-Clazz.declarePackage ("jalview.analysis");
-Clazz.load (["java.lang.StringBuffer"], "jalview.analysis.PCA", ["jalview.datamodel.BinarySequence", "jalview.math.Matrix", "jalview.schemes.ResidueProperties"], function () {
-c$ = Clazz.decorateAsClass (function () {
-this.m = null;
-this.symm = null;
-this.m2 = null;
-this.eigenvalue = null;
-this.eigenvector = null;
-this.details = null;
-this.jvCalcMode = true;
-Clazz.instantialize (this, arguments);
-}, jalview.analysis, "PCA", null, Runnable);
-Clazz.prepareFields (c$, function () {
-this.details = new StringBuffer ();
-});
-Clazz.makeConstructor (c$,
-function (s) {
-this.construct (s, false);
-}, "~A");
-Clazz.makeConstructor (c$,
-function (s, nucleotides) {
-this.construct (s, nucleotides, null);
-}, "~A,~B");
-Clazz.makeConstructor (c$,
-function (s, nucleotides, s_m) {
-var bs = new Array (s.length);
-var ii = 0;
-while ((ii < s.length) && (s[ii] != null)) {
-bs[ii] = new jalview.datamodel.BinarySequence (s[ii], nucleotides);
-bs[ii].encode ();
-ii++;
-}
-var bs2 = new Array (s.length);
-ii = 0;
-var smtrx = null;
-var sm = s_m;
-if (sm != null) {
-smtrx = jalview.schemes.ResidueProperties.getScoreMatrix (sm);
-}if (smtrx == null) {
-smtrx = jalview.schemes.ResidueProperties.getScoreMatrix (sm = (nucleotides ? "DNA" : "BLOSUM62"));
-}this.details.append ("PCA calculation using " + sm + " sequence similarity matrix\n========\n\n");
-while ((ii < s.length) && (s[ii] != null)) {
-bs2[ii] = new jalview.datamodel.BinarySequence (s[ii], nucleotides);
-if (smtrx != null) {
-try {
-bs2[ii].matrixEncode (smtrx);
-} catch (x) {
-if (Clazz.exceptionOf (x, jalview.datamodel.BinarySequence.InvalidSequenceTypeException)) {
-this.details.append ("Unexpected mismatch of sequence type and score matrix. Calculation will not be valid!\n\n");
-} else {
-throw x;
-}
-}
-}ii++;
-}
-var count = 0;
-while ((count < bs.length) && (bs[count] != null)) {
-count++;
-}
-var seqmat = Clazz.newDoubleArray (count, bs[0].getDBinary ().length, 0);
-var seqmat2 = Clazz.newDoubleArray (count, bs2[0].getDBinary ().length, 0);
-var i = 0;
-while (i < count) {
-seqmat[i] = bs[i].getDBinary ();
-seqmat2[i] = bs2[i].getDBinary ();
-i++;
-}
-this.m = new jalview.math.Matrix (seqmat, count, bs[0].getDBinary ().length);
-this.m2 = new jalview.math.Matrix (seqmat2, count, bs2[0].getDBinary ().length);
-}, "~A,~B,~S");
-Clazz.defineMethod (c$, "getM",
-function () {
-return this.m;
-});
-Clazz.defineMethod (c$, "getEigenvalue",
-function (i) {
-return this.eigenvector.d[i];
-}, "~N");
-Clazz.defineMethod (c$, "getComponents",
-function (l, n, mm, factor) {
-var out = Clazz.newFloatArray (this.m.rows, 3, 0);
-for (var i = 0; i < this.m.rows; i++) {
-out[i][0] = this.component (i, l) * factor;
-out[i][1] = this.component (i, n) * factor;
-out[i][2] = this.component (i, mm) * factor;
-}
-return out;
-}, "~N,~N,~N,~N");
-Clazz.defineMethod (c$, "component",
-function (n) {
-var out = Clazz.newDoubleArray (this.m.rows, 0);
-for (var i = 0; i < this.m.rows; i++) {
-out[i] = this.component (i, n);
-}
-return out;
-}, "~N");
-Clazz.defineMethod (c$, "component",
-function (row, n) {
-var out = 0.0;
-for (var i = 0; i < this.symm.cols; i++) {
-out += (this.symm.value[row][i] * this.eigenvector.value[i][n]);
-}
-return out / this.eigenvector.d[n];
-}, "~N,~N");
-Clazz.defineMethod (c$, "getDetails",
-function () {
-return this.details.toString ();
-});
-Clazz.overrideMethod (c$, "run",
-function () {
-var ps = ((Clazz.isClassDefined ("jalview.analysis.PCA$1") ? 0 : jalview.analysis.PCA.$PCA$1$ ()), Clazz.innerTypeInstance (jalview.analysis.PCA$1, this, null, System.out));
-try {
-this.details.append ("PCA Calculation Mode is " + (this.jvCalcMode ? "Jalview variant" : "Original SeqSpace") + "\n");
-var mt = this.m.transpose ();
-this.details.append (" --- OrigT * Orig ---- \n");
-if (!this.jvCalcMode) {
-this.eigenvector = mt.preMultiply (this.m);
-} else {
-this.eigenvector = mt.preMultiply (this.m2);
