Tools for mathematical optimization region

Overview

README.md

中文博客主页:https://blog.csdn.net/linjing_zyq

pip install optimtool

1. 无约束优化算法性能对比

前五个参数完全一致,其中第四个参数是绘图接口,默认绘制单个算法的迭代过程;第五个参数是输出函数迭代值接口,默认为不输出。

method:用于传递线搜索方式

  • from optimtool.unconstrain import gradient_descent
方法 函数参数 调用示例
解方程得到精确解法(solve) solve(funcs, args, x_0, draw=True, output_f=False, epsilon=1e-10, k=0) gradient_descent.solve(funcs, args, x_0)
基于Grippo非单调线搜索的梯度下降法 barzilar_borwein(funcs, args, x_0, draw=True, output_f=False, method="grippo", M=20, c1=0.6, beta=0.6, alpha=1, epsilon=1e-10, k=0) gradient_descent.barzilar_borwein(funcs, args, x_0, method="grippo")
基于ZhangHanger非单调线搜索的梯度下降法 barzilar_borwein(funcs, args, x_0, draw=True, output_f=False, method="ZhangHanger", M=20, c1=0.6, beta=0.6, alpha=1, epsilon=1e-10, k=0) gradient_descent.barzilar_borwein(funcs, args, x_0, method="ZhangHanger")
基于最速下降法的梯度下降法 steepest(funcs, args, x_0, draw=True, output_f=False, method="wolfe", epsilon=1e-10, k=0) gradient_descent.steepest(funcs, args, x_0)
  • from optimtool.unconstrain import newton
方法 函数参数 调用示例
经典牛顿法 classic(funcs, args, x_0, draw=True, output_f=False, epsilon=1e-10, k=0) newton.classic(funcs, args, x_0)
基于armijo线搜索方法的修正牛顿法 modified(funcs, args, x_0, draw=True, output_f=False, method="armijo", m=20, epsilon=1e-10, k=0) newton.modified(funcs, args, x_0, method="armijo")
基于goldstein线搜索方法的修正牛顿法 modified(funcs, args, x_0, draw=True, output_f=False, method="goldstein", m=20, epsilon=1e-10, k=0) newton.modified(funcs, args, x_0, method="goldstein")
基于wolfe线搜索方法的修正牛顿法 modified(funcs, args, x_0, draw=True, output_f=False, method="wolfe", m=20, epsilon=1e-10, k=0) newton.modified(funcs, args, x_0, method="wolfe")
基于armijo线搜索方法的非精确牛顿法 CG(funcs, args, x_0, draw=True, output_f=False, method="armijo", epsilon=1e-6, k=0) newton.CG(funcs, args, x_0, method="armijo")
基于goldstein线搜索方法的非精确牛顿法 CG(funcs, args, x_0, draw=True, output_f=False, method="goldstein", epsilon=1e-6, k=0) newton.CG(funcs, args, x_0, method="goldstein")
基于wolfe线搜索方法的非精确牛顿法 CG(funcs, args, x_0, draw=True, output_f=False, method="wolfe", epsilon=1e-6, k=0) newton.CG(funcs, args, x_0, method="wolfe")
  • from optimtool.unconstrain import newton_quasi
方法 函数参数 调用示例
基于BFGS方法更新海瑟矩阵的拟牛顿法 bfgs(funcs, args, x_0, draw=True, output_f=False, method="wolfe", m=20, epsilon=1e-10, k=0) newton_quasi.bfgs(funcs, args, x_0)
基于DFP方法更新海瑟矩阵的拟牛顿法 dfp(funcs, args, x_0, draw=True, output_f=False, method="wolfe", m=20, epsilon=1e-4, k=0) newton_quasi.dfp(funcs, args, x_0)
基于有限内存BFGS方法更新海瑟矩阵的拟牛顿法 L_BFGS(funcs, args, x_0, draw=True, output_f=False, method="wolfe", m=6, epsilon=1e-10, k=0) newton_quasi.L_BFGS(funcs, args, x_0)
  • from optimtool.unconstrain import trust_region
方法 函数参数 调用示例
基于截断共轭梯度法的信赖域算法 steihaug_CG(funcs, args, x_0, draw=True, output_f=False, m=100, r0=1, rmax=2, eta=0.2, p1=0.4, p2=0.6, gamma1=0.5, gamma2=1.5, epsilon=1e-6, k=0) trust_region.steihaug_CG(funcs, args, x_0)
import sympy as sp
import matplotlib.pyplot as plt
import optimtool as oo

