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matrix.py
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matrix.py
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from scalar import Scalar
import math
class Matrix:
def __init__(self, rows, cols, initializer = 0.0):
self.rows = rows
self.cols = cols
self.data = [0.0] * (rows * cols)
if callable(initializer):
for r in range(self.rows):
for c in range(self.cols):
self.data[r * self.cols + c] = initializer(r, c)
elif isinstance(initializer, list):
for r in range(self.rows):
for c in range(self.cols):
self.data[r * self.cols + c] = initializer[r][c]
elif isinstance(initializer, float) or isinstance(initializer, int):
self.data = [initializer] * (rows * cols)
elif isinstance(initializer, Scalar):
# For now just allow wrapping in a 1x1, we need to decide copy semantics.
assert rows == 1 and cols == 1
self.data[0] = initializer
def copy(self):
m = Matrix(self.rows, self.cols)
for i in range(len(self.data)):
m.data[i] = self.data[i]
return m
def reshape(self, new_rows, new_cols):
assert self.rows * self.cols == new_rows * new_cols
m = self.copy()
m.rows = new_rows
m.cols = new_cols
return m
def _apply(self, func):
for i in range(len(self.data)):
func(self.data[i])
def apply_copy(self, func):
m = Matrix(self.rows, self.cols)
for i in range(len(self.data)):
m.data[i] = func(self.data[i])
return m
def compare(self, other, tolerance = 0.0):
if self.rows != other.rows or self.cols != other.cols:
return False
for i in range(len(self.data)):
if abs(self.data[i] - other.data[i]) > tolerance:
return False
return True
def euclidean_norm(self):
total = 0
for i in range(len(self.data)):
total += self.data[i]*self.data[i]
return math.sqrt(total)
def gather_rows(self, indices):
m = Matrix(len(indices), self.cols)
for i in range(len(indices)):
for col in range(self.cols):
m[i, col] = self[indices[i], col]
return m
def get(self, row, col):
return self.data[row * self.cols + col]
def matmul(self, other):
assert self.cols == other.rows
m = Matrix(self.rows, other.cols)
for r in range(m.rows):
for c in range(m.cols):
for k in range(self.cols):
m.data[r * m.cols + c] += self.get(r, k) * other.get(k, c)
return m
def reduce_sum(self):
s = 0
for i in range(len(self.data)):
s += self.data[i]
return s
def transpose(self):
m = Matrix(self.cols, self.rows)
for r in range(m.rows):
for c in range(m.cols):
m.data[r * m.cols + c] = self.get(c, r)
return m
def __getitem__(self, key):
if isinstance(key, int):
if self.cols == 1:
return self.get(key, 0)
else:
m = Matrix(1, self.cols)
for i in range(self.cols):
m[0, i] = self.get(key, i)
return m
if isinstance(key, tuple):
assert len(key) == 2, "Must specify 2 arguments for indexing a matrix (%d, %d)" % (self.rows, self.cols)
return self.get(key[0], key[1])
raise TypeError
def __setitem__(self, key, value):
if isinstance(key, int):
if isinstance(value, Matrix):
assert self.cols == value.cols and value.rows == 1
for c in range(value.cols):
self.data[key * self.cols + c] = value[0, c]
else:
assert self.cols == 1, "Must specify 2 arguments for indexing a non column matrix (%d, %d)" % (self.rows, self.cols)
self.data[key * self.cols + 0] = value
elif isinstance(key, tuple):
assert len(key) == 2, "Must specify 2 arguments for indexing a matrix (%d, %d)" % (self.rows, self.cols)
self.data[key[0] * self.cols + key[1]] = value
else:
raise TypeError
def __add__(self, other):
if isinstance(other, Matrix):
assert self.rows == other.rows and self.cols == other.cols
m = Matrix(self.rows, self.cols)
for i in range(len(self.data)):
m.data[i] = self.data[i] + other.data[i]
return m
elif isinstance(other, float) or isinstance(other, int) or isinstance(other, Scalar):
m = Matrix(self.rows, self.cols)
for i in range(len(self.data)):
m.data[i] = self.data[i] + other
return m
def __sub__(self, other):
if isinstance(other, Matrix):
assert self.rows == other.rows and self.cols == other.cols
m = Matrix(self.rows, self.cols)
for i in range(len(self.data)):
m.data[i] = self.data[i] - other.data[i]
return m
elif isinstance(other, float) or isinstance(other, int) or isinstance(other, Scalar):
m = Matrix(self.rows, self.cols)
for i in range(len(self.data)):
m.data[i] = self.data[i] - other
return m
def __mul__(self, other):
m = Matrix(self.rows, self.cols)
if isinstance(other, Matrix):
assert self.rows == other.rows and self.cols == other.cols
for i in range(len(self.data)):
m.data[i] = self.data[i] * other.data[i]
return m
elif isinstance(other, float) or isinstance(other, int):
for i in range(len(self.data)):
m.data[i] = self.data[i] * other
return m
def __rmul__(self, other):
return self.__mul__(other)
def __pow__(self, other):
if isinstance(other, float) or isinstance(other, int):
m = Matrix(self.rows, self.cols)
for i in range(len(self.data)):
m.data[i] = self.data[i] ** other
return m
def __truediv__(self, other):
m = Matrix(self.rows, self.cols)
if isinstance(other, Matrix):
assert self.rows == other.rows and self.cols == other.cols
for i in range(len(self.data)):
m.data[i] = self.data[i] / other.data[i]
return m
elif isinstance(other, float) or isinstance(other, int) or isinstance(other, Scalar):
for i in range(len(self.data)):
m.data[i] = self.data[i] / other
return m
def convert_to_scalar(m):
if isinstance(m, Scalar):
return m
if isinstance(m, Matrix):
return Matrix(m.rows, m.cols, lambda r, c: Scalar(m[r, c]))
if isinstance(m, float) or isinstance(m, int):
return Scalar(m)
return m
def convert_from_scalar(m):
if isinstance(m, Scalar):
return m.value
if isinstance(m, Matrix):
return Matrix(m.rows, m.cols, lambda r, c: m[r,c].value if isinstance(m[r,c], Scalar) else m[r,c])
return m