all_things_PCA.ipynb

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     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib notebook\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "from sklearn.svm import SVC\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# PCA-zoo"
   ]
  },
  {
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   "source": [
    "This notebook explores different methods for calculating Principle Component Analysis and some clever usecases in neural networks.  \n",
    "https://arxiv.org/abs/1404.3606 PCAnet  \n",
    "https://arxiv.org/abs/1512.06337 kernel-PCAnet*  \n",
    "  \n",
    "*Unfortunately my laptop has limited amount of RAM and I can't test kPCAnet....  \n",
    "  \n",
    "  \n",
    "Dimensionality of extracted features is usually much larger than in original features, so first guess was using SVMs to classify afterwards.  \n",
    "Some people seem to use RandomForests or similar but especially with kPCAnet the number of features tends to be very high. I'm not sure  \n",
    "whether I missed something, since other implementations have way lower number of features. But then this is controlled by filtersize, stride,  \n",
    "and number of blocks used for hashing. Latter is mostly influenced by the number of filters of in the second stage, since the histogram size  \n",
    "is '2 ^ (filters in 2nd stage)'.  \n",
    "  \n",
    "The total number of extracted features is **( 2^[#stage2 filters] \\* [#stage1 filters] \\* [#blocks] )**\n"
   ]
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   "source": [
    "## Power iteration PCA  \n",
    "calculates PCA via power iteration and deflation  \n",
    "pros:  \n",
    "- only a single hyperparam, that has to be just high enough\n",
    "  \n",
    "cons:  \n",
    "- computes PCs iteratively, so only use it if you want to know the first few"
   ]
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    "def power_pca(data, number_of_components = 8,iterations=100,M=[]):\n",
    "    '''\n",
    "    input: data, or M = data x data     ( ASSUMPTION:  data.mean(0) = ZERO !!)\n",
    "    output: first 'number_of_components' of eigenvalues and eigenvectors\n",
    "    '''\n",
    "    if np.array(M).shape[0] > 0: pass # do better checking \n",
    "    else: M = np.dot(data.T,data)\n",
    "    \n",
    "    eigenwerte = []\n",
    "    eigenvektoren = []\n",
    "    for k in range(number_of_components):\n",
    "        ev_zero = np.random.uniform(-1,1,(M.shape[0]))\n",
    "        ev_zero /= np.linalg.norm(ev_zero)\n",
    "        eV = ev_zero\n",
    "        for i in range(iterations):\n",
    "            eV = np.dot(M,eV)\n",
    "            eW = np.linalg.norm(eV)\n",
    "            eV = eV / eW\n",
    "        eigenwerte  += [eW]\n",
    "        eigenvektoren += [eV]\n",
    "        M = M - eW*np.dot(eV[:,True],eV[:,True].T)\n",
    "        \n",
    "    return np.array(eigenwerte), np.array(eigenvektoren)\n"
   ]
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   "source": [
    "## GHA (Hebbian PCA)  \n",
    "the general hebbian algorithm ...calculates PCA via Sanger's rule / Oja's rule & Gram-Schmidt orthonormalization  \n",
    "pros:  \n",
    "- kinda fast  \n",
    "- online PCA\n",
    "  \n",
    "cons:  \n",
    "- the usual learningrate-hyperparam stuff ...make it small enough or loose accuracy blabla"
   ]
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    "def hebbian_pca(data, number_of_components =8, w =[], lr =0.001, iterations =100):\n",
    "    '''\n",
    "    input: data (N x d)        ( ASSUMPTION:  data.mean(0) = ZERO !!)\n",
    "    output: first 'number_of_components' of eigenvectors 'w'\n",
    "    \n",
    "    '''\n",
    "    npc = number_of_components\n",
    "    if np.array(w).shape[0] > 0: pass # do better checking \n",
    "    else: w = np.random.uniform(-1,1,(data.shape[1],npc))\n",
    "\n",
    "    for iters in range(iterations):\n",
    "        for x in data:\n",
