None:
        cmin = x.min()
    if cmax is None:
        cmax = x.max()
    if cmin == cmax:
        return .5 * np.ones(x.shape)
    else:
        cmin, cmax = float(cmin), float(cmax)
        y = (x - cmin) * 1. / (cmax - cmin)
        if clip:
            y = np.clip(y, 0., 1.)
        return y
924829
def normalize(x, min_value, max_value):
    """"""
    x = (x - min_value) / (max_value - min_value)
    return clip(x, 0, 1)
303228
def _norm_default(x):
    """"""
    # Lazy import to improve `import odl` time
    import scipy.linalg

    if _blas_is_applicable(x.data):
        nrm2 = scipy.linalg.blas.get_blas_funcs('nrm2', dtype=x.dtype)
        norm = partial(nrm2, n=native(x.size))
    else:
        norm = np.linalg.norm
    return norm(x.data.ravel())
631798
def normalize(vector):
    '''
    '''
    d = sum(x * x for x in vector) ** 0.5
    return tuple(x / d for x in vector)
541100
def normalize(p):
    
    l = math.sqrt(p[0]**2 + p[1]**2)
    return [0.0, 0.0] if l == 0 else [p[0]/l, p[1]/l]