Lohith-194101034

Phase 3 - 2020 (Summer)/Lohith_194101034/Week-3/Data/ex2data1.txt

@@ -0,0 +1,100 @@
+34.62365962451697,78.0246928153624,0
+30.28671076822607,43.89499752400101,0
+35.84740876993872,72.90219802708364,0
+60.18259938620976,86.30855209546826,1
+79.0327360507101,75.3443764369103,1
+45.08327747668339,56.3163717815305,0
+61.10666453684766,96.51142588489624,1
+75.02474556738889,46.55401354116538,1
+76.09878670226257,87.42056971926803,1
+84.43281996120035,43.53339331072109,1
+95.86155507093572,38.22527805795094,0
+75.01365838958247,30.60326323428011,0
+82.30705337399482,76.48196330235604,1
+69.36458875970939,97.71869196188608,1
+39.53833914367223,76.03681085115882,0
+53.9710521485623,89.20735013750205,1
+69.07014406283025,52.74046973016765,1
+67.94685547711617,46.67857410673128,0
+70.66150955499435,92.92713789364831,1
+76.97878372747498,47.57596364975532,1
+67.37202754570876,42.83843832029179,0
+89.67677575072079,65.79936592745237,1
+50.534788289883,48.85581152764205,0
+34.21206097786789,44.20952859866288,0
+77.9240914545704,68.9723599933059,1
+62.27101367004632,69.95445795447587,1
+80.1901807509566,44.82162893218353,1
+93.114388797442,38.80067033713209,0
+61.83020602312595,50.25610789244621,0
+38.78580379679423,64.99568095539578,0
+61.379289447425,72.80788731317097,1
+85.40451939411645,57.05198397627122,1
+52.10797973193984,63.12762376881715,0
+52.04540476831827,69.43286012045222,1
+40.23689373545111,71.16774802184875,0
+54.63510555424817,52.21388588061123,0
+33.91550010906887,98.86943574220611,0
+64.17698887494485,80.90806058670817,1
+74.78925295941542,41.57341522824434,0
+34.1836400264419,75.2377203360134,0
+83.90239366249155,56.30804621605327,1
+51.54772026906181,46.85629026349976,0
+94.44336776917852,65.56892160559052,1
+82.36875375713919,40.61825515970618,0
+51.04775177128865,45.82270145776001,0
+62.22267576120188,52.06099194836679,0
+77.19303492601364,70.45820000180959,1
+97.77159928000232,86.7278223300282,1
+62.07306379667647,96.76882412413983,1
+91.56497449807442,88.69629254546599,1
+79.94481794066932,74.16311935043758,1
+99.2725269292572,60.99903099844988,1
+90.54671411399852,43.39060180650027,1
+34.52451385320009,60.39634245837173,0
+50.2864961189907,49.80453881323059,0
+49.58667721632031,59.80895099453265,0
+97.64563396007767,68.86157272420604,1
+32.57720016809309,95.59854761387875,0
+74.24869136721598,69.82457122657193,1
+71.79646205863379,78.45356224515052,1
+75.3956114656803,85.75993667331619,1
+35.28611281526193,47.02051394723416,0
+56.25381749711624,39.26147251058019,0
+30.05882244669796,49.59297386723685,0
+44.66826172480893,66.45008614558913,0
+66.56089447242954,41.09209807936973,0
+40.45755098375164,97.53518548909936,1
+49.07256321908844,51.88321182073966,0
+80.27957401466998,92.11606081344084,1
+66.74671856944039,60.99139402740988,1
+32.72283304060323,43.30717306430063,0
+64.0393204150601,78.03168802018232,1
+72.34649422579923,96.22759296761404,1
+60.45788573918959,73.09499809758037,1
+58.84095621726802,75.85844831279042,1
+99.82785779692128,72.36925193383885,1
+47.26426910848174,88.47586499559782,1
+50.45815980285988,75.80985952982456,1
+60.45555629271532,42.50840943572217,0
+82.22666157785568,42.71987853716458,0
+88.9138964166533,69.80378889835472,1
+94.83450672430196,45.69430680250754,1
+67.31925746917527,66.58935317747915,1
+57.23870631569862,59.51428198012956,1
+80.36675600171273,90.96014789746954,1
+68.46852178591112,85.59430710452014,1
+42.0754545384731,78.84478600148043,0
+75.47770200533905,90.42453899753964,1
+78.63542434898018,96.64742716885644,1
+52.34800398794107,60.76950525602592,0
+94.09433112516793,77.15910509073893,1
+90.44855097096364,87.50879176484702,1
+55.48216114069585,35.57070347228866,0
+74.49269241843041,84.84513684930135,1
+89.84580670720979,45.35828361091658,1
+83.48916274498238,48.38028579728175,1
+42.2617008099817,87.10385094025457,1
+99.31500880510394,68.77540947206617,1
+55.34001756003703,64.9319380069486,1
+74.77589300092767,89.52981289513276,1