    "            y = np.dot(w.T,x[:,True])\n",
    "            Ymat= []\n",
    "            for i in range(npc):\n",
    "                Ymat += [y]\n",
    "            Y_mat = np.array(Ymat)[:,:,0]\n",
    "            tril_mat = np.tril(Y_mat,k=0).T\n",
    "            S_mat = np.dot(w,tril_mat)\n",
    "            Xmat= []\n",
    "            for i in range(npc):\n",
    "                Xmat += [x]\n",
    "            X_mat = np.array(Xmat).T\n",
    "            dw = lr * y[:,0]* (X_mat - S_mat)\n",
    "            w += dw\n",
    "    return w\n"
   ]
  },
  {
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   "source": [
    "### PCA showdown  \n",
    "comparing power PCA with GHA and eigendecomposition  by plotting PC1 vs PC2  \n",
    "(notice how signs are flipped sometimes)"
   ]
  },
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     "text": [
      "done with donald hebb\n",
      "done with power\n",
      "done with numpy\n"
     ]
    },
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       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\numpy\\core\\numeric.py:583: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order, subok=True)\n"
     ]
    }
   ],
   "source": [
    "data = mnist.train.images[:100]\n",
    "data -= data.mean(0)\n",
    "\n",
    "w = hebbian_pca(data,number_of_components= 10)\n",
    "print('done with donald hebb')\n",
    "ew, ev = power_pca(data[:100],number_of_components=10)\n",
    "print('done with power')\n",
    "eW, eV = np.linalg.eig(np.dot(data[:100].T,data[:100]))\n",
    "print('done with numpy')\n",
    "reco_A = np.dot(data[:100],w)\n",
    "reco_B = np.dot(data[:100],ev.T)\n",
    "reco_C = np.dot(data[:100],eV)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(14,4))\n",
    "colors = np.argmax(mnist.train.labels[:100],axis=1)/10. +.1\n",
    "plt.subplot(131)\n",
    "plt.scatter(reco_A[:,0],reco_A[:,1],c=colors,edgecolors=None,s=30,cmap='rainbow')\n",
    "plt.title('hebbian pca')\n",
    "plt.subplot(132)\n",
    "plt.scatter(reco_B[:,0],reco_B[:,1],c=colors,edgecolors=None,s=30,cmap='rainbow')\n",
    "plt.title('power iteration pca')\n",
    "plt.subplot(133)\n",
    "plt.scatter(reco_C[:,0],reco_C[:,1],c=colors,edgecolors=None,s=30,cmap='rainbow')\n",
    "plt.title('numpy linalg eig pca')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## kernelPCA  \n",
    "kernelPCA function with different kernels and a projection function for new data  \n",
    "pros:  \n",
    "- kernel stuff, the usual magic\n",
    "  \n",
    "cons:  \n",
    "- lots of data = huuuuge kernel matrices ....better have a sh*tload of RAM available  \n",
    "- have I mentioned hyperparameters you can tune?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-30T18:00:54.812494",
     "start_time": "2017-12-30T18:00:54.765630"
    },
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#kernels:\n",
    "def rbf_K(x,y,gamma=0.1):\n",
    "    return np.exp(- gamma*(np.linalg.norm(x-y))**2 )\n",
    "\n",
    "def lin_K(x, y, c=0.):\n",
    "    return np.dot(x, y) + c\n",
    "\n",
    "def poly_K(x, y, p=3):\n",
    "    return np.power((1 + np.dot(x, y)),p)\n",
    "\n",
    "\n",
    "def kernel_pca(data,kernel=rbf_K,number_of_components=8):\n",
    "    '''\n",
    "    kernel PCA using power method to calculate components\n",
    "    \n",
    "    input: data  (doesn't have to be centered, since PCA is performed in Kernel-space and Kernel is centered)\n",
    "    output: first 'number_of_components' of eigenvalues and eigenvectors\n",
    "    \n",
    "    '''\n",
    "    K_data = np.empty((data.shape[0],data.shape[0]))\n",
    "    for i in range(data.shape[0]):\n",
    "        for j in range(data.shape[0]):\n",
    "            K_data[i,j] = kernel(data[i],data[j])\n",
    "       \n",
    "    K_data_mean_zero = K_data.mean(0)\n",
    "    K_data_mean_one = K_data.mean(1)\n",
    "    K_data_mean = K_data.mean()\n",
    "    K_data_centered = K_data - K_data_mean_zero - K_data_mean_one + K_data_mean\n",
