Add initial vector_mpi.py
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0450811b0c
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363
src/vector_mpi.py
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363
src/vector_mpi.py
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import numpy as np
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from mpi4py import MPI
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class Vector:
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start_idx = 0 # Nullter Eintrag des Vektors auf dem aktuellen Rang
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end_idx = 0 # Letzer Eintrag des Vektors auf dem aktuellen Rang
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rank_size = 0 # Dimension des Vektors der auf dem aktuellen Rang gespeichert wird
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kind = '' # Art des Vektors, Zeilen oder Spaltenvektor
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vec = np.arange(rank_size)
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vshape = np.arange(2) # Array mit Länge 2, um die Shape des Vektors zu speichern
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dim = 0 # Gesamtdimension des Vektors, Länge des Vektors
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comm = MPI.COMM_WORLD
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size = comm.Get_size()
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rank = comm.Get_rank()
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# Konstruktor
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def __init__(self, array):
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if isinstance(array, np.ndarray):
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form = array.shape
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if len(array) < self.size:
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raise ValueError("ERROR_3: Die Dimension des Vektors ist kleiner als die Anzahl der benutzten Ränge.")
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if len(form) > 2:
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raise ValueError("ERROR_2: Falsche Dimension, kann kein 1 x n oder n x 1 Vektor sein.")
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# Array ist Zeilenvektor:
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if len(form) == 1 or (len(form) == 2 and form[0] == 1):
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self.vshape[0] = 1
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self.vshape[1] = len(array) # Shape des Vectors
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self.start_idx = int(self.rank * len(array) / self.size)
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self.end_idx = int(len(array) / self.size + self.rank * len(array) / self.size) - 1
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self.rank_size = (
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self.end_idx - self.start_idx) + 1 # Größe des Teilvektors auf dem akt. Rang: Differenz zw. Start- und Endindex + 1
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self.vec = array[
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self.start_idx: self.end_idx + 1] # Auf jedem Rang werden die Einträge vom Start bis zum Endindex gespeichert
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self.kind = 'row'
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self.dim = len(array)
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# Array ist Spaltenvektor
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if len(form) == 2 and form[1] == 1:
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self.vshape[0] = len(array)
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self.vshape[1] = 1
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self.start_idx = int(self.rank * len(array) / self.size)
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self.end_idx = int(len(array) / self.size + self.rank * len(array) / self.size) - 1
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self.rank_size = (
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self.end_idx - self.start_idx) + 1 # Größe des Teilvektors auf dem akt. Rang: Differenz zw. Start- und Endindex + 1
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self.vec = array[
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self.start_idx: self.end_idx + 1] # Auf jedem Rang werden die Einträge vom Start bis zum Endindex gespeichert
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self.kind = 'column'
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self.dim = len(array)
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elif isinstance(array, list):
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self.vshape[0] = 1
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self.vshape[1] = len(array)
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self.start_idx = int(self.rank * len(array) / self.size)
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self.end_idx = int(len(array) / self.size + self.rank * len(array) / self.size) - 1
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self.rank_size = (self.end_idx - self.start_idx) + 1
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self.vec = np.array(array[self.start_idx:self.end_idx + 1])
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self.kind = 'row'
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self.dim = len(array)
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else:
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raise ValueError(
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"ERROR_1: Die übergebene Variable ist kein Numpy-Array, Keine Initialisierung der Vector-Klasse möglich.")
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def __add__(self, other): # Überschreibung der Addition
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if (isinstance(self, Vector) and isinstance(other, Vector)):
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Add_Vec = Vector(np.arange(self.dim))
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if (self.vshape[0] == other.vshape[0] and self.vshape[1] == other.vshape[1]):
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for i in range(0, self.rank_size):
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Add_Vec.vec[i] = self.vec[i] + other.vec[i]
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else:
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raise ValueError("Die Dimensionen der Vektoren stimmen nicht überein, Addition nicht möglich.")
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elif (isinstance(self, Vector) and isinstance(other, (int, float, complex))):
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Add_Vec = Vector(np.arange(self.dim))
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for i in range(0, self.rank_size):
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Add_Vec.vec[i] = self.vec[i] + other
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elif (isinstance(self, (int, float, complex)) and isinstance(other, Vector)):
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Add_Vec = Vector(np.arange(other.dim))
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for i in range(0, other.rank_size):
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Add_Vec.vec[i] = other.vec[i] + self
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else:
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raise ValueError("Ungeeigneter Datentyp für die Addition mit einem Vektor.")
