Merge pull request #1 from cyanaspect/master

swapped out pyplot for plt for all exercises

Author: MitchMG2

Exercise1/exercise1.ipynb

@@ -30,7 +30,7 @@
     "import numpy as np\n",
     "\n",
     "# Plotting library\n",
-    "from matplotlib import pyplot\n",
+    "from matplotlib import pyplot as plt\n",
     "from mpl_toolkits.mplot3d import Axes3D  # needed to plot 3-D surfaces\n",
     "\n",
     "# library written for this exercise providing additional functions for assignment submission, and others\n",
@@ -220,17 +220,16 @@
     "\n",
     "In this course, we will be exclusively using `matplotlib` to do all our plotting. `matplotlib` is one of the most popular scientific plotting libraries in python and has extensive tools and functions to make beautiful plots. `pyplot` is a module within `matplotlib` which provides a simplified interface to `matplotlib`'s most common plotting tasks, mimicking MATLAB's plotting interface.\n",
     "\n",
-    "<div class=\"alert alert-block alert-warning\">\n",
-    "You might have noticed that we have imported the `pyplot` module at the beginning of this exercise using the command `from matplotlib import pyplot`. This is rather uncommon, and if you look at python code elsewhere or in the `matplotlib` tutorials, you will see that the module is named `plt`. This is used by module renaming by using the import command `import matplotlib.pyplot as plt`. We will not using the short name of `pyplot` module in this class exercises, but you should be aware of this deviation from norm.\n",
-    "</div>\n",
+    "\n",
+    "Also, we have imported the `pyplot` module at the beginning of this exercise using the command `from matplotlib import pyplot as plt`. \"plt\" is a commonly used abbreviation of the pyplot module.\n",
     "\n",
     "\n",
     "In the following part, your first job is to complete the `plotData` function below. Modify the function and fill in the following code:\n",
     "\n",
     "```python\n",
-    "    pyplot.plot(x, y, 'ro', ms=10, mec='k')\n",
-    "    pyplot.ylabel('Profit in $10,000')\n",
-    "    pyplot.xlabel('Population of City in 10,000s')\n",
+    "    plt.plot(x, y, 'ro', ms=10, mec='k')\n",
+    "    plt.ylabel('Profit in $10,000')\n",
+    "    plt.xlabel('Population of City in 10,000s')\n",
     "```"
    ]
   },
@@ -267,7 +266,7 @@
     "    using plot(..., 'ro', ms=10), where `ms` refers to marker size. You \n",
     "    can also set the marker edge color using the `mec` property.\n",
     "    \"\"\"\n",
-    "    fig = pyplot.figure()  # open a new figure\n",
+    "    fig = plt.figure()  # open a new figure\n",
     "    \n",
     "    # ====================== YOUR CODE HERE ======================= \n",
     "    \n",
@@ -299,7 +298,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To quickly learn more about the `matplotlib` plot function and what arguments you can provide to it, you can type `?pyplot.plot` in a cell within the jupyter notebook. This opens a separate page showing the documentation for the requested function. You can also search online for plotting documentation. \n",
+    "To quickly learn more about the `matplotlib` plot function and what arguments you can provide to it, you can type `?plt.plot` in a cell within the jupyter notebook. This opens a separate page showing the documentation for the requested function. You can also search online for plotting documentation. \n",
     "\n",
     "To set the markers to red circles, we used the option `'or'` within the `plot` function."
    ]
@@ -310,7 +309,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "?pyplot.plot"
+    "?plt.plot"
    ]
   },
   {
@@ -599,8 +598,8 @@