    "    \n",
    "    eW , eV = power_pca(K_data , M=K_data_centered,number_of_components=number_of_components,iterations=100)\n",
    "    \n",
    "    return eW, eV\n",
    "\n",
    "\n",
    "def kpca_proj(new_data, pca_data, eV, eW, kernel= rbf_K):\n",
    "    new_data_kernel = np.empty((new_data.shape[0],pca_data.shape[0]))\n",
    "    for i in range(new_data.shape[0]):\n",
    "        for j in range(pca_data.shape[0]):\n",
    "            new_data_kernel[i,j] = kernel(new_data[i],pca_data[j])\n",
    "        \n",
    "    return new_data_kernel.dot(eV.T / eW)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### kernel showdown  \n",
    "kernelPCA is super cool, but notice the importance of hyperparameter choice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-30T18:01:05.421005",
     "start_time": "2017-12-30T18:00:54.812494"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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       "\n",
       "    this.ws = websocket;\n",
       "\n",
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       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
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       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width);\n",
       "        canvas.attr('height', height);\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'];\n",
       "    var y0 = fig.canvas.height - msg['y0'];\n",
       "    var x1 = msg['x1'];\n",
       "    var y1 = fig.canvas.height - msg['y1'];\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x;\n",
       "    var y = canvas_pos.y;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = mnist.train.images[500:1000]\n",
    "data -= data.mean(0)\n",
    "newdata = mnist.train.images[:100]\n",
    "#rbf\n",
    "eWa, eVa = kernel_pca(data,rbf_K,30)\n",
    "projection_rbf = kpca_proj(newdata, data, eVa, eWa, kernel= rbf_K)\n",
    "#poly\n",
    "eWb, eVb = kernel_pca(data,poly_K,30)\n",
    "projection_poly = kpca_proj(newdata, data, eVb, eWb, kernel= poly_K)\n",
    "#linear\n",
    "eWc, eVc = kernel_pca(data,lin_K,30)\n",
    "projection_lin = kpca_proj(newdata, data, eVc, eWc, kernel= lin_K)\n",
    "\n",
    "plt.figure(figsize=(14,8))\n",
    "colors = np.argmax(mnist.train.labels[:100],axis=1)/10. +.1\n",
    "#rbf\n",
    "plt.subplot(231)\n",
    "plt.scatter(projection_rbf[:,0],projection_rbf[:,1],c=colors,edgecolors=None,s=30,cmap='rainbow')\n",
    "plt.title('rbf kernel')\n",
    "plt.subplot(234)\n",
    "plt.scatter(range(eWa.shape[0]),eWa/np.sum(eWa),s=5)\n",
    "\n",
    "#poly\n",
    "plt.subplot(232)\n",
    "plt.scatter(projection_poly[:,0],projection_poly[:,1],c=colors,edgecolors=None,s=30,cmap='rainbow')\n",
    "plt.title('poly kernel')\n",
    "plt.subplot(235)\n",
    "plt.scatter(range(eWb.shape[0]),eWb/np.sum(eWb),s=5)\n",
    "\n",
    "#lin\n",
    "plt.subplot(233)\n",
    "plt.scatter(projection_lin[:,0],projection_lin[:,1],c=colors,edgecolors=None,s=30,cmap='rainbow')\n",
    "plt.title('linear kernel')\n",
    "plt.subplot(236)\n",
    "plt.scatter(range(eWc.shape[0]),eWc/np.sum(eWc),s=5)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCAnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-30T18:01:05.686446",
     "start_time": "2017-12-30T18:01:05.421005"
    },
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def make_patches(inputs, filter_size=7 ,stride=1 ):\n",
    "    '''\n",
    "    input: N x W x H images\n",
    "    output: creates patches with zero-mean \n",
    "    \n",
    "    '''\n",
    "    if len(inputs.shape) > 3:\n",
    "        inputs = inputs.reshape(inputs.shape[0]*inputs.shape[1],inputs.shape[2],inputs.shape[3])\n",
    "    X = []\n",
    "    for img in range(inputs.shape[0]): \n",
    "        arr = inputs[img]\n",
    "        sub_arr = []\n",
    "        x = y = 0\n",
    "        while (x+filter_size) <= arr.shape[0]:\n",
    "            y = 0\n",
    "            while (y+filter_size) <= arr.shape[1]:\n",
