Construct a StatVector from a VectorN [Double], i.
Construct a StatVector from a VectorN [Double], i.e., VectorD.
the vector to initialize StatVector
Construct a StatVector from two or more values (repeated values Double*).
Construct a StatVector from two or more values (repeated values Double*).
the first value
the second value
the rest of the values (zero or more additional values)
the dimension/size of the vector
whether the estimators are restricted to be unbiased.
Multiply this 'row' vector by matrix m.
Multiply this vector by scalar s.
Multiply this vector by vector b.
Multiply in-place this vector and scalar s.
Multiply in-place this vector and vector b.
Add this vector and scalar s.
Add this vector and scalar s._1 only at position s._2.
the (scalar, position) to add
Add this vector and scalar s.
Add this vector and vector b.
Concatenate this vector and scalar b.
Concatenate this vector and vector b.
Add in-place this vector and scalar s.
Add in-place this vector and vector b.
From this vector subtract scalar s.
From this vector subtract scalar s._1 only at position s._2.
the (scalar, position) to subtract
From this vector subtract scalar s.
From this vector subtract vector b.
From this vector subtract in-place scalar s.
From this vector subtract in-place vector b.
Divide this vector by scalar s.
Divide this vector by vector b (element-by-element).
Divide this vector by vector b (element-by-element).
the vector to divide by
Divide in-place this vector and scalar s.
Divide in-place this vector and vector b.
Create a vector of the form (0, .
Create a vector of the form (0, ... -1, ... 0) where the -1 is at position j.
the position to place the 1
the size of the vector (upper bound = size - 1)
Return the vector that is the element-wise absolute value of this matrix.
Return the vector that is the element-wise absolute value of this matrix.
Compute the k-lag auto-correlation of this vector.
Compute the k-lag auto-correlation of this vector.
the lag parameter
Compute the k-lag auto-covariance of this vector.
Compute the k-lag auto-covariance of this vector.
the lag parameter
Get this vector's entire array.
Get this vector's entire array.
Get this vector's elements within the given range (vector slicing).
Get this vector's elements within the given range (vector slicing).
the given range
Get this vector's element at the i-th index position.
Find the argument maximum of this vector (index of maximum element).
Find the argument maximum of this vector (index of maximum element).
the ending index (exclusive) for the search
Return the argument maximum of this vector (-1 if its not positive).
Return the argument maximum of this vector (-1 if its not positive).
the ending index (exclusive) for the search
Find the argument minimum of this vector (index of minimum element).
Find the argument minimum of this vector (index of minimum element).
the ending index (exclusive) for the search
Return the argument minimum of this vector (-1 if its not negative).
Return the argument minimum of this vector (-1 if its not negative).
the ending index (exclusive) for the search
Determine whether x is contained in this vector.
Determine whether x is contained in this vector.
the element to be checked
Compute Pearson's correlation of this vector with vector y.
Compute Pearson's correlation of this vector with vector y.
the other vector
Count the number of strictly negative entries in this vector.
Count the number of strictly negative entries in this vector.
Count the number of strictly positive entries in this vector.
Count the number of strictly positive entries in this vector.
Compute the covariance of this vector with vector y.
Compute the covariance of this vector with vector y.
the other vector
Cumulate the values of this vector from left to right (e.
Cumulate the values of this vector from left to right (e.g., create a cdf from a pmf). Example: (4, 2, 3, 1) --> (4, 6, 9, 10)
Compute the dot product (or inner product) of this vector with vector b.
Compute the dot product (or inner product) of this vector with vector b.
the other vector
Override equals to determine whether vector this equals vector b.
Override equals to determine whether vector this equals vector b.
the vector to compare with this
Determine whether the predicate pred holds for some element in this vector.
Determine whether the predicate pred holds for some element in this vector.
the predicate to test (e.g., "_ == 5.")
Expand the size (dim) of this vector by 'more' elements.
Expand the size (dim) of this vector by 'more' elements.
Return the index of the first negative element in this vector (-1 otherwise).
Return the index of the first negative element in this vector (-1 otherwise).
the ending index (exclusive) for the search
Return the index of the first positive element in this vector (-1 otherwise).
Return the index of the first positive element in this vector (-1 otherwise).
the ending index (exclusive) for the search
Show the flaw by printing the error message.
Show the flaw by printing the error message.
the method where the error occurred
the error message
Iterate over the vector element by element.
Must also override hashCode to be be compatible with equals.
