Numpy Basics.ipynb

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      "<type 'numpy.ndarray'>\n"
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    "#reference:http://cs231n.github.io/python-numpy-tutorial/\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "test = np.array([1, 2, 3])  \n",
    "print type(test)            \n",
    "#print test.shape            \n",
    "#print test[0], test[1],test[2]  \n",
    "#test[0] = 5                \n",
    "#print test                 \n"
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      "(2, 3)\n"
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    "\n",
    "test2 = np.array([[1,2,3],[4,5,6]])   \n",
    "print test2.shape                     \n",
    "#print test2[0, 0]\n",
    "#print test2"
   ]
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      "[1 2 3]\n"
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    "#built in functions to create arrays:\n",
    "#test = np.zeros((2,2))\n",
    "#test = np.ones((1,2)) \n",
    "#test = np.random.random((2,2))\n",
    "print test"
   ]
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    "# Create the following rank 2 array with shape (3, 4)\n",
    "# [[ 1  2  3  4]\n",
    "#  [ 5  6  7  8]\n",
    "#  [ 9 10 11 12]]\n",
    "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
    "\n",
    "\n",
    "row_r1 = a[1, :]     \n",
    "row_r2 = a[1:2, :]  \n",
    "\n",
    "#print row_r1, row_r1.shape  \n",
    "#print row_r2, row_r2.shape\n",
    "\n",
    "# We can make the same distinction when accessing columns of an array:\n",
    "col_r1 = a[:, 1]\n",
    "col_r2 = a[:, 1:2]\n",
    "#print col_r1, col_r1.shape\n",
    "#print col_r2, col_r2.shape "
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   "source": [
    "a = np.array([[1,2], [3, 4], [5, 6]])\n",
    "\n",
    "# An example of integer array indexing.\n",
    "# The returned array will have shape (3,) and \n",
    "#print a[[0, 1, 2], [0, 1, 0]]  \n",
    "\n",
    "# The above example of integer array indexing is equivalent to this:\n",
    "#print np.array([a[0, 0], a[1, 1], a[2, 0]])  "
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    "#Elementwise Operations\n",
    "\n",
    "x = np.array([[1,2],[3,4]], dtype=np.float64)\n",
    "y = np.array([[5,6],[7,8]], dtype=np.float64)\n",
    "\n",
    "\n",
    "#print x + y\n",
    "#print np.add(x, y)\n",
    "\n",
    "#print x - y\n",
    "#print np.subtract(x, y)\n",
    "\n",
    "#print x * y\n",
    "#print np.multiply(x, y)\n",
    "\n",
    "#print x / y\n",
    "#print np.divide(x, y)\n",
    "\n",
    "#print np.sqrt(x)"
   ]
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   "source": [
    "x = np.array([[1,2],[3,4]])\n",
    "y = np.array([[5,6],[7,8]])\n",
    "\n",
    "v = np.array([9,10])\n",
    "w = np.array([11, 12])\n",
    "\n",
    "# Inner product of vectors; both produce 219\n",
    "#print v.dot(w)\n",
    "#print np.dot(v, w)\n",
    "\n",
    "# Matrix / vector product; both produce the rank 1 array [29 67]\n",
    "#print x.dot(v)\n",
    "#print np.dot(x, v)\n",
    "\n",
    "# Matrix / matrix product;\n",
    "#print x.dot(y)\n",
    "#print np.dot(x, y)"
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      "10\n",
      "[4 6]\n",
      "[3 7]\n"
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   "source": [
    "x = np.array([[1,2],[3,4]])\n",
    "\n",
    "#print np.sum(x)  # Compute sum of all elements;\n",
    "#print np.sum(x, axis=0)  # Compute sum of each column;\n",
    "#print np.sum(x, axis=1)  # Compute sum of each row; "
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