<?php $machine = "Prep_HPLC"; $LMCS_rf = $_REQUEST['LMCS_rf']; $run_time = 0; $run_time = 0; $output1 = ""; $percentACN = 0; if ($machine != "" && $LMCS_rf != "") { $output = ""; $output .= "<b>For the system:</b> <br />"; //$output .= "<b>Machine :</b> " . $machine . "<br />"; $output .= "<b>Rt (SCIEX LCMS):</b> " . $LMCS_rf . "<br />"; if ($LMCS_rf < 1.18 || $LMCS_rf > 3.72) { } else { predict($machine, $LMCS_rf, $percentACN, $output1); //echo "<br /><br />"; } } function predict($machine, $LMCS_rf, &$percentACN, &$output1) { if ($machine == "Prep_HPLC") { $percentACN = round(5.9864 * pow($LMCS_rf, 3) - 55.83 * pow($LMCS_rf, 2) + 188.76 * $LMCS_rf - 137.76, 1); $output1 .= "Ending Percent ACN is: " . $percentACN; $output1 .= "<br />"; $output1 .= "<br />"; } $output1; } ?>
<div class="menu_nav"> <div class="clr"></div> </div> <div i class="clr"></div> <div class="content"> <div class="content_bg"> <div class="mainbar"> <?php include '../config.inc'; $USER_ID = $_SESSION["user_id"]; echo "<h2>Here are some recommendations for you:<br/>_______________ </h2><br/><br/>"; for ($BOOK_ID = 1; $BOOK_ID <= 10; $BOOK_ID++) { $value = predict($USER_ID, $BOOK_ID); $threshold = 5; $img = mysql_query("SELECT BOOK_IMG FROM bookinfo WHERE BOOK_ID={$BOOK_ID}"); if ($threshold < $value) { $BOOK_NAME_RESULT = mysql_query("SELECT BOOK_NAME, BOOK_AUTHOR FROM bookinfo WHERE BOOK_ID={$BOOK_ID}"); $BOOK_NAME_RESULT_ROW = mysql_fetch_assoc($BOOK_NAME_RESULT); $BOOK_NAME = $BOOK_NAME_RESULT_ROW["BOOK_NAME"]; $BOOK_AUTHOR = $BOOK_NAME_RESULT_ROW["BOOK_AUTHOR"]; echo $BOOK_NAME . " by " . $BOOK_AUTHOR . "<br/><br/>"; //echo "The predicted rating for the book<a href=\"http://localhost/projectNew/bookInfo.php?book_id=".$BOOK_ID."\"> ".$BOOK_ID."</a> is"; //echo $value."<br/>"; } //else //echo "do not recommend <br />"; } function predict($USER_ID, $BOOK_ID)
$ending_percent = 0; $run_time = 0; $output1 = ""; $flow_rate = 0; $max_loading = 0; if ($solvent_system != "" && $tlc_rf != "") { $output = ""; $output .= "<b>For the system:</b> <br />"; $output .= "<b>TLC Solvent System:</b> " . $solvent_system . "<br />"; $output .= "<b>Column:</b> " . $column_s . "<br />"; $output .= "<b>Rf (TLC):</b> " . $tlc_rf . "<br />"; $output .= "<br />"; if ($tlc_rf < 0.2 || $tlc_rf > 0.6) { //echo "Rf must fall within range: 0.2 >= Rf <= 0.6"; } else { predict($solvent_system, $tlc_rf, $column_s, $starting_percent, $grad_starting_time, $ending_percent, $grad_ending_time, $run_time, $output1, $flow_rate, $max_loading); //echo "<br /><br />"; } } function roundDownToAny($n, $x = 5) { return round($n) % $x === 0 ? round($n) : round(($n - $x / 2) / $x) * $x; } function predict($solvent_system, $tlc_rf, $column_s, &$starting_percent, &$grad_starting_time, &$ending_percent, &$grad_ending_time, &$run_time, &$output1, &$flow_rate, &$max_loading) { $run_time = 27; $grad_starting_time = 3; $grad_ending_time = 13; if ($column_s == "12g") { $grad_starting_time = 2; $grad_ending_time = 8;
<pre> <?php include 'classifier.php'; // train or load an NGram profile, note a model will not be able to deal with languages it has not seen during training and // will gladly misclassify any such language as one it has seen during training. $classifier = new NGramProfiles('etc/classifiers/ngrams.dat'); if (!$classifier->exists()) { $classifier->train('en', 'etc/data/english.raw'); $classifier->train('nl', 'etc/data/dutch.raw'); $classifier->train('fr', 'etc/data/french.raw'); $classifier->save(); } else { $classifier->load(); } // simple prediction function that takes a classifier and a text and echo's the most likely language function predict($classifier, $text) { $language = $classifier->predict($text); echo "{$language} = '{$text}'\n"; } predict($classifier, "Dit is een nederlandse text."); predict($classifier, "This is an english text."); predict($classifier, "Ceci n'est pas une pipe.");
