Vectorkit is part of a series of python implementations of mathematical concepts of AI from the scratch. It is a pet project. Even though it gets packaged to pypi, it is a work in progress, will undergo a lot of changes, and not (conciously) optimized for large scale computation. You can play with its javascript counterpart in the browser at https://vectorkitweb.netlify.com/.
components - a list of the components of a vector
dimensions - the dimension of the vector, or count of its components
min - the minimum component
max - the maximum component
sum - the sum of the components of a vector
memsize - the size of a vector in memory
add - Adds two vectors
append - Appends new components to a vector
concat - Merges two vectors into a single vector
corr - Returns the correlation of two vectors
cosinesim - Returns the cosine similarity between two vectors
cost - Computes the squared error cost function, under the assumption that one vector is the ground truth and the other is the compared.
cov - Returns the covariance between two vectors
crossmul - Returns the cross product of two 3-dimensional vectors
describe - Returns a description of a vector, including its dimensions and memory size
distance - Returns the euclidean distance between two vectors
dotmul - Returns the dot product between two vectors
ediv - Returns the element-wise quotient of two vectors
emul - Returns the element-wise product of two vectors
insert - Inserts a new component at a specified index
jaccard - Returns the jaccard similarity between two vectors
leakyrelu - Passes vector through the leaky version of Rectified Linear Unit
leastdev - Returns the Least Absolute Deviations(L1 Norm) between to vectors
leastsq - Returns the Least Squares(L2 Norm) of two vectors
mae - Returns the mean absolute error between two vectors
mbe - Returns the Mean Bias Error between two vectors
magnitude - Returns the magnitude of a vector
mean - Returns the mean of the components of a vector
minmax - Returns a variant of a vector which has been normalized using standard min-max feature scaling
minmaxmean - Returns a variant of a vector which has been normalized using standard mean and min-max feature scaling
mse - Returns the mean square error of two vectors
pad - Appends zeroes to vectors to a specified length, in-place
padded - Returns a new vector with zero appended to it to a specified length
pararelu - Passes vector through the parametric version of Rectified Linear Unit
pop - Removes a component at a specified location
relu - Passes a vector through a Rectified Linear Unit function and returns a new vector
reverse - Reverses the direction of a vector in-place
reversed - Returns a variant of a vector with reversed direction
rmse - Returns the root mean square error between two vectors
rsquare - Calculates the R square error between two vectors
sdiv - Returns a new vector, which is the quotient from a scalar division of a vector
shuffle - Shuffles vector components in place
shuffled - Returns a new vector with shuffled version of a vector's components
sigmoid - Passes a vector through a logistic sigmoid function and returns a new vector
softmax - Passes a vector through a softmax function and returns a new vector
smul - Returns a new vector, which is the product from a scalar multiplication of a vector
std - Returns the standard deviation of the components of a vector
stdnorm - Returns a variant of a vector which has been normalized using the z-score
subtract - Returns a new vector, which is the result of the subtraction of one vector from another
subvec - Returns a new vector which is a slice from the original vector
tanh - Passes a vector through a TanH function and returns a new vector
to_list - Returns a list of the components of a vector
to_tuple - Returns a tuple of the components of a vector
unitvec - Returns a new vector which has been scaled to unit length
vector_eq - Returns the vector equation of a line between two vectors
isovector - Returns a vector of a specified length containing the same component throughout
randvec - Generates a random vector of specified length
New Methods/Changes (yet to be rolled out in new version) -----------
Replacing normalized with normalize
normalize - Returns a variant of a vector normalized by one of Z-score, Min Max, or Mean Max feature scalings.
pow - raise components of a vector to a given power
join - concatenate components of a vector into a string
flatten - Converts a sequence of any of list, tuple, set, int, float into a Vector
sum - Performs vector addition on a sequence of vectors
New methods: cosinesim, cost, mbe, emul, ediv
Fixes: Dimensionality Checks