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return Add_Vec
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def __radd__(self, other): # Überschreibung der Addition eines Vektors von rechts
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Add_Vec = Vector(np.arange(self.dim))
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if (isinstance(self, Vector) and isinstance(other, (int, float, complex))):
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Add_Vec = Vector(np.arange(self.dim))
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for i in range(0, self.rank_size):
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Add_Vec.vec[i] = self.vec[i] + other
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else:
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raise ValueError("Ungeeigneter Datentyp für die Addition mit einem Vektor.")
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return Add_Vec
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def __sub__(self, other): # Überschreibung der Subtraktion
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if (isinstance(self, Vector) and isinstance(other, Vector)):
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Sub_Vec = Vector(np.arange(self.dim))
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if (self.vshape[0] == other.vshape[0] and self.vshape[1] == other.vshape[1]):
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for i in range(0, self.rank_size):
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Sub_Vec.vec[i] = self.vec[i] - other.vec[i]
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else:
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raise ValueError("Die Dimension der Vektoren stimmen nicht überein, Subtraktion nicht möglich.")
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elif (isinstance(self, Vector) and isinstance(other, (int, float, complex))):
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Sub_Vec = Vector(np.arange(self.dim))
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for i in range(0, self.rank_size):
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Sub_Vec.vec[i] = self.vec[i] - other
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elif (isinstance(self, (int, float, complex)) and isinstance(other, Vector)):
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Sub_Vec = Vector(np.arange(self.dim))
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for i in range(0, other.rank_size):
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Sub_Vec.vec[i] = other.vec[i] - self
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else:
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raise ValueError("Ungeeigneter Datentyp für die Subtraktion mit einem Vektor.")
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return Sub_Vec
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def __rsub__(self, other): # Subtraktion einer Zahl von einem Vektor
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Sub_Vec = Vector(np.arange(self.dim))
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if (isinstance(self, Vector) and isinstance(other, (float, int, complex))):
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for i in range(0, self.rank_size):
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Sub_Vec.vec[i] = self.vec[i] - other
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else:
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raise ValueError("Ungeeigneter Datentyp für die Subtraktion von einem Vektor.")
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return Sub_Vec
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def __mul__(self, other): # Überschreibung der Multiplikation
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if (isinstance(self, Vector) and isinstance(other, Vector)):
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Mult_Vec = Vector(np.arange(self.dim))
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if (self.vshape[0] == other.vshape[0] and self.vshape[1] == other.vshape[
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1]): # Elementweise Multiplikation
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for i in range(0, self.rank_size):
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Mult_Vec.vec[i] = self.vec[i] * other.vec[i]
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elif (self.vshape[1] == other.vshape[0] and self.vshape[0] == 1): # Inneres Produkt (Skalarprodukt)
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skal_prod = 0
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for i in range(0, self.rank_size):
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skal_prod = skal_prod + self.vec[i] * other.vec[i]
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return skal_prod
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elif (self.vshape[0] == other.vshape[1] and self.vshape[1] == 1):
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raise ValueError("Kann erst implementiert werden, wenn Matrix-Klasse existiert.")
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else:
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raise ValueError("Die Dimensionen der Vektoren stimmen nicht überein, Multiplikation nicht möglich.")
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elif (isinstance(self, Vector) and isinstance(other, (int, float, complex))):
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Mult_Vec = Vector(np.arange(self.dim))
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for i in range(0, self.rank_size):
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Mult_Vec.vec[i] = self.vec[i] * other
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elif (isinstance(self, (int, float, complex)) and isinstance(other, Vector)):
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Mult_Vec = Vector(np.arange(self.dim))
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for i in range(0, other.rank_size):
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Mult_Vec.vec[i] = other.vec[i] * self
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else:
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raise ValueError("Ungeeigneter Datentyp für die Multiplikation mit einem Vektor.")
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return Mult_Vec
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def __rmul__(self, other): # Rechtsseitige Multiplikation von einer Zahl an einen Vektor
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Mult_Vec = Vector(np.arange(self.dim))
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if (isinstance(self, Vector) and isinstance(other, (int, float, complex))):
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for i in range(0, self.rank_size):
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Mult_Vec.vec[i] = self.vec[i] * other
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else:
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raise ValueError("Ungeeigneter Datentyp für die Multiplikation mit einem Vektor.")