f, x1, x2, x3, x4 = sp.symbols("f x1 x2 x3 x4")
f = (x1 - 1)**2 + (x2 - 1)**2 + (x3 - 1)**2 + (x1**2 + x2**2 + x3**2 + x4**2 - 0.25)**2
funcs = sp.Matrix([f])
args = sp.Matrix([x1, x2, x3, x4])
x_0 = (1, 2, 3, 4)

# 无约束优化测试函数性能对比
f_list = []
title = ["gradient_descent_barzilar_borwein", "newton_CG", "newton_quasi_L_BFGS", "trust_region_steihaug_CG"]
colorlist = ["maroon", "teal", "slateblue", "orange"]
_, _, f = oo.unconstrain.gradient_descent.barzilar_borwein(funcs, args, x_0, False, True)
f_list.append(f)
_, _, f = oo.unconstrain.newton.CG(funcs, args, x_0, False, True)
f_list.append(f)
_, _, f = oo.unconstrain.newton_quasi.L_BFGS(funcs, args, x_0, False, True)
f_list.append(f)
_, _, f = oo.unconstrain.trust_region.steihaug_CG(funcs, args, x_0, False, True)
f_list.append(f)

# 绘图
handle = []
for j, z in zip(colorlist, f_list):
    ln, = plt.plot([i for i in range(len(z))], z, c=j, marker='o', linestyle='dashed')
    handle.append(ln)
plt.xlabel("$Iteration \ times \ (k)$")
plt.ylabel("$Objective \ function \ value: \ f(x_k)$")
plt.legend(handle, title)
plt.title("Performance Comparison")
plt.show()

2. 非线性最小二乘问题

  • from optimtool.unconstrain import nonlinear_least_square

method:用于传递线搜索方法

方法 函数参数 调用示例
基于高斯牛顿法的非线性最小二乘问题解法 gauss_newton(funcr, args, x_0, draw=True, output_f=False, method="wolfe", epsilon=1e-10, k=0) nonlinear_least_square.gauss_newton(funcr, args, x_0)
基于levenberg_marquardt的非线性最小二乘问题解法 levenberg_marquardt(funcr, args, x_0, draw=True, output_f=False, m=100, lamk=1, eta=0.2, p1=0.4, p2=0.9, gamma1=0.7, gamma2=1.3, epsilon=1e-10, k=0) nonlinear_least_square.levenberg_marquardt(funcr, args, x_0)
import sympy as sp
import matplotlib.pyplot as plt
import optimtool as oo

r1, r2, x1, x2 = sp.symbols("r1 r2 x1 x2")
r1 = x1**3 - 2*x2**2 - 1
r2 = 2*x1 + x2 - 2
funcr = sp.Matrix([r1, r2])
args = sp.Matrix([x1, x2])
x_0 = (2, 2)

f_list = []
title = ["gauss_newton", "levenberg_marquardt"]
colorlist = ["maroon", "teal"]
_, _, f = oo.unconstrain.nonlinear_least_square.gauss_newton(funcr, args, x_0, False, True) # 第五参数控制输出函数迭代值列表
f_list.append(f)
_, _, f = oo.unconstrain.nonlinear_least_square.levenberg_marquardt(funcr, args, x_0, False, True)
f_list.append(f)

# 绘图
handle = []
for j, z in zip(colorlist, f_list):
    ln, = plt.plot([i for i in range(len(z))], z, c=j, marker='o', linestyle='dashed')
    handle.append(ln)
plt.xlabel("$Iteration \ times \ (k)$")
plt.ylabel("$Objective \ function \ value: \ f(x_k)$")
plt.legend(handle, title)
plt.title("Performance Comparison")
plt.show()