    "source": [
     "# plot the linear fit\n",
     "plotData(X[:, 1], y)\n",
-    "pyplot.plot(X[:, 1], np.dot(X, theta), '-')\n",
-    "pyplot.legend(['Training data', 'Linear regression']);"
+    "plt.plot(X[:, 1], np.dot(X, theta), '-')\n",
+    "plt.legend(['Training data', 'Linear regression']);"
    ]
   },
   {
@@ -687,21 +686,21 @@
     "J_vals = J_vals.T\n",
     "\n",
     "# surface plot\n",
-    "fig = pyplot.figure(figsize=(12, 5))\n",
+    "fig = plt.figure(figsize=(12, 5))\n",
     "ax = fig.add_subplot(121, projection='3d')\n",
     "ax.plot_surface(theta0_vals, theta1_vals, J_vals, cmap='viridis')\n",
-    "pyplot.xlabel('theta0')\n",
-    "pyplot.ylabel('theta1')\n",
-    "pyplot.title('Surface')\n",
+    "plt.xlabel('theta0')\n",
+    "plt.ylabel('theta1')\n",
+    "plt.title('Surface')\n",
     "\n",
     "# contour plot\n",
     "# Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100\n",
-    "ax = pyplot.subplot(122)\n",
-    "pyplot.contour(theta0_vals, theta1_vals, J_vals, linewidths=2, cmap='viridis', levels=np.logspace(-2, 3, 20))\n",
-    "pyplot.xlabel('theta0')\n",
-    "pyplot.ylabel('theta1')\n",
-    "pyplot.plot(theta[0], theta[1], 'ro', ms=10, lw=2)\n",
-    "pyplot.title('Contour, showing minimum')\n",
+    "ax = plt.subplot(122)\n",
+    "plt.contour(theta0_vals, theta1_vals, J_vals, linewidths=2, cmap='viridis', levels=np.logspace(-2, 3, 20))\n",
+    "plt.xlabel('theta0')\n",
+    "plt.ylabel('theta1')\n",
+    "plt.plot(theta[0], theta[1], 'ro', ms=10, lw=2)\n",
+    "plt.title('Contour, showing minimum')\n",
     "pass"
    ]
   },
@@ -1082,7 +1081,7 @@
     "</div>\n",
     "\n",
     "<div class=\"alert alert-block alert-warning\">\n",
-    "**MATPLOTLIB tip:** To compare how different learning learning rates affect convergence, it is helpful to plot $J$ for several learning rates on the same figure. This can be done by making `alpha` a python list, and looping across the values within this list, and calling the plot function in every iteration of the loop. It is also useful to have a legend to distinguish the different lines within the plot. Search online for `pyplot.legend` for help on showing legends in `matplotlib`.\n",
+    "**MATPLOTLIB tip:** To compare how different learning learning rates affect convergence, it is helpful to plot $J$ for several learning rates on the same figure. This can be done by making `alpha` a python list, and looping across the values within this list, and calling the plot function in every iteration of the loop. It is also useful to have a legend to distinguish the different lines within the plot. Search online for `plt.legend` for help on showing legends in `matplotlib`.\n",
     "</div>\n",
     "\n",
     "Notice the changes in the convergence curves as the learning rate changes. With a small learning rate, you should find that gradient descent takes a very long time to converge to the optimal value. Conversely, with a large learning rate, gradient descent might not converge or might even diverge!\n",
@@ -1127,9 +1126,9 @@
     "theta, J_history = gradientDescentMulti(X, y, theta, alpha, num_iters)\n",
     "\n",
     "# Plot the convergence graph\n",
-    "pyplot.plot(np.arange(len(J_history)), J_history, lw=2)\n",
-    "pyplot.xlabel('Number of iterations')\n",
-    "pyplot.ylabel('Cost J')\n",
+    "plt.plot(np.arange(len(J_history)), J_history, lw=2)\n",
+    "plt.xlabel('Number of iterations')\n",
+    "plt.ylabel('Cost J')\n",
     "\n",
     "# Display the gradient descent's result\n",
     "print('theta computed from gradient descent: {:s}'.format(str(theta)))\n",
@@ -1299,7 +1298,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