<?php require "index.php"; function predict($file, $user) { $guess = exec("/var/www/facerec/faces predict " . $file . " " . $user, $output); $guess = $output[0]; $conn = connectToDB(); $sql = "SELECT ACQUAINTANCE_FNAME, ACQUAINTANCE_LNAME, GENDER, RELATION, DESCRIPTION, ACQUAINTANCE_UID FROM RELATIONSHIP NATURAL JOIN ACQUAINTANCE WHERE USER_UID = '" . $user . "' AND REL_ID=" . $guess . ";"; $result = $conn->query($sql); $row = $result->fetch_assoc(); $row["DISTANCE"] = $output[1]; echo json_encode($row); } $filep = $_FILES['pic']['tmp_name']; $userid = $_POST['USERNAME']; predict($filep, $userid);
<?php include 'function.php'; for ($i = 1; $i <= 5; $i++) { for ($j = 1; $j <= 20; $j++) { echo 'User Id =' . $i . ' --- ' . 'Book Id =' . $j . ' --- ' . predict($i, $j) . '<br>'; } }
mysql_query($sql, $connection); if ($itemID != $other_itemID) { $sql = "UPDATE dev SET count=count+1,\n\t sum=sum-{$rating_difference}\n\t WHERE (itemID1={$other_itemID} AND itemID2={$itemID})"; mysql_query($sql, $connection); } } else { $sql = "INSERT INTO dev VALUES ({$itemID}, {$other_itemID},\n 1, {$rating_difference})"; mysql_query($sql, $connection); if ($itemID != $other_itemID) { $sql = "INSERT INTO dev VALUES ({$other_itemID},\n\t {$itemID}, 1, -{$rating_difference})"; mysql_query($sql, $connection); } } } for ($x = 1; $x <= 3; $x++) { $value = predict(1, $x); $threshold = 5; echo "the predicted rating for the item is"; echo $value . "<br />"; if ($threshold < $value) { echo "recommend item <br /> "; } else { echo "do not recommend <br />"; } } function predict($userID, $itemID) { global $connection; $denom = 0.0; $numer = 0.0; $k = $itemID;
<pre> <?php include 'classifier.php'; $classifier = new NGramProfiles('etc/classifiers/full.dat'); $classifier->train('en', 'etc/data/english.raw'); $classifier->train('nl', 'etc/data/dutch.raw'); $classifier->train('fr', 'etc/data/french.raw'); $classifier->train('de', 'etc/data/german.raw'); $classifier->train('id', 'etc/data/indonesian.raw'); $classifier->train('jp', 'etc/data/japanese.raw'); $classifier->train('pt', 'etc/data/portugese.raw'); $classifier->train('es', 'etc/data/spanish.raw'); $classifier->save(); // simple prediction function that takes a classifier and a text and echo's the most likely language function predict($classifier, $text, $result) { $language = $classifier->predict($text); echo "{$language} = {$result} @ '{$text}'\n"; } predict($classifier, "Dit is een nederlandse text.", 'nl'); predict($classifier, "This is an english text.", 'en'); predict($classifier, "Ceci n'est pas une pipe.", 'fr'); predict($classifier, "dies ist ein Satz auf Deutsch", 'de'); predict($classifier, "esta es una frase en alemán", 'es');
} echo json_encode(array("Movie" => $picked_list)); } else { // rating array got, call predict function $rating_array = json_decode($query, true); if ($rating_array == null) { echo "failed to parse json string."; echo "<br>"; echo $query; return; } if ($rating_array[0] == null or $rating_array[0]['id'] == null or $rating_array[0]['rating'] == null) { echo "json should be an array, each element of the array should contain key \"id\" and key \"rating\""; return; } $predict_array = predict($rating_array); $predict_array = array("Movie" => $predict_array); $json = json_encode($predict_array); echo $json; } function movie_ls() { // return movie list // return array ("id" => "movie_string") $file = fopen("movie_ids.txt", "r") or die("Unable to open movie_ids.txt"); $ans = array(); while (!feof($file)) { $line = fgets($file); $row = preg_split("/ /", $line, 2); $row[1] = rtrim($row[1], "\n"); $item = array("id" => $row[0], "title" => $row[1]);