    "                sub_arr += [arr[x:x+filter_size,y:y+filter_size]]\n",
    "                y += stride\n",
    "            x += stride  \n",
    "        sub_arr = np.array(sub_arr)\n",
    "        sub_arr = sub_arr.reshape(int(np.sqrt(sub_arr.shape[0])),int(np.sqrt(sub_arr.shape[0])),(sub_arr.shape[1]),(sub_arr.shape[2]))\n",
    "        sub_arrR = sub_arr.reshape(sub_arr.shape[0]**2,filter_size**2)\n",
    "        sub_arrRmean = sub_arrR - sub_arrR.mean(0)\n",
    "        X += [sub_arrRmean]\n",
    "    X = np.array(X)\n",
    "    X = X.reshape(X.shape[0]*X.shape[1],X.shape[2])\n",
    "    return X\n",
    "\n",
    "def make_filter(patches, filter_= 8):\n",
    "    '''\n",
    "    input: N x (W*H) patches\n",
    "    output: specified number of PCA filter\n",
    "    '''\n",
    "    X = patches\n",
    "    eW, eV = power_pca(X,number_of_components= filter_,iterations=100,M=[])\n",
    "    L_filter = eV.reshape(eW.shape[0],int(np.sqrt(patches.shape[1])),int(np.sqrt(patches.shape[1])))\n",
    "    \n",
    "    ### adaptation to kernelPCA filters...\n",
    "    #EW, EV = kernel_pca(X,rbf_K,filter_)\n",
    "    #EV = EV[:,:int(patches.shape[1])]\n",
    "    #print('kernel eigenvectors shape:   ',EV.shape)\n",
    "    #L_filter = EV.reshape(EV.shape[0],int(np.sqrt(patches.shape[1])),int(np.sqrt(patches.shape[1])))\n",
    "    #print('kernel eigenfilters shape:   ',L_filter.shape)\n",
    "    \n",
    "    return L_filter\n",
    "\n",
    "def convolve_(inputs, filter_ ,stage = 1,stride=1, padding=0 ):\n",
    "    '''\n",
    "    input: N x (S1 x) W x H images\n",
    "    output: convolved images\n",
    "    '''\n",
    "    if stage == 2 : \n",
    "        innerloop = inputs.shape[1]\n",
    "        outerloop = inputs.shape[0]\n",
    "    else: \n",
    "        innerloop = inputs.shape[0]\n",
    "        outerloop = 1\n",
    "    \n",
    "    out_stage_two = []\n",
    "    filter_shape = filter_.shape[0]\n",
    "    for img in range(outerloop):  \n",
    "        out_stage_one = []\n",
    "        for st in range(innerloop):\n",
    "            if outerloop > 1: arr = inputs[img,st]\n",
    "            else: arr = inputs[st]\n",
    "            if padding > 0:\n",
    "                arr = np.pad(arr,padding,mode='constant')\n",
    "            filter_shape = filter_.shape[1]\n",
    "            sub_arr = []\n",
    "            x = y = 0\n",
    "            while (x+filter_shape) <= arr.shape[0]:\n",
    "                y = 0\n",
    "                while (y+filter_shape) <= arr.shape[1]:\n",
    "                    sub_arr += [arr[x:x+filter_shape,y:y+filter_shape]]\n",
    "                    y += stride\n",
    "                x += stride  \n",
    "            sub_arr = np.array(sub_arr)\n",
    "            sub_arr = sub_arr.reshape(int(np.sqrt(sub_arr.shape[0])),int(np.sqrt(sub_arr.shape[0])),(sub_arr.shape[1]),(sub_arr.shape[2]))\n",
    "            stage_arr = []\n",
    "            for f in range(filter_.shape[0]):\n",
    "                new_arr = (sub_arr*filter_[f]).sum(axis=(2,3))\n",
    "                stage_arr += [new_arr]\n",
    "            out_stage_one += [stage_arr]\n",
    "        out_stage_two += [out_stage_one] \n",
    "    if stage == 1 : out_stage_two = out_stage_two[0]\n",
    "    return np.array(out_stage_two)\n",
    "\n",
    "\n",
    "def binarize(inputs):\n",
    "    '''\n",
    "    0 if <= 0\n",
    "    1 else\n",
    "    '''\n",
    "    return np.where(inputs <= 0., 0,1)\n",
    "\n",
    "\n",
    "def decimalize(inputs):\n",
    "    '''\n",
    "    inputs: N x S1 x S2 X W x H   binarized PCAnet outputs\n",
    "    output: N x S1 x W x H x 1 decimalized PCAnet outputs\n",
    "    '''\n",
    "    binarr = inputs.swapaxes(2,4)\n",
    "    return np.packbits(binarr,axis=4)\n",
    "\n",
    "\n",
    "def make_blocks(inputs,block_size = 7, stride = 3):\n",
    "    '''\n",
    "    inputs: N x S1 x W x H x 1  decimalized PCAnet outputs\n",