3. 等式约束优化测试

  • from optimtool.constrain import equal

无约束内核默认采用wolfe线搜索方法

方法 函数参数 调用示例
二次罚函数法 penalty_quadratic(funcs, args, cons, x_0, draw=True, output_f=False, method="gradient_descent", sigma=10, p=2, epsilon=1e-4, k=0) equal.penalty_quadratic(funcs, args, cons, x_0)
增广拉格朗日法 lagrange_augmented(funcs, args, cons, x_0, draw=True, output_f=False, method="gradient_descent", lamk=6, sigma=10, p=2, etak=1e-4, epsilon=1e-6, k=0) equal.lagrange_augmented(funcs, args, cons, x_0)
import numpy as np
import sympy as sp
import matplotlib.pyplot as plt
import optimtool as oo

f, x1, x2 = sp.symbols("f x1 x2")
f = x1 + np.sqrt(3) * x2
c1 = x1**2 + x2**2 - 1
funcs = sp.Matrix([f])
cons = sp.Matrix([c1])
args = sp.Matrix([x1, x2])
x_0 = (-1, -1)

f_list = []
title = ["penalty_quadratic", "lagrange_augmented"]
colorlist = ["maroon", "teal"]
_, _, f = oo.constrain.equal.penalty_quadratic(funcs, args, cons, x_0, False, True) # 第四个参数控制单个算法不显示迭代图,第五参数控制输出函数迭代值列表
f_list.append(f)
_, _, f = oo.constrain.equal.lagrange_augmented(funcs, args, cons, x_0, False, True)
f_list.append(f)

# 绘图
handle = []
for j, z in zip(colorlist, f_list):
    ln, = plt.plot([i for i in range(len(z))], z, c=j, marker='o', linestyle='dashed')
    handle.append(ln)
plt.xlabel("$Iteration \ times \ (k)$")
plt.ylabel("$Objective \ function \ value: \ f(x_k)$")
plt.legend(handle, title)
plt.title("Performance Comparison")
plt.show()

4. 不等式约束优化测试

  • from optimtool.constrain import unequal

无约束内核默认采用wolfe线搜索方法

方法 函数参数 调用示例
二次罚函数法 penalty_quadratic(funcs, args, cons, x_0, draw=True, output_f=False, method="gradient_descent", sigma=1, p=0.4, epsilon=1e-10, k=0) unequal.penalty_quadratic(funcs, args, cons, x_0)
内点(分式)罚函数法 penalty_interior_fraction(funcs, args, cons, x_0, draw=True, output_f=False, method="gradient_descent", sigma=12, p=0.6, epsilon=1e-6, k=0) unequal.penalty_interior_fraction(funcs, args, cons, x_0)
拉格朗日法(本质上为不存在等式约束) lagrange_augmented(funcs, args, cons, x_0, draw=True, output_f=False, method="gradient_descent", muk=10, sigma=8, alpha=0.2, beta=0.7, p=2, eta=1e-1, epsilon=1e-4, k=0) unequal.lagrange_augmented(funcs, args, cons, x_0)
import sympy as sp
import matplotlib.pyplot as plt
import optimtool as oo

f, x1, x2 = sp.symbols("f x1 x2")
f = x1**2 + (x2 - 2)**2
c1 = 1 - x1
c2 = 2 - x2
funcs = sp.Matrix([f])
cons = sp.Matrix([c1, c2])
args = sp.Matrix([x1, x2])
x_0 = (2, 3)

f_list = []
title = ["penalty_quadratic", "penalty_interior_fraction"]
colorlist = ["maroon", "teal"]
_, _, f = oo.constrain.unequal.penalty_quadratic(funcs, args, cons, x_0, False, True, method="newton", sigma=10, epsilon=1e-6) # 第四个参数控制单个算法不显示迭代图,第五参数控制输出函数迭代值列表
f_list.append(f)
_, _, f = oo.constrain.unequal.penalty_interior_fraction(funcs, args, cons, x_0, False, True, method="newton")
f_list.append(f)

# 绘图
handle = []
for j, z in zip(colorlist, f_list):
    ln, = plt.plot([i for i in range(len(z))], z, c=j, marker='o', linestyle='dashed')
    handle.append(ln)
plt.xlabel("$Iteration \ times \ (k)$")
plt.ylabel("$Objective \ function \ value: \ f(x_k)$")
plt.legend(handle, title)
plt.title("Performance Comparison")
plt.show()