Must also override hashCode to be be compatible with equals.
Compute the confidence interval half-width for the given confidence level.
Compute the confidence interval half-width for the given confidence level.
the confidence level
Check whether this vector is nonnegative (has no negative elements).
Check whether this vector is nonnegative (has no negative elements).
Find the element with the greatest magnitude in this vector.
Find the element with the greatest magnitude in this vector.
Take the maximum of this vector with vector b (element-by element).
Take the maximum of this vector with vector b (element-by element).
the other vector
Find the maximum element in this vector.
Find the maximum element in this vector.
the ending index (exclusive) for the search
Compute the mean of this vector.
Compute the median (middle value) of this vector.
Compute the median (middle value) of this vector. For odd size, it is the middle element, for even, it is the larger of the two middle elements. FIX: need a more efficient algorithm
Take the minimum of this vector with vector b (element-by element).
Take the minimum of this vector with vector b (element-by element).
the other vector
Find the minimum element in this vector.
Find the minimum element in this vector.
the ending index (exclusive) for the search
Compute the mean square (ms) of this vector.
Compute the Euclidean norm (2-norm) of this vector.
Compute the Euclidean norm (2-norm) of this vector.
Compute the Manhattan norm (1-norm) of this vector.
Compute the Manhattan norm (1-norm) of this vector.
Compute the Euclidean norm (2-norm) squared of this vector.
Compute the Euclidean norm (2-norm) squared of this vector.
Normalize this vector so that it sums to one (like a probability vector).
Normalize this vector so that it sums to one (like a probability vector).
Normalize this vector to have a maximum of one.
Normalize this vector to have a maximum of one.
Normalize this vector so its length is one (unit vector).
Normalize this vector so its length is one (unit vector).
Get the number of samples.
Create a vector of the form (0, .
Create a vector of the form (0, ... 1, ... 0) where the 1 is at position j.
the position to place the 1
the size of the vector (upper bound = size - 1)
Create a ramp-up vector of increasing values: 0, 1, 2, .
Create a ramp-up vector of increasing values: 0, 1, 2, ..., size - 1.
the size of the vector (upper bound = size - 1)
Compute the root mean square (rms) of this vector.
Check whether the other vector is at least as long as this vector.
Check whether the other vector is at least as long as this vector.
the other vector
Select a subset of elements of this vector corresponding to a basis.
Select a subset of elements of this vector corresponding to a basis.
the set of index positions (e.g., 0, 2, 5)
Set each value in this vector to x.
Set the values in this vector to the values in array u.
Set the values in this vector to the values in array u.
the array of values to be assigned
Set the format to the newFormat.
Compute the skewness of this vector.
Compute the skewness of this vector. Negative skewness indicates the distribution is elongated on the left, zero skewness indicates it is symmetric, and positive skewness indicates it is elongated on the right.
http://www.mathworks.com/help/stats/skewness.html
Slice this vector from to end.
Slice this vector from to end.
the start of the slice (included)
the end of the slice (excluded)
Sort this vector in-place in non-decreasing order.
Sort this vector in-place in non-decreasing order.
Return the vector containing the square each element of this vector.
Return the vector containing the square each element of this vector.
Compute the standard deviation of this vector.
Sum the elements of this vector.
Sum the elements of this vector.
Sum the elements of this vector skipping the i-th element.
Sum the elements of this vector skipping the i-th element.
the index of the element to skip
Sum the positive (> 0) elements of this vector.
Sum the positive (> 0) elements of this vector.
Convert this vector to a string.
Convert this vector to a string.
Toggle/flip the bias flag for the estimators.
Compare this vector with vector b.
Return the negative of this vector (unary minus).
Return the negative of this vector (unary minus).
Set this vector's elements over the given range (vector slicing).
Set this vector's elements over the given range (vector slicing).
the given range
the vector to assign
Set this vector's elements over the given range (vector slicing).
Set this vector's elements over the given range (vector slicing).
the given range
the value to assign
Set this vector's element at the i-th index position.
Set this vector's element at the i-th index position.
the given index
the value to assign
Compute the variance of this vector.
Return the vector containing each element of this vector raised to the s-th power.
Return the vector containing each element of this vector raised to the s-th power.
the scalar exponent
Raise each element of this vector to the s-th power.
The StatVector class provides methods for computing common statistics on a data vector. Both maximum likelihood (the default) and unbiased estimators are supported. Unbiased should only be used on sample (not population) data.