    "    output: N x S1 x B x (block_size**2) \n",
    "    '''\n",
    "    images = []\n",
    "    for img in range(inputs.shape[0]): \n",
    "        X = []\n",
    "        for ston in range(inputs.shape[1]):\n",
    "\n",
    "            arr = inputs[img,ston]\n",
    "            sub_arr = []\n",
    "            x = y = 0\n",
    "            while (x+block_size) <= arr.shape[0]:\n",
    "                y = 0\n",
    "                while (y+block_size) <= arr.shape[1]:\n",
    "                    sub_arr += [arr[x:x+block_size,y:y+block_size]]\n",
    "                    y += stride\n",
    "                x += stride  \n",
    "            sub_arr = np.array(sub_arr)\n",
    "            sub_arrR = sub_arr.reshape(sub_arr.shape[0],block_size**2)\n",
    "            X += [sub_arrR]\n",
    "        images += [X]\n",
    "    blocks = np.array(images)\n",
    "    return blocks\n",
    "\n",
    "\n",
    "def histogramming(inputs):\n",
    "    '''\n",
    "    inputs: N x S1 x B x (block_size**2) \n",
    "    output: N x (2**S2 * S1 * B) \n",
    "    '''\n",
    "    in_blocks = inputs\n",
    "    features = []\n",
    "    for img in range(in_blocks.shape[0]): \n",
    "        TI = []\n",
    "        for ston in range(in_blocks.shape[1]):\n",
    "            X = []\n",
    "            for block in range(in_blocks.shape[2]):\n",
    "                hist = np.histogram(in_blocks[img,ston,block],255,range=(0,255))[0]\n",
    "                X += [hist]\n",
    "            TI += [X]\n",
    "        features += [TI]\n",
    "    features = np.array(features)\n",
    "    features = features.reshape(features.shape[0],features.shape[1]*features.shape[2]*features.shape[3])\n",
    "    return features\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## training  \n",
    "create filters from patches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-31T15:47:12.294482",
     "start_time": "2017-12-31T15:45:26.265518"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(54000, 49) patches\n",
      "(8, 7, 7) L1 filter\n",
      "(1500, 8, 28, 28) out 1\n",
      "(432000, 49) patches2\n",
      "(8, 7, 7) L2 filter\n",
      "(1500, 8, 8, 28, 28) out 2\n",
      "(1500, 8, 8, 28, 28) out bin\n",
      "(1500, 8, 28, 28, 1) out dec\n",
      "(1500, 8, 64, 49) out blocks\n",
      "(1500, 130560) out hist\n"
     ]
    }
   ],
   "source": [
    "### PCAnet:\n",
    "number_of_images = 1500\n",
    "images = mnist.train.images[:number_of_images].reshape(number_of_images,28,28)\n",
    "\n",
    "\n",
    "### TRAINING\n",
    "#stage 1 \n",
    "patches = make_patches(images,filter_size=7,stride=4)\n",
    "print(patches.shape, 'patches' )\n",
    "L1filter = make_filter(patches,filter_=8) #####1\n",
    "print(L1filter.shape, 'L1 filter')\n",
    "outS1 = convolve_(images,L1filter,stage=1,stride=1,padding=3)\n",
    "print(outS1.shape, 'out 1')\n",
    "#stage 2 \n",
    "patches2 = make_patches(outS1,7,4)\n",
    "print(patches2.shape, 'patches2' )\n",
    "L2filter = make_filter(patches2,8) ####4\n",
    "print(L2filter.shape, 'L2 filter')\n",
    "outS2 = convolve_(outS1,L2filter,stage=2,stride=1,padding=3)\n",
    "print(outS2.shape, 'out 2')\n",
    "#binary hashing\n",
    "outBIN = binarize(outS2)\n",
    "print(outBIN.shape, 'out bin')\n",
    "outDEC = decimalize(outBIN)\n",
    "print(outDEC.shape, 'out dec')\n",
    "outBlocks = make_blocks(outDEC,block_size=7,stride=3)\n",
    "print(outBlocks.shape, 'out blocks')\n",
    "outHist = histogramming(outBlocks)\n",
    "print(outHist.shape, 'out hist')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### classification  \n",
    "SVM is used to get some results for the training error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-31T15:59:36.601845",
     "start_time": "2017-12-31T15:47:12.294482"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 3 4 6 1 8 1 0 9 8 0 3 1 2 7 0 2 9 6 0]\n",
      "[7 3 4 6 1 8 1 0 9 8 0 3 1 2 7 0 2 9 6 0]\n",
      "[0.0]  train error\n"
     ]