单独测试拉格朗日方法

# 导入符号运算的包
import sympy as sp

# 导入约束优化
import optimtool as oo

# 构造函数
f1 = sp.symbols("f1")
x1, x2, x3, x4 = sp.symbols("x1 x2 x3 x4")
f1 = x1**2 + x2**2 + 2*x3**3 + x4**2 - 5*x1 - 5*x2 - 21*x3 + 7*x4
c1 = 8 - x1 + x2 - x3 + x4 - x1**2 - x2**2 - x3**2 - x4**2
c2 = 10 + x1 + x4 - x1**2 - 2*x2**2 - x3**2 - 2*x4**2
c3 = 5 - 2*x1 + x2 + x4 - 2*x1**2 - x2**2 - x3**2
cons_unequal1 = sp.Matrix([c1, c2, c3])
funcs1 = sp.Matrix([f1])
args1 = sp.Matrix([x1, x2, x3, x4])
x_1 = (0, 0, 0, 0)

x_0, _, f = oo.constrain.unequal.lagrange_augmented(funcs1, args1, cons_unequal1, x_1, output_f=True, method="trust_region", sigma=1, muk=1, p=1.2)
for i in range(len(x_0)):
     x_0[i] = round(x_0[i], 2)
print("\n最终收敛点:", x_0, "\n目标函数值:", f[-1])

result

最终收敛点: [ 2.5   2.5   1.87 -3.5 ] 
目标函数值: -50.94151192711454

5. 混合等式约束测试

  • from optimtool.constrain import mixequal

无约束内核默认采用wolfe线搜索方法

方法 函数参数 调用示例
二次罚函数法 penalty_quadratic(funcs, args, cons_equal, cons_unequal, x_0, draw=True, output_f=False, method="gradient_descent", sigma=1, p=0.6, epsilon=1e-10, k=0) mixequal.penalty_quadratic(funcs, args, cons_equal, cons_unequal, x_0)
L1罚函数法 penalty_L1(funcs, args, cons_equal, cons_unequal, x_0, draw=True, output_f=False, method="gradient_descent", sigma=1, p=0.6, epsilon=1e-10, k=0) mixequal.penalty_L1(funcs, args, cons_equal, cons_unequal, x_0)
增广拉格朗日函数法 lagrange_augmented(funcs, args, cons_equal, cons_unequal, x_0, draw=True, output_f=False, method="gradient_descent", lamk=6, muk=10, sigma=8, alpha=0.5, beta=0.7, p=2, eta=1e-3, epsilon=1e-4, k=0) mixequal.lagrange_augmented(funcs, args, cons_equal, cons_unequal, x_0)
import sympy as sp
import matplotlib.pyplot as plt
import optimtool as oo

f, x1, x2 = sp.symbols("f x1 x2")
f = (x1 - 2)**2 + (x2 - 1)**2
c1 = x1 - 2*x2
c2 = 0.25*x1**2 - x2**2 - 1
funcs = sp.Matrix([f])
cons_equal = sp.Matrix([c1])
cons_unequal = sp.Matrix([c2])
args = sp.Matrix([x1, x2])
x_0 = (0.5, 1)

f_list = []
title = ["penalty_quadratic", "penalty_L1", "lagrange_augmented"]
colorlist = ["maroon", "teal", "orange"]
_, _, f = oo.constrain.mixequal.penalty_quadratic(funcs, args, cons_equal, cons_unequal, x_0, False, True) # 第四个参数控制单个算法不显示迭代图,第五参数控制输出函数迭代值列表
f_list.append(f)
_, _, f = oo.constrain.mixequal.penalty_L1(funcs, args, cons_equal, cons_unequal, x_0, False, True)
f_list.append(f)
_, _, f = oo.constrain.mixequal.lagrange_augmented(funcs, args, cons_equal, cons_unequal, x_0, False, True)
f_list.append(f)

# 绘图
handle = []
for j, z in zip(colorlist, f_list):
    ln, = plt.plot([i for i in range(len(z))], z, c=j, marker='o', linestyle='dashed')
    handle.append(ln)
plt.xlabel("$Iteration \ times \ (k)$")
plt.ylabel("$Objective \ function \ value: \ f(x_k)$")
plt.legend(handle, title)
plt.title("Performance Comparison")
plt.show()

