PYTHON VECTOR 2D CLASS UPDATE

Print('Number of axes:', rank_4_tensor.ndim) Print('Type of every element:', rank_4_tensor.dtype) Tensors and tf.TensorShape objects have convenient properties for accessing these: rank_4_tensor = tf.zeros() Note: Although you may see reference to a 'tensor of two dimensions', a rank-2 tensor does not usually describe a 2D space.

Size: The total number of items in the tensor, the product of the shape vector's elements.

Axis or Dimension: A particular dimension of a tensor.

A scalar has rank 0, a vector has rank 1, a matrix is rank 2.

Shape: The length (number of elements) of each of the axes of a tensor.

Tensors are used in all kinds of operations (ops). Print(a * b, ' ') # element-wise multiplication Print(a + b, ' ') # element-wise addition You can do basic math on tensors, including addition, element-wise multiplication, and matrix multiplication.

Sparse tensors (see SparseTensor below).

Ragged tensors (see RaggedTensor below).

However, there are specialized types of tensors that can handle different shapes: The base tf.Tensor class requires tensors to be 'rectangular'-that is, along each axis, every element is the same size. Tensors often contain floats and ints, but have many other types, including: You can convert a tensor to a NumPy array either using np.array or the tensor.numpy method: np.array(rank_2_tensor) There are many ways you might visualize a tensor with more than two axes. Tensors may have more axes here is a tensor with three axes: # There can be an arbitrary number of Tf.Tensor(, shape=(3,), dtype=float32)ДЄ 'matrix' or 'rank-2' tensor has two axes: # If you want to be specific, you can set the dtype (see below) at creation time A vector has one axis: # Let's make this a float tensor. # This will be an int32 tensor by default see 'dtypes' below.ДЄ 'vector' or 'rank-1' tensor is like a list of values. A scalar contains a single value, and no 'axes'.

PYTHON VECTOR 2D CLASS UPDATE

If you're familiar with NumPy, tensors are (kind of) like np.arrays.ДЄll tensors are immutable like Python numbers and strings: you can never update the contents of a tensor, only create a new one. You can see all supported dtypes at tf.dtypes.DType. Tensors are multi-dimensional arrays with a uniform type (called a dtype). Import unittest import pickle # class UnitTestVec2D(unittest.

y = dict # Unit Testing # if _name_ = '_main_': y) * range)Дef convert_to_basis( self, x_vector, y_vector): Return other *(projected_length_times_other_length /other_length_sqrd) Projected_length_times_other_length = self. Other_length_sqrd = other *other + other *other y - other) **2 def projection( self, other): x))ДЄngle = property(get_angle, _setangle, None, 'gets or sets the angle of a vector')Д¬ross = self. Length = property(get_length, _setlength, None, 'gets or sets the magnitude of the vector') # vectory functions def get_length_sqrd( self): _rxor_ = _xor_ # Unary operations def _neg_( self):

# Bitwise operators def _lshift_( self, other): Return self # Division def _div_( self, other): Return self # Multiplication def _mul_( self, other): Return self # Subtraction def _sub_( self, other): Return self # Addition def _add_( self, other): 'inplace operator' if ( hasattr(other, '_getitem_')): 'Any two-operator operation where the right operand is a Vec2d' if ( hasattr(other, '_getitem_')):

'Any two-operator operation where the left operand is a Vec2d' if isinstance(other, Vec2d): # Generic operator handlers def _o2( self, other, f): If hasattr(other, '_getitem_') and len(other) = 2: # String representaion (for debugging) def _repr_( self): Raise IndeењЋrror( 'Invalid subscript ' + str(key) + ' to Vec2d') '''2d vector class, supports vector and scalar operators, and also provides a bunch of high level functions ''' _slots_ = Дef _init_( self, x_or_pair, y = None): # import operator import math class Vec2d( object):

Python vector 2d class