Exercise2/exercise2.ipynb

@@ -30,7 +30,7 @@
     "import numpy as np\n",
     "\n",
     "# Plotting library\n",
-    "from matplotlib import pyplot\n",
+    "from matplotlib import pyplot as plt\n",
     "\n",
     "# Optimization module in scipy\n",
     "from scipy import optimize\n",
@@ -118,8 +118,8 @@
     "neg = y == 0\n",
     "\n",
     "# Plot Examples\n",
-    "pyplot.plot(X[pos, 0], X[pos, 1], 'k*', lw=2, ms=10)\n",
-    "pyplot.plot(X[neg, 0], X[neg, 1], 'ko', mfc='y', ms=8, mec='k', mew=1)\n",
+    "plt.plot(X[pos, 0], X[pos, 1], 'k*', lw=2, ms=10)\n",
+    "plt.plot(X[neg, 0], X[neg, 1], 'ko', mfc='y', ms=8, mec='k', mew=1)\n",
     "```"
    ]
   },
@@ -148,7 +148,7 @@
     "    option 'k*' for the positive examples and 'ko' for the negative examples.    \n",
     "    \"\"\"\n",
     "    # Create New Figure\n",
-    "    fig = pyplot.figure()\n",
+    "    fig = plt.figure()\n",
     "\n",
     "    # ====================== YOUR CODE HERE ======================\n",
     "\n",
@@ -171,9 +171,9 @@
    "source": [
     "plotData(X, y)\n",
     "# add axes labels\n",
-    "pyplot.xlabel('Exam 1 score')\n",
-    "pyplot.ylabel('Exam 2 score')\n",
-    "pyplot.legend(['Admitted', 'Not admitted'])\n",
+    "plt.xlabel('Exam 1 score')\n",
+    "plt.ylabel('Exam 2 score')\n",
+    "plt.legend(['Admitted', 'Not admitted'])\n",
     "pass"
    ]
   },
@@ -660,11 +660,11 @@
    "source": [
     "plotData(X, y)\n",
     "# Labels and Legend\n",
-    "pyplot.xlabel('Microchip Test 1')\n",
-    "pyplot.ylabel('Microchip Test 2')\n",
+    "plt.xlabel('Microchip Test 1')\n",
+    "plt.ylabel('Microchip Test 2')\n",
     "\n",
     "# Specified in plot order\n",
-    "pyplot.legend(['y = 1', 'y = 0'], loc='upper right')\n",
+    "plt.legend(['y = 1', 'y = 0'], loc='upper right')\n",
     "pass"
    ]
   },
@@ -920,11 +920,11 @@
     "theta = res.x\n",
     "\n",
     "utils.plotDecisionBoundary(plotData, theta, X, y)\n",
-    "pyplot.xlabel('Microchip Test 1')\n",
-    "pyplot.ylabel('Microchip Test 2')\n",
-    "pyplot.legend(['y = 1', 'y = 0'])\n",
-    "pyplot.grid(False)\n",
-    "pyplot.title('lambda = %0.2f' % lambda_)\n",
+    "plt.xlabel('Microchip Test 1')\n",
+    "plt.ylabel('Microchip Test 2')\n",
+    "plt.legend(['y = 1', 'y = 0'])\n",
+    "plt.grid(False)\n",
+    "plt.title('lambda = %0.2f' % lambda_)\n",
     "\n",
     "# Compute accuracy on our training set\n",
     "p = predict(theta, X)\n",
@@ -957,7 +957,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

Exercise3/exercise3.ipynb

@@ -30,7 +30,7 @@
     "import numpy as np\n",
     "\n",
     "# Plotting library\n",
-    "from matplotlib import pyplot\n",
+    "from matplotlib import pyplot as plt\n",
     "\n",
     "# Optimization module in scipy\n",
     "from scipy import optimize\n",
@@ -915,7 +915,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

Exercise4/exercise4.ipynb

@@ -29,7 +29,7 @@
     "import numpy as np\n",
     "\n",
     "# Plotting library\n",
-    "from matplotlib import pyplot\n",
+    "from matplotlib import pyplot as plt\n",
     "\n",
     "# Optimization module in scipy\n",
     "from scipy import optimize\n",
@@ -932,7 +932,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

