Custom Vector and Matrix classes in python for machine learning
I am creating a machine learning tool set from scratch in python. I have never done something of this kind and I don't usually use python but I thought it would be good to expand my horizons. I am really looking for aspects of the code that would really hinder performance and things to consider since this will be used for a neural network implementation.
class vector:
def __init__(self, size):
self.elems = [0] * size
self.size = size
def __repr__(self):
return repr(self.elems)
def __mul__(self, other):
if(self.size != other.size):
raise ArithmeticError("vectors of two different lengths")
a = 0
for i in range(self.size):
a += self.elems[i] * other.elems[i]
return a
def set(self, array):
for i in range(self.size):
self.elems[i] = array[i]
self.mag = sum([i**2 for i in self.elems])**.5
def normalize(self):
a = vector(self.size)
a.set([i/self.mag for i in self.elems])
return(a)
class matrix:
def __init__(self, r, c):
self.coloums = [vector(c) for i in range(r)]
self.r = r
self.c = c
def __repr__(self):
return repr(self.coloums)
def __mul__(self, other):
if(type(other) != vector):
raise TypeError("matrices can only be multiplied by vectors")
if(self.c != other.size):
raise ArithmeticError("rows and lengths do not match")
a = vector(self.r)
a.set([(other*self.coloums[i]) for i in range(self.r)])
return a
def set(self, multiarray):
for i in range(self.c):
self.coloums[i].set(multiarray[i])
I am aware this has no way of multiply by a scalar but I have no need for that just yet and it would be pretty trivial to implement.
python performance machine-learning neural-network
asked 7 mins ago
robert gibson
1
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I am creating a machine learning tool set from scratch in python. I have never done something of this kind and I don't usually use python but I thought it would be good to expand my horizons. I am really looking for aspects of the code that would really hinder performance and things to consider since this will be used for a neural network implementation.
class vector:
def __init__(self, size):
self.elems = [0] * size
self.size = size
def __repr__(self):
return repr(self.elems)
def __mul__(self, other):
if(self.size != other.size):
raise ArithmeticError("vectors of two different lengths")
a = 0
for i in range(self.size):
a += self.elems[i] * other.elems[i]
return a
def set(self, array):
for i in range(self.size):
self.elems[i] = array[i]
self.mag = sum([i**2 for i in self.elems])**.5
def normalize(self):
a = vector(self.size)
a.set([i/self.mag for i in self.elems])
return(a)
class matrix:
def __init__(self, r, c):
self.coloums = [vector(c) for i in range(r)]
self.r = r
self.c = c
def __repr__(self):
return repr(self.coloums)
def __mul__(self, other):
if(type(other) != vector):
raise TypeError("matrices can only be multiplied by vectors")
if(self.c != other.size):
raise ArithmeticError("rows and lengths do not match")
a = vector(self.r)
a.set([(other*self.coloums[i]) for i in range(self.r)])
return a
def set(self, multiarray):
for i in range(self.c):
self.coloums[i].set(multiarray[i])
I am aware this has no way of multiply by a scalar but I have no need for that just yet and it would be pretty trivial to implement.
python performance machine-learning neural-network
asked 7 mins ago
robert gibson
1
New contributor
robert gibson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
I am creating a machine learning tool set from scratch in python. I have never done something of this kind and I don't usually use python but I thought it would be good to expand my horizons. I am really looking for aspects of the code that would really hinder performance and things to consider since this will be used for a neural network implementation.
class vector:
def __init__(self, size):
self.elems = [0] * size
self.size = size
def __repr__(self):
return repr(self.elems)
def __mul__(self, other):
if(self.size != other.size):
raise ArithmeticError("vectors of two different lengths")
a = 0
for i in range(self.size):
a += self.elems[i] * other.elems[i]
return a
def set(self, array):
for i in range(self.size):
self.elems[i] = array[i]
self.mag = sum([i**2 for i in self.elems])**.5
def normalize(self):
a = vector(self.size)
a.set([i/self.mag for i in self.elems])
return(a)
class matrix:
def __init__(self, r, c):
self.coloums = [vector(c) for i in range(r)]
self.r = r
self.c = c
def __repr__(self):
return repr(self.coloums)
def __mul__(self, other):
if(type(other) != vector):
raise TypeError("matrices can only be multiplied by vectors")
if(self.c != other.size):
raise ArithmeticError("rows and lengths do not match")
a = vector(self.r)
a.set([(other*self.coloums[i]) for i in range(self.r)])
return a
def set(self, multiarray):
for i in range(self.c):
self.coloums[i].set(multiarray[i])
I am aware this has no way of multiply by a scalar but I have no need for that just yet and it would be pretty trivial to implement.
python performance machine-learning neural-network
asked 7 mins ago
robert gibson
1
New contributor
robert gibson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
I am creating a machine learning tool set from scratch in python. I have never done something of this kind and I don't usually use python but I thought it would be good to expand my horizons. I am really looking for aspects of the code that would really hinder performance and things to consider since this will be used for a neural network implementation.
class vector:
def __init__(self, size):
self.elems = [0] * size
self.size = size
def __repr__(self):
return repr(self.elems)
def __mul__(self, other):
if(self.size != other.size):
raise ArithmeticError("vectors of two different lengths")
a = 0
for i in range(self.size):
a += self.elems[i] * other.elems[i]
return a
def set(self, array):
for i in range(self.size):
self.elems[i] = array[i]
self.mag = sum([i**2 for i in self.elems])**.5
def normalize(self):
a = vector(self.size)
a.set([i/self.mag for i in self.elems])
return(a)
class matrix:
def __init__(self, r, c):
self.coloums = [vector(c) for i in range(r)]
self.r = r
self.c = c
def __repr__(self):
return repr(self.coloums)
def __mul__(self, other):
if(type(other) != vector):
raise TypeError("matrices can only be multiplied by vectors")
if(self.c != other.size):
raise ArithmeticError("rows and lengths do not match")
a = vector(self.r)
a.set([(other*self.coloums[i]) for i in range(self.r)])
return a
def set(self, multiarray):
for i in range(self.c):
self.coloums[i].set(multiarray[i])
I am aware this has no way of multiply by a scalar but I have no need for that just yet and it would be pretty trivial to implement.
python performance machine-learning neural-network
python performance machine-learning neural-network
asked 7 mins ago
robert gibson
1
New contributor
robert gibson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 7 mins ago
robert gibson
1
New contributor
robert gibson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 7 mins ago
robert gibson
1
New contributor
robert gibson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 7 mins ago
robert gibson
1
asked 7 mins ago
robert gibson
1
1
New contributor
robert gibson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
robert gibson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
robert gibson is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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