Phase 3 - 2020 (Summer)/Lohith_194101034/Week-3/Data/ex2data2.txt

@@ -0,0 +1,118 @@
+0.051267,0.69956,1
+-0.092742,0.68494,1
+-0.21371,0.69225,1
+-0.375,0.50219,1
+-0.51325,0.46564,1
+-0.52477,0.2098,1
+-0.39804,0.034357,1
+-0.30588,-0.19225,1
+0.016705,-0.40424,1
+0.13191,-0.51389,1
+0.38537,-0.56506,1
+0.52938,-0.5212,1
+0.63882,-0.24342,1
+0.73675,-0.18494,1
+0.54666,0.48757,1
+0.322,0.5826,1
+0.16647,0.53874,1
+-0.046659,0.81652,1
+-0.17339,0.69956,1
+-0.47869,0.63377,1
+-0.60541,0.59722,1
+-0.62846,0.33406,1
+-0.59389,0.005117,1
+-0.42108,-0.27266,1
+-0.11578,-0.39693,1
+0.20104,-0.60161,1
+0.46601,-0.53582,1
+0.67339,-0.53582,1
+-0.13882,0.54605,1
+-0.29435,0.77997,1
+-0.26555,0.96272,1
+-0.16187,0.8019,1
+-0.17339,0.64839,1
+-0.28283,0.47295,1
+-0.36348,0.31213,1
+-0.30012,0.027047,1
+-0.23675,-0.21418,1
+-0.06394,-0.18494,1
+0.062788,-0.16301,1
+0.22984,-0.41155,1
+0.2932,-0.2288,1
+0.48329,-0.18494,1
+0.64459,-0.14108,1
+0.46025,0.012427,1
+0.6273,0.15863,1
+0.57546,0.26827,1
+0.72523,0.44371,1
+0.22408,0.52412,1
+0.44297,0.67032,1
+0.322,0.69225,1
+0.13767,0.57529,1
+-0.0063364,0.39985,1
+-0.092742,0.55336,1
+-0.20795,0.35599,1
+-0.20795,0.17325,1
+-0.43836,0.21711,1
+-0.21947,-0.016813,1
+-0.13882,-0.27266,1
+0.18376,0.93348,0
+0.22408,0.77997,0
+0.29896,0.61915,0
+0.50634,0.75804,0
+0.61578,0.7288,0
+0.60426,0.59722,0
+0.76555,0.50219,0
+0.92684,0.3633,0
+0.82316,0.27558,0
+0.96141,0.085526,0
+0.93836,0.012427,0
+0.86348,-0.082602,0
+0.89804,-0.20687,0
+0.85196,-0.36769,0
+0.82892,-0.5212,0
+0.79435,-0.55775,0
+0.59274,-0.7405,0
+0.51786,-0.5943,0
+0.46601,-0.41886,0
+0.35081,-0.57968,0
+0.28744,-0.76974,0
+0.085829,-0.75512,0
+0.14919,-0.57968,0
+-0.13306,-0.4481,0
+-0.40956,-0.41155,0
+-0.39228,-0.25804,0
+-0.74366,-0.25804,0
+-0.69758,0.041667,0
+-0.75518,0.2902,0
+-0.69758,0.68494,0
+-0.4038,0.70687,0
+-0.38076,0.91886,0
+-0.50749,0.90424,0
+-0.54781,0.70687,0
+0.10311,0.77997,0
+0.057028,0.91886,0
+-0.10426,0.99196,0
+-0.081221,1.1089,0
+0.28744,1.087,0
+0.39689,0.82383,0
+0.63882,0.88962,0
+0.82316,0.66301,0
+0.67339,0.64108,0
+1.0709,0.10015,0
+-0.046659,-0.57968,0
+-0.23675,-0.63816,0
+-0.15035,-0.36769,0
+-0.49021,-0.3019,0
+-0.46717,-0.13377,0
+-0.28859,-0.060673,0
+-0.61118,-0.067982,0
+-0.66302,-0.21418,0
+-0.59965,-0.41886,0
+-0.72638,-0.082602,0
+-0.83007,0.31213,0
+-0.72062,0.53874,0
+-0.59389,0.49488,0
+-0.48445,0.99927,0
+-0.0063364,0.99927,0
+0.63265,-0.030612,0