6. Lasso问题测试

  • from optimtool.example import Lasso
方法 函数参数 调用示例
梯度下降法 gradient_descent(A, b, mu, args, x_0, draw=True, output_f=False, delta=10, alp=1e-3, epsilon=1e-2, k=0) Lasso.gradient_descent(A, b, mu, args, x_0,)
次梯度算法 subgradient(A, b, mu, args, x_0, draw=True, output_f=False, alphak=2e-2, epsilon=1e-3, k=0) Lasso.subgradient(A, b, mu, args, x_0,)
import numpy as np
import sympy as sp
import matplotlib.pyplot as plt
import optimtool as oo

import scipy.sparse as ss
f, A, b, mu = sp.symbols("f A b mu")
x = sp.symbols('x1:9')
m = 4
n = 8
u = (ss.rand(n, 1, 0.1)).toarray()
A = np.random.randn(m, n)
b = A.dot(u)
mu = 1e-2
args = sp.Matrix(x)
x_0 = tuple([1 for i in range(8)])

f_list = []
title = ["gradient_descent", "subgradient"]
colorlist = ["maroon", "teal"]
_, _, f = oo.example.Lasso.gradient_descent(A, b, mu, args, x_0, False, True, epsilon=1e-4)# 第四个参数控制单个算法不显示迭代图,第五参数控制输出函数迭代值列表
f_list.append(f)
_, _, f = oo.example.Lasso.subgradient(A, b, mu, args, x_0, False, True)
f_list.append(f)

# 绘图
handle = []
for j, z in zip(colorlist, f_list):
    ln, = plt.plot([i for i in range(len(z))], z, c=j, marker='o', linestyle='dashed')
    handle.append(ln)
plt.xlabel("$Iteration \ times \ (k)$")
plt.ylabel("$Objective \ function \ value: \ f(x_k)$")
plt.legend(handle, title)
plt.title("Performance Comparison")
plt.show()

7. WanYuan问题测试

  • from optimtool.example import WanYuan
方法 函数参数 调用示例
构造7个残差函数并采用高斯牛顿法 gauss_newton(m, n, a, b, c, x3, y3, x_0, draw=False, eps=1e-10) WanYuan.gauss_newton(1, 2, 0.2, -1.4, 2.2, 2**(1/2), 0, (0, -1, -2.5, -0.5, 2.5, -0.05), draw=True)

问题描述

给定直线方程的斜率($m$)与截距($n$),给定一元二次方程的二次项系数($a$)、一次项系数($b$)、常数($c$),给定一个过定点的圆($x_3$,$y_3$​​),要求这个过定点的圆与直线($x_1$,$y_1$)和抛物线($x_2$,$y_2$)相切的切点以及该圆的圆心($x_0$,$y_0$)。

code

# 导包
import sympy as sp
import matplotlib.pyplot as plt
import optimtool as oo

# 构造数据
m = 1
n = 2
a = 0.2
b = -1.4
c = 2.2
x3 = 2*(1/2)
y3 = 0
x_0 = (0, -1, -2.5, -0.5, 2.5, -0.05)

# 训练
oo.example.WanYuan.gauss_newton(1, 2, 0.2, -1.4, 2.2, 2**(1/2), 0, (0, -1, -2.5, -0.5, 2.5, -0.05), draw=True)
You might also like...
A Python step-by-step primer for Machine Learning and Optimization

early-ML Presentation General Machine Learning tutorials A Python step-by-step primer for Machine Learning and Optimization This github repository gat

Implementation of linesearch Optimization Algorithms in Python

Nonlinear Optimization Algorithms During my time as Scientific Assistant at the Karlsruhe Institute of Technology (Germany) I implemented various Opti

Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning.

Microsoft contributing libraries, tools, recipes, sample codes and workshop contents for machine learning & deep learning.

A single Python file with some tools for visualizing machine learning in the terminal.
A single Python file with some tools for visualizing machine learning in the terminal.

Machine Learning Visualization Tools A single Python file with some tools for visualizing machine learning in the terminal. This demo is composed of t

🔬 A curated list of awesome machine learning strategies & tools in financial market.

🔬 A curated list of awesome machine learning strategies & tools in financial market.

A Tools that help Data Scientists and ML engineers train and deploy ML models.