    }
   ],
   "source": [
    "inputs = outHist\n",
    "number_of_images = 1500\n",
    "vectormachine = SVC(C=8.)\n",
    "vectormachine.fit(inputs, np.argmax(mnist.train.labels[:number_of_images],axis=1))\n",
    "print(vectormachine.predict(inputs[:20]))\n",
    "print(np.argmax(mnist.train.labels[:20],axis=1))\n",
    "print([np.where(np.argmax(mnist.train.labels[:number_of_images*1],axis=1) != vectormachine.predict(inputs))[0].shape[0] /  number_of_images*1.], ' train error')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## testing  \n",
    "create features for new data by convolving with trained filters.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-31T16:00:10.933581",
     "start_time": "2017-12-31T15:59:36.601845"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 8, 28, 28) out 1\n",
      "(500, 8, 8, 28, 28) out 2\n",
      "(500, 8, 8, 28, 28) out bin\n",
      "(500, 8, 28, 28, 1) out dec\n",
      "(500, 8, 64, 49) out blocks\n",
      "(500, 130560) out hist\n"
     ]
    }
   ],
   "source": [
    "### TESTING\n",
    "\n",
    "number_of_images = 500\n",
    "images = mnist.test.images[:number_of_images].reshape(number_of_images,28,28)\n",
    "\n",
    "#stage 1 \n",
    "outS1 = convolve_(images,L1filter,stage=1,stride=1,padding=3)\n",
    "print(outS1.shape, 'out 1')\n",
    "#stage 2 \n",
    "outS2 = convolve_(outS1,L2filter,stage=2,stride=1,padding=3)\n",
    "print(outS2.shape, 'out 2')\n",
    "#binary hashing\n",
    "outBIN = binarize(outS2)\n",
    "print(outBIN.shape, 'out bin')\n",
    "outDEC = decimalize(outBIN)\n",
    "print(outDEC.shape, 'out dec')\n",
    "outBlocks = make_blocks(outDEC,block_size=7,stride=3)\n",
    "print(outBlocks.shape, 'out blocks')\n",
    "outHist_test = histogramming(outBlocks)\n",
    "print(outHist_test.shape, 'out hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### classification  \n",
    "SVM trained on original features for comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-31T16:00:12.261849",
     "start_time": "2017-12-31T16:00:10.933581"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "number_of_images = 1500\n",
    "bas = SVC(C=8.)\n",
    "bas.fit(mnist.train.images[:number_of_images] , np.argmax(mnist.train.labels[:number_of_images],axis=1))\n",
    "print('done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-31T16:02:01.322439",
     "start_time": "2017-12-31T16:00:12.261849"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error with PCAnet features:  [0.044]\n",
      "error w/o PCAnet features:  [0.11]\n"
     ]
    }
   ],
   "source": [
    "number_of_images = 500\n",
    "test_err = [np.where(np.argmax(mnist.test.labels[:number_of_images],axis=1) != vectormachine.predict(outHist_test))[0].shape[0] /  number_of_images*1.]\n",
    "print('error with PCAnet features: ',test_err)\n",
    "bas_err = [np.where(np.argmax(mnist.test.labels[:number_of_images],axis=1) != bas.predict(mnist.test.images[:number_of_images]))[0].shape[0] /  number_of_images*1.]\n",
    "print('error w/o PCAnet features: ',bas_err)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## conclusion  \n",
    "While there is an improvement in classification performance with PCAnet features extractor, the dimensionality growth a lot. This often limits the choice of classifyer due to the curse of  dimensionality. Anyway, one could see PCAnet like a CNN without pooling, where the input only growth in volume after each stage. The binary hashing creates similar translation invariance as pooling would do. Since the volume growth in PCAnet is way higher than with traditional convolutions, the approach to concatenate stages until the 'image resolution' is **1x1xfeatures** is unfeasable. Maybe instead of hashing the images after the second stage, a deep convolution could be applied, which would work similar to hashing - but then again computational complexity may be too large."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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