finish review of linear algebra

Author: KellyHwong

optimization.py

@@ -0,0 +1,7 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# @Date    : Jul-01-19 16:26
+# @Author  : Your Name (you@example.org)
+# @Link    : http://example.org
+
+import os

probability.py

@@ -0,0 +1,20 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# @Date    : Jul-01-19 12:50
+# @Author  : Your Name (you@example.org)
+# @Link    : http://example.org
+
+import os
+from scipy.integrate import quad
+from numpy import sqrt, pi, exp
+
+
+def gaussian(x, mu, sigma):
+    return 1/sqrt(2*pi*sigma**2)*exp(-((x-mu)/sigma)**2/2)
+
+
+if __name__ == "__main__":
+    mu, sigma = 1, sqrt(2)
+    a, b = 0.5, 2
+    I = quad(gaussian, a, b, args=(mu, sigma))
+    print(I)

project0/debug.py

@@ -0,0 +1,22 @@
+def get_sum_metrics(predictions, metrics=[]):
+    for i in range(3):
+        metrics.append(lambda x: x + i)
+
+    sum_metrics = 0
+    for metric in metrics:
+        sum_metrics += metric(predictions)
+
+    return sum_metrics
+
+
+def main():
+    print(get_sum_metrics(0))  # Should be (0 + 0) + (0 + 1) + (0 + 2) = 3
+    print(get_sum_metrics(1))  # Should be (1 + 0) + (1 + 1) + (1 + 2) = 6
+    print(get_sum_metrics(2))  # Should be (2 + 0) + (2 + 1) + (2 + 2) = 9
+    print(get_sum_metrics(3, [lambda x: x]))  # Should be (3) + (3 + 0) + (3 + 1) + (3 + 2) = 15
+    print(get_sum_metrics(0))  # Should be (0 + 0) + (0 + 1) + (0 + 2) = 3
+    print(get_sum_metrics(1))  # Should be (1 + 0) + (1 + 1) + (1 + 2) = 6
+    print(get_sum_metrics(2))  # Should be (2 + 0) + (2 + 1) + (2 + 2) = 9
+
+if __name__ == "__main__":
+    main()

project0/main.py

@@ -0,0 +1,57 @@
+import numpy as np
+
+def randomization(n):
+    """
+    Arg:
+      n - an integer
+    Returns:
+      A - a randomly-generated nx1 Numpy array.
+    """
+    #Your code here
+    raise NotImplementedError
+
+def operations(h, w):
+    """
+    Takes two inputs, h and w, and makes two Numpy arrays A and B of size
+    h x w, and returns A, B, and s, the sum of A and B.
+
+    Arg:
+      h - an integer describing the height of A and B
+      w - an integer describing the width of A and B
+    Returns (in this order):
+      A - a randomly-generated h x w Numpy array.
+      B - a randomly-generated h x w Numpy array.
+      s - the sum of A and B.
+    """
+    #Your code here
+    raise NotImplementedError
+
+
+def norm(A, B):
+    """
+    Takes two Numpy column arrays, A and B, and returns the L2 norm of their
+    sum.
+
+    Arg:
+      A - a Numpy array
+      B - a Numpy array
+    Returns:
+      s - the L2 norm of A+B.
+    """
+    #Your code here
+    raise NotImplementedError
+
+
+def neural_network(inputs, weights):
+    """
+     Takes an input vector and runs it through a 1-layer neural network
+     with a given weight matrix and returns the output.
+
+     Arg:
+       inputs - 2 x 1 NumPy array
+       weights - 2 x 1 NumPy array
+     Returns (in this order):
+       out - a 1 x 1 NumPy array, representing the output of the neural network
+    """
+    #Your code here
+    raise NotImplementedError

project0/test.py

@@ -0,0 +1,78 @@
+import traceback
+import torch.nn as nn
+
+
+def green(s):
+    return '\033[1;32m%s\033[m' % s
+
+
+def yellow(s):
+    return '\033[1;33m%s\033[m' % s
+
+
+def red(s):
+    return '\033[1;31m%s\033[m' % s
+
+
+def log(*m):
+    print(" ".join(map(str, m)))
+
+
+def log_exit(*m):
+    log(red("ERROR:"), *m)
+    exit(1)
+
+
+def check_numpy():
+    try:
+        import numpy
+        log(green("PASS"), "NumPy installed")
+    except ModuleNotFoundError:
+        log(red("FAIL"), "NumPy not installed")
+
+
+def check_scipy():
+    try:
+        import scipy
+        log(green("PASS"), "SciPy installed")
+    except ModuleNotFoundError:
+        log(red("FAIL"), "SciPy not installed")
+
+
+def check_matplotlib():
+    try:
+        import matplotlib
+        log(green("PASS"), "matplotlib installed")
+    except ModuleNotFoundError:
+        log(red("FAIL"), "matplotlib not installed")
+
+
+def check_torch():
+    try:
+        import torch
+        log(green("PASS"), "PyTorch installed")
+    except ModuleNotFoundError:
+        log(red("FAIL"), "PyTorch not installed")
+
+
+def check_tqdm():
+    try:
+        import tqdm
+        log(green("PASS"), "tqdm installed")
+    except ModuleNotFoundError:
+        log(red("FAIL"), "tqdm not installed")
+
+
+def main():
+    try:
+        check_numpy()
+        check_scipy()
+        check_matplotlib()
+        check_torch()
+        check_tqdm()
+    except Exception:
+        log_exit(traceback.format_exc())
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -1 +1,5 @@
 numpy
+matplotlib
+SciPy
+tqdm
+torch

resources_project0.tar.gz

@@ -0,0 +1,24 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Date    : 2019-06-01 20:13:43
+# @Author  : Your Name (you@example.org)
+# @Link    : http://example.org
+# @Version : $Id$
+
+import os
+
+def sqrt_newton(y):
+    if y < 0:
+        return None
+    err = 1e-7
+    xn = y
+    while abs(y - xn*xn) > err:
+        xn = (y/xn + xn) / 2.0;
+        print(xn)
+    return xn
+
+def main():
+    print(sqrt_newton(2))
+
+if __name__ == '__main__':
+    main()