Domino Research This repo contains projects under active development by the Domino R&D team. We build tools that help Data Scientists and ML engineers

A collection of Scikit-Learn compatible time series transformers and tools.
A collection of Scikit-Learn compatible time series transformers and tools.

tsfeast A collection of Scikit-Learn compatible time series transformers and tools. Installation Create a virtual environment and install: From PyPi p

Tools for Optuna, MLflow and the integration of both.
Tools for Optuna, MLflow and the integration of both.

HPOflow - Sphinx DOC Tools for Optuna, MLflow and the integration of both. Detailed documentation with examples can be found here: Sphinx DOC Table of

ClearML - Auto-Magical Suite of tools to streamline your ML workflow. Experiment Manager, MLOps and Data-Management
ClearML - Auto-Magical Suite of tools to streamline your ML workflow. Experiment Manager, MLOps and Data-Management

ClearML - Auto-Magical Suite of tools to streamline your ML workflow Experiment Manager, MLOps and Data-Management ClearML Formerly known as Allegro T

Comments
  • Minimize the Amount of Guided Packages

    Minimize the Amount of Guided Packages

    Is it necessary to reconstruct the matrix operation system of numpy and the symbolic algebra operation system of sympy in order to reduce the amount of dependent packets in the process of guilding packets.

    opened by zzqwdwd 1
Releases(v1.5)
  • v1.5(Nov 10, 2022)

    This version reduces the memory pressure caused by typing compared to v1.4.

    import optimtool as oo
    x1, x2, x3, x4 = sp.symbols("x1 x2 x3 x4") # Declare symbolic variables
    f = (x1 - 1)**2 + (x2 - 1)**2 + (x3 - 1)**2 + (x1**2 + x2**2 + x3**2 + x4**2 - 0.25)**2
    oo.unconstrain.gradient_descent.barzilar_borwein(f, [x1, x2, x3, x4], (1, 2, 3, 4)) # funcs, args, x_0
    
    Source code(tar.gz)
    Source code(zip)
  • v1.4(Nov 8, 2022)

    import optimtool as oo
    x1, x2, x3, x4 = sp.symbols("x1 x2 x3 x4") # Declare symbolic variables
    f = (x1 - 1)**2 + (x2 - 1)**2 + (x3 - 1)**2 + (x1**2 + x2**2 + x3**2 + x4**2 - 0.25)**2
    oo.unconstrain.gradient_descent.barzilar_borwein(f, [x1, x2, x3, x4], (1, 2, 3, 4)) # funcs, args, x_0
    

    Use FuncArray, ArgArray, PointArray, IterPointType, OutputType in typing, and delete functions/ folder. I use many means to accelerate the method, I can't enumerate them here.

    Source code(tar.gz)
    Source code(zip)
  • v1.3(Apr 25, 2022)

    In v2.3.4, We call a method as follows:

    import optimtool as oo
    x1, x2, x3, x4 = sp.symbols("x1 x2 x3 x4")
    f = (x1 - 1)**2 + (x2 - 1)**2 + (x3 - 1)**2 + (x1**2 + x2**2 + x3**2 + x4**2 - 0.25)**2
    funcs = sp.Matrix([f])
    args = sp.Matrix([x1, x2, x3, x4])
    x_0 = (1, 2, 3, 4)
    oo.unconstrain.gradient_descent.barzilar_borwein(funcs, args, x_0)
    

    But in v2.3.5, We now call a method as follows: (It reduces the trouble of constructing data externally.)

    import optimtool as oo
    x1, x2, x3, x4 = sp.symbols("x1 x2 x3 x4") # Declare symbolic variables
    f = (x1 - 1)**2 + (x2 - 1)**2 + (x3 - 1)**2 + (x1**2 + x2**2 + x3**2 + x4**2 - 0.25)**2
    oo.unconstrain.gradient_descent.barzilar_borwein(f, [x1, x2, x3, x4], (1, 2, 3, 4)) # funcs, args, x_0
    # funcs(args) can be list, tuple, sp.Matrix
    

    Our function parameter input method is similar to matlab, and supports more methods than matlab.

