def euclidean_distance(self, a, b):
"""
Returns a scalar Euclidean Distance value between two points on a 2-D plane
Parameters:
a = Point_1 on the plane
b = Point_2 on the plane
Output:
Scalar Value for Distance between the two points.
"""
a = Tensor(a, name="a")
sq_cal = square_root(((a.sub(b)).pow(Scalar(2))).sum(axis=1))
while sq_cal.status != "computed":
pass
# np.sqrt(sum((a-b)**2), axis = 1)
# c = Scalar(2)
# d = Scalar(4)
# e = c.add(d)
# print("\n\nOutput is : \n\n",e.output, "\n\n Status is : \n\n", e.status)
# a = Tensor([[1]])
# b = [[2.724]]
# print("\n what is a \n", a)
# distance = Tensor(b, name = "d_check")
# inverse_distance = a.div(distance)
# while inverse_distance.status != "computed":
# pass
# print("\n inverse_distance_first created \n", inverse_distance)
return sq_cal
def distribution(self, x, mean, std):
"""
Gaussian Distribution Function
"""
exponent = R.exp(-((x - mean)**2 / (2 * std**2)))
gaussian_func = exponent / (R.square_root(2 * (3.1415) * std))
def pearson_correlation(x, y):
"""
Calculate linear correlation(pearson correlation)
"""
if not isinstance(x, R.Tensor):
x = R.Tensor(x)
if not isinstance(y, R.Tensor):
y = R.Tensor(y)
a = R.sum(R.square(x))
b = R.sum(R.square(y))
n = a.output.shape[0]
return R.div(
R.sub(R.multiply(R.Scalar(n), R.sum(R.multiply(x, y))),
R.multiply(R.sum(x), R.sum(y))),
R.multiply(
R.square_root(R.sub(R.multiply(R.Scalar(n), a), R.square(b))),
R.square_root(R.sub(R.multiply(R.Scalar(n), b), R.square(b)))))
def distribution(self, x, mean, std):
"""
Gaussian Distribution Function
"""
numerator = R.square(x - mean)
denominator = R.Scalar(2) * R.square(std)
frac = R.div(numerator,denominator)
exponent = R.exp(R.Scalar(-1) * frac)
two_pi = R.Scalar(2) * R.pi()
gaussian_denominator = R.square_root(two_pi) * std
gaussian_func = R.div(exponent, gaussian_denominator)
return gaussian_func
def distribution(self, x, mean, std):
"""
Gaussian Distribution Function
exponent = np.exp(-((x-mean)**2 / (2*std**2)))
gauss_func = exponent / (np.sqrt(2*np.pi)*std)
"""
numerator = R.square(x - mean)
denominator = R.Scalar(2) * R.square(std)
frac = R.div(numerator,denominator)
exponent = R.exp(R.Scalar(-1) * frac)
two_pi = R.Scalar(2) * R.Scalar(3.141592653589793)
gaussian_denominator = R.square_root(two_pi) * std
gaussian_func = R.div(exponent, gaussian_denominator)
return gaussian_func
def __euclidean_distance(self, X):
X = R.expand_dims(X, axis=1, name="expand_dims")
return R.square_root(R.sub(X, self._X).pow(Scalar(2)).sum(axis=2))
def closest_centroids(self, points, centroids):
centroids = R.expand_dims(centroids, axis=1)
return R.argmin(
R.square_root(R.sum(R.square(R.sub(points, centroids)), axis=2)))
def closest_centroids(self, centroids):
centroids = R.expand_dims(centroids, axis=1)
return R.argmin(
square_root(
R.sub(self.points, centroids).pow(Scalar(2)).sum(axis=2)))
def __eucledian_distance(self, X):
X = R.expand_dims(X, axis=1, name="expand_dims")
return R.square_root(
R.sub(X, self.X_train).pow(R.Scalar(2)).sum(axis=2))
def eucledian_distance(self, X, Y):
return R.square_root(((R.sub(X, Y)).pow(Scalar(2))).sum(axis=0))