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return Mult_Vec
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def __truediv__(self, other):
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Div_Vec = Vector(np.arange(self.dim, dtype=np.double))
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if (isinstance(self, Vector) and isinstance(other, Vector)):
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if (self.vshape[0] == other.vshape[0] and self.vshape[1] == other.vshape[1]):
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for i in range(0, self.rank_size):
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if (other.vec[i] == 0):
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raise ValueError("Ein Eintrag des Divisor-Vektors ist 0, Divion nicht möglich.")
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Div_Vec.vec[i] = self.vec[i] / other.vec[i]
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else:
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raise ValueError("Die Dimensionen der Vektoren stimmen nicht überein, Division nicht möglich.")
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elif (isinstance(self, (int, float, complex)) and isinstance(other, Vector)):
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for i in range(0, other.rank_size):
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if (other.vec[i] == 0):
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raise ValueError("Ein Eintrag des Divisor-Vektors ist 0, Divion nicht möglich.")
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Div_Vec.vec[i] = self / other.vec[i]
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elif (isinstance(self, Vector) and isinstance(other, (float, int, complex))):
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if (other == 0):
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raise ValueError("Division durch Null ist nicht möglich.")
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else:
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for i in range(0, self.rank_size):
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Div_Vec.vec[i] = self.vec[i] / other
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else:
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raise ValueError("ERROR 11: Ungeeigneter Datentyp für die Division mit einem Vektor.")
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return Div_Vec
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def __rtruediv__(self, other):
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Div_Vec = Vector(np.arange(self.dim, dtype=np.double))
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if (isinstance(self, Vector) and isinstance(other, (float, int, complex))):
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if (other == 0):
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raise ValueError("Division durch Null ist nicht möglich.")
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else:
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for i in range(0, self.rank_size):
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Div_Vec.vec[i] = self.vec[i] / other
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else:
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raise ValueError("ERROR 10: Uneignete Datentyp, um einen Vektor durch diesen zu dividieren")
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return Div_Vec
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def __neg__(self):
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Neg_Vec = -1 * self
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return Neg_Vec
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def shape(self):
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if (self.rank == 0):
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return self.vshape
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def T(self):
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Transpose = self
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if (self.kind == 'row'):
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Transpose.kind = 'column'
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else:
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Transpose.kind = 'row'
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# Tauschen der Dimensionen
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var_shift = self.vshape[0]
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Transpose.vshape[0] = self.vshape[1]
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Transpose.vshape[1] = var_shift
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return Transpose
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def str(self): # Rückgabe des gesamten Vektors als string
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str_rep = ''
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if (self.rank == 0):
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if (self.kind == 'row'):
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str_rep = '[' + ','.join(map(str, self.vec))
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if (self.kind == 'column'):
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str_rep = '[' + '\n'.join(map(str, self.vec))
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if (self.size > 1):
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self.comm.send(str_rep, dest=self.rank + 1)
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elif (self.rank == self.size - 1):
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if (self.kind == 'row'):
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str_rep = self.comm.recv(source=self.rank - 1) + ',' + ','.join(map(str, self.vec))
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if (self.kind == 'column'):
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str_rep = self.comm.recv(source=self.rank - 1) + '\n' + '\n'.join(map(str, self.vec))
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else:
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if (self.kind == 'row'):
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str_rep = self.comm.recv(source=self.rank - 1) + ',' + ','.join(map(str, self.vec))
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if (self.kind == 'column'):
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str_rep = self.comm.recv(source=self.rank - 1) + '\n' + '\n'.join(map(str, self.vec))
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self.comm.send(str_rep, dest=self.rank + 1)
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str_rep = self.comm.bcast(str_rep, root=self.size - 1)
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if (self.rank == 0):
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return str_rep + ']'
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def string(self, limit_entry): # Gibt den Vektor als String zurück bis zum Eintrag limit_entry
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str_rep = ''
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if (limit_entry > self.vec_size):
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raise ValueError("ERROR_4: Die eingegebene Zahl ist größer, als der größte Index des Vectors.")
