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');" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x1 = torch.randn(40):mul(100)\n", "y1 = torch.randn(40):mul(100)\n", "x2 = torch.randn(40):mul(100)\n", "y2 = torch.randn(40):mul(100)\n", "x3 = torch.randn(40):mul(200)\n", "y3 = torch.randn(40):mul(200)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Plot = require 'itorch.Plot'" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\t\n", "\u001b[1;35m[res] torch.cmul([res,] tensor1, tensor2)\u001b[0m\n", "\n", "\n", "Element-wise multiplication of\u001b[0;32m tensor1 \u001b[0mby\u001b[0;32m tensor2 \u001b[0m. The number\n", "of elements must match, but sizes do not matter.\n", "\u001b[0;36m\u001b[0;36m > x = torch.Tensor(2,2):fill(2)\n", "> y = torch.Tensor(4):fill(3)\n", "> x:cmul(y)\n", "> = x\n", " 6 6\n", " 6 6\n", "[torch.Tensor of dimension 2x2] \u001b[0m\u001b[0m\n", "\n", "\u001b[0;32m z=torch.cmul(x,y) \u001b[0mreturns a new tensor.\n", "\n", "\u001b[0;32m torch.cmul(z,x,y) \u001b[0mputs the result in\u001b[0;32m z \u001b[0m.\n", "\n", "\u001b[0;32m y:cmul(x) \u001b[0mmultiplies all elements of\u001b[0;32m y \u001b[0mwith corresponding elements \n", "of\u001b[0;32m x \u001b[0m.\n", "\n", "\u001b[0;32m z:cmul(x,y) \u001b[0mputs the result in\u001b[0;32m z \u001b[0m.\t\n", "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\t\n", "\n" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "?torch.cmul" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot = Plot():circle(x1, y1, 'red', 'hi'):circle(x2, y2, 'blue', 'bye'):draw()\n", "plot:circle(x3,y3,'green', 'yolo'):redraw()\n", "plot:title('Scatter Plot Demo'):redraw()\n", "plot:xaxis('length'):yaxis('width'):redraw()\n", "plot:legend(true)\n", "plot:redraw()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "-- line plots\n", "plot = Plot():line(x1, y1,'red','example'):legend(true):title('Line Plot Demo'):draw()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "-- segment plots\n", "plot = Plot():segment(x1, y1, x1+10,y1+10, 'red','demo'):title('Segment Plot Demo'):draw()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "-- quiver plots\n", "xx = torch.linspace(-3,3,10)\n", "yy = torch.linspace(-3,3,10)\n", "local function meshgrid(x,y)\n", " local xx = torch.repeatTensor(x, y:size(1),1)\n", " local yy = torch.repeatTensor(y:view(-1,1), 1, x:size(1))\n", " return xx, yy\n", "end\n", "Y, X = meshgrid(xx, yy)\n", "U = -torch.pow(X,2) + Y -1\n", "V = X - torch.pow(Y,2) +1\n", "plot = Plot():quiver(U,V,'red','',10):title('Quiver Plot Demo'):draw()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "-- quads/rectangles\n", "x1=torch.randn(10)\n", "y1=torch.randn(10)\n", "plot = Plot():quad(x1,y1,x1+1,y1+1,'red',''):draw()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "-- histogram\n", "x = torch.rand(10000)\n", "lx = torch.log(x):add(1)\n", "plot = Plot():histogram(x):histogram(lx):title('Histogram of a gaussian draw'):draw()\n" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "-- group scatter\n", "x = torch.randn(200); y = torch.randn(200); x:narrow(1, 1, 100):add(2);\n", "labels = torch.LongTensor(200):zero(); labels:add(1); labels:narrow(1, 1, 100):add(1)\n", "itorch.Plot():gscatter(x, y):title('Scatter plot without labels'):draw()\n", "itorch.Plot():gscatter(x, y, labels):title('Scatter plot with labels and legend #1'):legend(true):draw()\n", "itorch.Plot():gscatter(x, y, labels, true):title('Scatter plot with labels and legend #2'):draw()\n", "itorch.Plot():gscatter(x, y, labels, false):title('Scatter plot with labels and no legend'):draw()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "-- Text method to plot\n", "local t = torch.Tensor\n", "local y = t(10)\n", "local x = t(y:size()):zero()\n", "local labels = {}\n", "for i = 1, 10 do\n", " y[i] = i\n", " labels[i] = tostring(i)\n", "end\n", "\n", "itorch.Plot():gscatter(x, y)\n", " :text(x, y, labels, y, 'black')\n", " :triangle(x, y, 'blue')\n", " :draw()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "-- hover tool\n", "local t = torch.Tensor\n", "local y = t(10)\n", "local x = t(y:size()):zero()\n", "local labels = {}\n", "for i = 1, 10 do\n", " y[i] = i\n", " labels[i] = tostring(i)\n", "end\n", "\n", "itorch.Plot()\n", " :circle(x, y, 'red', nil, {foo=labels})\n", " :hover_tool({{'xy', '@x @y'}, {'foo', '@foo'}})\n", " :draw()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "iTorch", "language": "lua", "name": "itorch" }, "language_info": { "name": "lua", "version": "20100" } }, "nbformat": 4, "nbformat_minor": 0 }