    Source code(tar.gz)
    Source code(zip)
End to End toy example of MLOps

churn_model MLOps Toy Example End to End You might find below links useful Connect VSCode to Git MLFlow Port Heroku App Project Organization ├── LICEN

Ashish Tele 6 Feb 06, 2022
A flexible CTF contest platform for coming PKU GeekGame events

Project Guiding Star: the Backend A flexible CTF contest platform for coming PKU GeekGame events Still in early development Highlights Not configurabl

PKU GeekGame 14 Dec 15, 2022
ml4ir: Machine Learning for Information Retrieval

ml4ir: Machine Learning for Information Retrieval | changelog Quickstart → ml4ir Read the Docs | ml4ir pypi | python ReadMe ml4ir is an open source li

Salesforce 77 Jan 06, 2023
Python library which makes it possible to dynamically mask/anonymize data using JSON string or python dict rules in a PySpark environment.

pyspark-anonymizer Python library which makes it possible to dynamically mask/anonymize data using JSON string or python dict rules in a PySpark envir

6 Jun 30, 2022
PennyLane is a cross-platform Python library for differentiable programming of quantum computers

PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural ne

PennyLaneAI 1.6k Jan 01, 2023
Mixing up the Invariant Information clustering architecture, with self supervised concepts from SimCLR and MoCo approaches

Self Supervised clusterer Combined IIC, and Moco architectures, with some SimCLR notions, to get state of the art unsupervised clustering while retain

Bendidi Ihab 9 Feb 13, 2022
Module is created to build a spam filter using Python and the multinomial Naive Bayes algorithm.

Naive-Bayes Spam Classificator Module is created to build a spam filter using Python and the multinomial Naive Bayes algorithm. Main goal is to code a

Viktoria Maksymiuk 1 Jun 27, 2022
QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.

152 Jan 02, 2023
Management of exclusive GPU access for distributed machine learning workloads

TensorHive is an open source tool for managing computing resources used by multiple users across distributed hosts. It focuses on granting

Paweł Rościszewski 131 Dec 12, 2022
using Machine Learning Algorithm to classification AppleStore application

AppleStore-classification-with-Machine-learning-Algo- using Machine Learning Algorithm to classification AppleStore application. the first step : 1: p

Mohammed Hussien 2 May 02, 2022
A python library for easy manipulation and forecasting of time series.

Time Series Made Easy in Python darts is a python library for easy manipulation and forecasting of time series. It contains a variety of models, from

Unit8 5.2k Jan 04, 2023
Python 3.6+ toolbox for submitting jobs to Slurm

Submit it! What is submitit? Submitit is a lightweight tool for submitting Python functions for computation within a Slurm cluster. It basically wraps

Facebook Incubator 768 Jan 03, 2023
DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning.

DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported ha

Microsoft 1.1k Jan 04, 2023
Optimal Randomized Canonical Correlation Analysis

ORCCA Optimal Randomized Canonical Correlation Analysis This project is for the python version of ORCCA algorithm. It depends on Numpy for matrix calc

Yinsong Wang 1 Nov 21, 2021
Stock Price Prediction Bank Jago Using Facebook Prophet Machine Learning & Python

Stock Price Prediction Bank Jago Using Facebook Prophet Machine Learning & Python Overview Bank Jago has attracted investors' attention since the end

Najibulloh Asror 3 Feb 10, 2022
pure-predict: Machine learning prediction in pure Python

pure-predict speeds up and slims down machine learning prediction applications. It is a foundational tool for serverless inference or small batch prediction with popular machine learning frameworks l

Ibotta 84 Dec 29, 2022
Simple Machine Learning Tool Kit

Getting started smltk (Simple Machine Learning Tool Kit) package is implemented for helping your work during data preparation testing your model The g

Alessandra Bilardi 1 Dec 30, 2021
K-means clustering is a method used for clustering analysis, especially in data mining and statistics.

K Means Algorithm What is K Means This algorithm is an iterative algorithm that partitions the dataset according to their features into K number of pr

1 Nov 01, 2021
Coursera Machine Learning - Python code

Coursera Machine Learning This repository contains python implementations of certain exercises from the course by Andrew Ng. For a number of assignmen

Jordi Warmenhoven 859 Dec 10, 2022
This is my implementation on the K-nearest neighbors algorithm from scratch using Python

K Nearest Neighbors (KNN) algorithm In this Machine Learning world, there are various algorithms designed for classification problems such as Logistic

sonny1902 1 Jan 08, 2022