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# Rank 0
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if (self.rank == 0 and limit_entry <= self.end_idx): # Limit_entry befindet sich im Rang 0
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if (self.kind == 'row'):
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str_rep = '[' + ','.join(map(str, self.vec[:limit_entry]))
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if (self.kind == 'column'):
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str_rep = '[' + '\n'.join(map(str, self.vec[:limit_entry]))
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if (self.size > 1):
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self.comm.send(str_rep, dest=self.rank + 1)
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if (self.rank == 0 and limit_entry > self.end_idx): # Limit_entry befindet sich nicht im Rang 0
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if (self.kind == 'row'):
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str_rep = '[' + ','.join(map(str, self.vec))
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if (self.kind == 'column'):
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str_rep = '[' + '\n'.join(map(str, self.vec))
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if (self.size > 1):
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self.comm.send(str_rep, dest=self.rank + 1)
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# Rank im Intervall [1,size-1]
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if (
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self.rank > 0 and self.rank < self.size - 1 and limit_entry <= self.start_idx): # wenn lim_ent == start_idx, dann wurden bereits alle relevanten Indizes im String gespeichert, da Vector nullinitialisiert ist
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str_rep = self.comm.recv(source=self.rank - 1)
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self.comm.send(str_rep, dest=self.rank + 1)
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if (
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self.rank > 0 and self.rank < self.size - 1 and limit_entry > self.start_idx and limit_entry <= self.end_idx):
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if (self.kind == 'row'):
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str_rep = self.comm.recv(source=self.rank - 1) + ',' + ','.join(
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map(str, self.vec[:(limit_entry - self.start_idx)]))
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if (self.kind == 'column'):
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str_rep = self.comm.recv(source=self.rank - 1) + '\n' + '\n'.join(
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map(str, self.vec[:(limit_entry - self.start_idx)])) + ']'
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self.comm.send(str_rep, dest=self.rank + 1)
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if (self.rank > 0 and self.rank < self.size - 1 and limit_entry > self.end_idx):
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if (self.kind == 'row'):
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str_rep = self.comm.recv(source=self.rank - 1) + ',' + ','.join(map(str, self.vec))
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if (self.kind == 'column'):
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str_rep = self.comm.recv(source=self.rank - 1) + '\n' + '\n'.join(map(str, self.vec))
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self.comm.send(str_rep, dest=self.rank + 1)
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# Rank size-1
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if (self.rank == self.size - 1 and limit_entry <= self.start_idx and self.rank > 1):
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str_rep = self.comm.recv(source=self.rank - 1)
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if (self.rank == self.size - 1 and limit_entry >= self.start_idx and self.rank > 1):
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if (self.kind == 'row'):
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str_rep = self.comm.recv(source=self.rank - 1) + ',' + ','.join(
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map(str, self.vec[:(limit_entry - self.start_idx)]))
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if (self.kind == 'column'):
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str_rep = self.comm.recv(source=self.rank - 1) + '\n' + '\n'.join(
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map(str, self.vec[:(limit_entry - self.start_idx)]))
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str_rep = self.comm.bcast(str_rep, root=self.size - 1)
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if (self.rank == 0):
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return str_rep + ']'
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def norm(self): # Berechnung der 2-Norm / euklidischen Norm
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norm = 0
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sum_of_squares = 0
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if (self.rank == 0):
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for i in range(0, self.rank_size):
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sum_of_squares = sum_of_squares + self.vec[i] ** 2
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if (self.size > 1):
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self.comm.send(sum_of_squares, dest=self.rank + 1)
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elif (self.rank == self.size - 1):
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sum_of_squares = self.comm.recv(source=self.rank - 1)
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for i in range(0, self.rank_size):
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sum_of_squares = sum_of_squares + self.vec[i] ** 2
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else:
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sum_of_squares = self.comm.recv(source=self.rank - 1)
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for i in range(0, self.rank_size):
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sum_of_squares = sum_of_squares + self.vec[i] ** 2
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self.comm.send(sum_of_squares, dest=self.rank + 1)
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sum_of_squares = self.comm.bcast(sum_of_squares, root=self.size - 1)
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norm = np.sqrt(sum_of_squares)
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return norm
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def normalize(self): # Normalisierung eines Vectors
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norm = self.norm()
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if (norm == 0):
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return self
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normalized_vec = self / norm
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return normalized_vec
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# Main-Funktion
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x = Vector(np.arange(10))
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print(x.str(), x.shape())
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print(x.vshape[0], x.vshape[1])
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minus = -1 * x
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_x = -x
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print(_x.str())
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print(minus.str())
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y = Vector(2 * np.arange(10))
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print(y.str())
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z = x - y
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print(z.str())
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ae = x + 5
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print(ae.str())
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o = x * y
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print(o.str())
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a = Vector(np.array([[1], [2]]))
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b = Vector(np.array([1, 2]))
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print(a.shape())
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# c = a * b
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# print(c.vec)
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