Exercise5/exercise5.ipynb

@@ -29,7 +29,7 @@
     "import numpy as np\n",
     "\n",
     "# Plotting library\n",
-    "from matplotlib import pyplot\n",
+    "from matplotlib import pyplot as plt\n",
     "\n",
     "# Optimization module in scipy\n",
     "from scipy import optimize\n",
@@ -114,9 +114,9 @@
     "m = y.size\n",
     "\n",
     "# Plot training data\n",
-    "pyplot.plot(X, y, 'ro', ms=10, mec='k', mew=1)\n",
-    "pyplot.xlabel('Change in water level (x)')\n",
-    "pyplot.ylabel('Water flowing out of the dam (y)');"
+    "plt.plot(X, y, 'ro', ms=10, mec='k', mew=1)\n",
+    "plt.xlabel('Change in water level (x)')\n",
+    "plt.ylabel('Water flowing out of the dam (y)');"
    ]
   },
   {
@@ -317,10 +317,10 @@
     "theta = utils.trainLinearReg(linearRegCostFunction, X_aug, y, lambda_=0)\n",
     "\n",
     "#  Plot fit over the data\n",
-    "pyplot.plot(X, y, 'ro', ms=10, mec='k', mew=1.5)\n",
-    "pyplot.xlabel('Change in water level (x)')\n",
-    "pyplot.ylabel('Water flowing out of the dam (y)')\n",
-    "pyplot.plot(X, np.dot(X_aug, theta), '--', lw=2);"
+    "plt.plot(X, y, 'ro', ms=10, mec='k', mew=1.5)\n",
+    "plt.xlabel('Change in water level (x)')\n",
+    "plt.ylabel('Water flowing out of the dam (y)')\n",
+    "plt.plot(X, np.dot(X_aug, theta), '--', lw=2);"
    ]
   },
   {
@@ -464,12 +464,12 @@
     "Xval_aug = np.concatenate([np.ones((yval.size, 1)), Xval], axis=1)\n",
     "error_train, error_val = learningCurve(X_aug, y, Xval_aug, yval, lambda_=0)\n",
     "\n",
-    "pyplot.plot(np.arange(1, m+1), error_train, np.arange(1, m+1), error_val, lw=2)\n",
-    "pyplot.title('Learning curve for linear regression')\n",
-    "pyplot.legend(['Train', 'Cross Validation'])\n",
-    "pyplot.xlabel('Number of training examples')\n",
-    "pyplot.ylabel('Error')\n",
-    "pyplot.axis([0, 13, 0, 150])\n",
+    "plt.plot(np.arange(1, m+1), error_train, np.arange(1, m+1), error_val, lw=2)\n",
+    "plt.title('Learning curve for linear regression')\n",
+    "plt.legend(['Train', 'Cross Validation'])\n",
+    "plt.xlabel('Number of training examples')\n",
+    "plt.ylabel('Error')\n",
+    "plt.axis([0, 13, 0, 150])\n",
     "\n",
     "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n",
     "for i in range(m):\n",
@@ -654,24 +654,24 @@
     "                             lambda_=lambda_, maxiter=55)\n",
     "\n",
     "# Plot training data and fit\n",
-    "pyplot.plot(X, y, 'ro', ms=10, mew=1.5, mec='k')\n",
+    "plt.plot(X, y, 'ro', ms=10, mew=1.5, mec='k')\n",
     "\n",
     "utils.plotFit(polyFeatures, np.min(X), np.max(X), mu, sigma, theta, p)\n",
     "\n",
-    "pyplot.xlabel('Change in water level (x)')\n",
-    "pyplot.ylabel('Water flowing out of the dam (y)')\n",
-    "pyplot.title('Polynomial Regression Fit (lambda = %f)' % lambda_)\n",
-    "pyplot.ylim([-20, 50])\n",
+    "plt.xlabel('Change in water level (x)')\n",
+    "plt.ylabel('Water flowing out of the dam (y)')\n",
+    "plt.title('Polynomial Regression Fit (lambda = %f)' % lambda_)\n",
+    "plt.ylim([-20, 50])\n",
     "\n",
-    "pyplot.figure()\n",
+    "plt.figure()\n",
     "error_train, error_val = learningCurve(X_poly, y, X_poly_val, yval, lambda_)\n",
-    "pyplot.plot(np.arange(1, 1+m), error_train, np.arange(1, 1+m), error_val)\n",
+    "plt.plot(np.arange(1, 1+m), error_train, np.arange(1, 1+m), error_val)\n",
     "\n",
-    "pyplot.title('Polynomial Regression Learning Curve (lambda = %f)' % lambda_)\n",
-    "pyplot.xlabel('Number of training examples')\n",
-    "pyplot.ylabel('Error')\n",
-    "pyplot.axis([0, 13, 0, 100])\n",
-    "pyplot.legend(['Train', 'Cross Validation'])\n",
+    "plt.title('Polynomial Regression Learning Curve (lambda = %f)' % lambda_)\n",
+    "plt.xlabel('Number of training examples')\n",
+    "plt.ylabel('Error')\n",
+    "plt.axis([0, 13, 0, 100])\n",
+    "plt.legend(['Train', 'Cross Validation'])\n",
     "\n",
     "print('Polynomial Regression (lambda = %f)\\n' % lambda_)\n",
     "print('# Training Examples\\tTrain Error\\tCross Validation Error')\n",
@@ -821,10 +821,10 @@
    "source": [
     "lambda_vec, error_train, error_val = validationCurve(X_poly, y, X_poly_val, yval)\n",
     "\n",
-    "pyplot.plot(lambda_vec, error_train, '-o', lambda_vec, error_val, '-o', lw=2)\n",
-    "pyplot.legend(['Train', 'Cross Validation'])\n",
-    "pyplot.xlabel('lambda')\n",
-    "pyplot.ylabel('Error')\n",
+    "plt.plot(lambda_vec, error_train, '-o', lambda_vec, error_val, '-o', lw=2)\n",
+    "plt.legend(['Train', 'Cross Validation'])\n",
+    "plt.xlabel('lambda')\n",
+    "plt.ylabel('Error')\n",
     "\n",
     "print('lambda\\t\\tTrain Error\\tValidation Error')\n",
     "for i in range(len(lambda_vec)):\n",
@@ -907,7 +907,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

Exercise6/exercise6.ipynb

@@ -32,7 +32,7 @@
     "import re\n",
     "\n",
     "# Plotting library\n",
-    "from matplotlib import pyplot\n",
+    "from matplotlib import pyplot as plt\n",
     "\n",
     "# Optimization module in scipy\n",
     "from scipy import optimize\n",
@@ -1021,7 +1021,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,