Return evenly spaced values within a given interval.

Values are generated within the half-open interval [start, stop) (in other words, the interval including start but excluding stop).

B.arange(1, 5) # => Tensor[1, 2, 3, 4]

Return evenly spaced values within a given interval.

Values are generated within the half-open interval [start, stop) (in other words, the interval including start but excluding stop).

B.arange(5) # => Tensor[0, 1, 2, 3, 4]

Count number of occurrences of each value in array of non-negative ints.

The number of bins (of size 1) is one larger than the largest value in x. If minlength is specified, there will be at least this number of bins in the output array (though it will be longer if necessary, depending on the contents of x). Each bin gives the number of occurrences of its index value in x. If weights is specified the input array is weighted by it, i.e. if a value n is found at position i, out[n] += weight[i] instead of out[n] += 1.

t = Tensor.random(0...10, [10])
t           # => Tensor([7, 2, 2, 7, 0, 7, 6, 6, 0, 6])
bincount(t) # => Tensor([2, 0, 2, 0, 0, 0, 3, 3, 0, 0])

Count number of occurrences of each value in array of non-negative ints.

The number of bins (of size 1) is one larger than the largest value in x. If minlength is specified, there will be at least this number of bins in the output array (though it will be longer if necessary, depending on the contents of x). Each bin gives the number of occurrences of its index value in x. If weights is specified the input array is weighted by it, i.e. if a value n is found at position i, out[n] += weight[i] instead of out[n] += 1.

t = Tensor.random(0...10, [10])
t           # => Tensor([7, 2, 2, 7, 0, 7, 6, 6, 0, 6])
bincount(t) # => Tensor([2, 0, 2, 0, 0, 0, 3, 3, 0, 0])

Returns a Matrix with the given Tensor set along the diagonal.

TODO Support k offsets

Initializes a Tensor with an uninitialized slice of data.

f = empty(5, dtype: Int32)
f # => Tensor[0, 0, 0, 0, 0]

Initializes a Tensor with an uninitialized slice of data that is the same size as a given Tensor.

t = Tensor.new [1, 2, 3]

f = empty_like(t, dtype: Int32)
f # => Tensor[0, 0, 0]

Return a Matrix with ones on the diagonal and zeros elsewhere.

m = eye(3, dtype: Int32)

m # => [[1, 0, 0], [0, 1, 0], 0, 0, 1]

Initializes a Tensor of the given size and dtype, filled with the given value.

f = full(5, 3, dtype: Int32)
f # => Tensor[3, 3, 3, 3, 3]

Initializes a Tensor filled with the provided value, whose size is inferred from a given Tensor

t = Tensor.new [1, 2, 3]

f = full_like(t, -1, dtype: Int32)
f # => Tensor[-1, -1, -1]

Return numbers spaced evenly on a log scale (a geometric progression). This is similar to #logspace, but with endpoints specified directly. Each output sample is a constant multiple of the previous.

geomspace(1, 1000, 4) # => Tensor[1.0, 10.0, 100.0, 1000.0]

Returns the identify matrix with dimensions m by m

m = identity(3)

m # => [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]

Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [start, stop]. The endpoint of the interval can optionally be excluded.

B.linspace(0, 1, 5) # => Tensor[0.0, 0.25, 0.5, 0.75, 1.0]

B.linspace(0, 1, 5, endpoint: false) # => Tensor[0.0, 0.2, 0.4, 0.6, 0.8]

Return numbers spaced evenly on a log scale. In linear space, the sequence starts at base ** start (base to the power of start) and ends with base ** stop (see endpoint below).

B.logspace(2.0, 3.0, num = 4) # => Tensor[100.0, 215.44346900318845, 464.15888336127773, 1000.0]

Initializes a Tensor of the given size and dtype, filled with ones.

f = ones(5, dtype: Int32)
f # => Tensor[1, 1, 1, 1, 1]

Initializes a Tensor filled with ones, whose size is inferred from a given Tensor

t = Tensor.new [1, 2, 3]

f = ones_like(t, dtype: Int32)
f # => Tensor[1, 1, 1]

Initializes a Tensor of the given size and dtype, filled with zeros.

f = zeros(5, dtype: Int32)
f # => Tensor[0, 0, 0, 0, 0]

Initializes a Tensor filled with zeros, whose size is inferred from a given Tensor

t = Tensor.new [1, 2, 3]

f = zeros_like(t, dtype: Int32)
f # => Tensor[0, 0, 0]