Phase 3 - 2020 (Summer)/Lohith_194101034/Week-3/utils.py

@@ -0,0 +1,147 @@
+import sys
+import numpy as np
+from matplotlib import pyplot
+
+sys.path.append('..')
+from submission import SubmissionBase
+
+
+def mapFeature(X1, X2, degree=6):
+    """
+    Maps the two input features to quadratic features used in the regularization exercise.
+
+    Returns a new feature array with more features, comprising of
+    X1, X2, X1.^2, X2.^2, X1*X2, X1*X2.^2, etc..
+
+    Parameters
+    ----------
+    X1 : array_like
+        A vector of shape (m, 1), containing one feature for all examples.
+
+    X2 : array_like
+        A vector of shape (m, 1), containing a second feature for all examples.
+        Inputs X1, X2 must be the same size.
+
+    degree: int, optional
+        The polynomial degree.
+
+    Returns
+    -------
+    : array_like
+        A matrix of of m rows, and columns depend on the degree of polynomial.
+    """
+    if X1.ndim > 0:
+        out = [np.ones(X1.shape[0])]
+    else:
+        out = [np.ones(1)]
+
+    for i in range(1, degree + 1):
+        for j in range(i + 1):
+            out.append((X1 ** (i - j)) * (X2 ** j))
+
+    if X1.ndim > 0:
+        return np.stack(out, axis=1)
+    else:
+        return np.array(out)
+
+
+def plotDecisionBoundary(plotData, theta, X, y):
+    """
+    Plots the data points X and y into a new figure with the decision boundary defined by theta.
+    Plots the data points with * for the positive examples and o for  the negative examples.
+
+    Parameters
+    ----------
+    plotData : func
+        A function reference for plotting the X, y data.
+
+    theta : array_like
+        Parameters for logistic regression. A vector of shape (n+1, ).
+
+    X : array_like
+        The input dataset. X is assumed to be  a either:
+            1) Mx3 matrix, where the first column is an all ones column for the intercept.
+            2) MxN, N>3 matrix, where the first column is all ones.
+
+    y : array_like
+        Vector of data labels of shape (m, ).
+    """
+    # make sure theta is a numpy array
+    theta = np.array(theta)
+
+    # Plot Data (remember first column in X is the intercept)
+    plotData(X[:, 1:3], y)
+
+    if X.shape[1] <= 3:
+        # Only need 2 points to define a line, so choose two endpoints
+        plot_x = np.array([np.min(X[:, 1]) - 2, np.max(X[:, 1]) + 2])
+
+        # Calculate the decision boundary line
+        plot_y = (-1. / theta[2]) * (theta[1] * plot_x + theta[0])
+
+        # Plot, and adjust axes for better viewing
+        pyplot.plot(plot_x, plot_y)
+
+        # Legend, specific for the exercise
+        pyplot.legend(['Admitted', 'Not admitted', 'Decision Boundary'])
+        pyplot.xlim([30, 100])
+        pyplot.ylim([30, 100])
+    else:
+        # Here is the grid range
+        u = np.linspace(-1, 1.5, 50)
+        v = np.linspace(-1, 1.5, 50)
+
+        z = np.zeros((u.size, v.size))
+        # Evaluate z = theta*x over the grid
+        for i, ui in enumerate(u):
+            for j, vj in enumerate(v):
+                z[i, j] = np.dot(mapFeature(ui, vj), theta)
+
+        z = z.T  # important to transpose z before calling contour
+        # print(z)
+
+        # Plot z = 0
+        pyplot.contour(u, v, z, levels=[0], linewidths=2, colors='g')
+        pyplot.contourf(u, v, z, levels=[np.min(z), 0, np.max(z)], cmap='Greens', alpha=0.4)
+
+
+class Grader(SubmissionBase):
+    X = np.stack([np.ones(20),
+                  np.exp(1) * np.sin(np.arange(1, 21)),
+                  np.exp(0.5) * np.cos(np.arange(1, 21))], axis=1)
+
+    y = (np.sin(X[:, 0] + X[:, 1]) > 0).astype(float)
+
+    def __init__(self):
+        part_names = ['Sigmoid Function',
+                      'Logistic Regression Cost',
+                      'Logistic Regression Gradient',
+                      'Predict',
+                      'Regularized Logistic Regression Cost',
+                      'Regularized Logistic Regression Gradient']
+        super().__init__('logistic-regression', part_names)
+
+    def __iter__(self):
+        for part_id in range(1, 7):
+            try:
+                func = self.functions[part_id]
+
+                # Each part has different expected arguments/different function
+                if part_id == 1:
+                    res = func(self.X)
+                elif part_id == 2:
+                    res = func(np.array([0.25, 0.5, -0.5]), self.X, self.y)
+                elif part_id == 3:
+                    J, grad = func(np.array([0.25, 0.5, -0.5]), self.X, self.y)
+                    res = grad
+                elif part_id == 4:
+                    res = func(np.array([0.25, 0.5, -0.5]), self.X)
+                elif part_id == 5:
+                    res = func(np.array([0.25, 0.5, -0.5]), self.X, self.y, 0.1)
+                elif part_id == 6:
+                    res = func(np.array([0.25, 0.5, -0.5]), self.X, self.y, 0.1)[1]
+                else:
+                    raise KeyError
+                yield part_id, res
+            except KeyError:
+                yield part_id, 0