-}this.eigenvector.print (ps);
-this.symm = this.eigenvector.copy ();
-this.eigenvector.tred ();
-this.details.append (" ---Tridiag transform matrix ---\n");
-this.details.append (" --- D vector ---\n");
-this.eigenvector.printD (ps);
-ps.println ();
-this.details.append ("--- E vector ---\n");
-this.eigenvector.printE (ps);
-ps.println ();
-this.eigenvector.tqli ();
-} catch (q) {
-if (Clazz.exceptionOf (q, Exception)) {
-q.printStackTrace ();
-this.details.append ("\n*** Unexpected exception when performing PCA ***\n" + q.getLocalizedMessage ());
-this.details.append ("*** Matrices below may not be fully diagonalised. ***\n");
-} else {
-throw q;
-}
-}
-this.details.append (" --- New diagonalization matrix ---\n");
-this.eigenvector.print (ps);
-this.details.append (" --- Eigenvalues ---\n");
-this.eigenvector.printD (ps);
-ps.println ();
-});
-Clazz.defineMethod (c$, "setJvCalcMode",
-function (calcMode) {
-this.jvCalcMode = calcMode;
-}, "~B");
-c$.$PCA$1$ = function () {
-Clazz.pu$h ();
-c$ = Clazz.declareAnonymous (jalview.analysis, "PCA$1", java.io.PrintStream);
-Clazz.defineMethod (c$, "print",
-function (x) {
-this.b$["jalview.analysis.PCA"].details.append (x);
-}, "~S");
-Clazz.defineMethod (c$, "println",
-function () {
-this.b$["jalview.analysis.PCA"].details.append ("\n");
-});
-c$ = Clazz.p0p ();
-};
-});
+Clazz.declarePackage ("jalview.analysis");\r
+Clazz.load (["java.lang.StringBuffer"], "jalview.analysis.PCA", ["jalview.datamodel.BinarySequence", "jalview.math.Matrix", "jalview.schemes.ResidueProperties"], function () {\r
+c$ = Clazz.decorateAsClass (function () {\r
+this.m = null;\r
+this.symm = null;\r
+this.m2 = null;\r
+this.eigenvalue = null;\r
+this.eigenvector = null;\r
+this.details = null;\r
+this.jvCalcMode = true;\r
+Clazz.instantialize (this, arguments);\r
+}, jalview.analysis, "PCA", null, Runnable);\r
+Clazz.prepareFields (c$, function () {\r
+this.details = new StringBuffer ();\r
+});\r
+Clazz.makeConstructor (c$, \r
+function (s) {\r
+this.construct (s, false);\r
+}, "~A");\r
+Clazz.makeConstructor (c$, \r
+function (s, nucleotides) {\r
+this.construct (s, nucleotides, null);\r
+}, "~A,~B");\r
+Clazz.makeConstructor (c$, \r
+function (s, nucleotides, s_m) {\r
+var bs = new Array (s.length);\r
+var ii = 0;\r
+while ((ii < s.length) && (s[ii] != null)) {\r
+bs[ii] = new jalview.datamodel.BinarySequence (s[ii], nucleotides);\r
+bs[ii].encode ();\r
+ii++;\r
+}\r
+var bs2 = new Array (s.length);\r
+ii = 0;\r
+var smtrx = null;\r
+var sm = s_m;\r
+if (sm != null) {\r
+smtrx = jalview.schemes.ResidueProperties.getScoreMatrix (sm);\r
+}if (smtrx == null) {\r
+smtrx = jalview.schemes.ResidueProperties.getScoreMatrix (sm = (nucleotides ? "DNA" : "BLOSUM62"));\r
+}this.details.append ("PCA calculation using " + sm + " sequence similarity matrix\n========\n\n");\r
+while ((ii < s.length) && (s[ii] != null)) {\r
+bs2[ii] = new jalview.datamodel.BinarySequence (s[ii], nucleotides);\r
+if (smtrx != null) {\r
+try {\r
+bs2[ii].matrixEncode (smtrx);\r
+} catch (x) {\r
+if (Clazz.exceptionOf (x, jalview.datamodel.BinarySequence.InvalidSequenceTypeException)) {\r
+this.details.append ("Unexpected mismatch of sequence type and score matrix. Calculation will not be valid!\n\n");\r
+} else {\r
+throw x;\r
+}\r
+}\r
+}ii++;\r
+}\r
+var count = 0;\r
+while ((count < bs.length) && (bs[count] != null)) {\r
+count++;\r
+}\r
+var seqmat = Clazz.newDoubleArray (count, bs[0].getDBinary ().length, 0);\r
+var seqmat2 = Clazz.newDoubleArray (count, bs2[0].getDBinary ().length, 0);\r
+var i = 0;\r
+while (i < count) {\r
+seqmat[i] = bs[i].getDBinary ();\r
+seqmat2[i] = bs2[i].getDBinary ();\r
+i++;\r
+}\r
+this.m = new jalview.math.Matrix (seqmat, count, bs[0].getDBinary ().length);\r
+this.m2 = new jalview.math.Matrix (seqmat2, count, bs2[0].getDBinary ().length);\r
+}, "~A,~B,~S");\r
+Clazz.defineMethod (c$, "getM", \r
+function () {\r
+return this.m;\r
+});\r
+Clazz.defineMethod (c$, "getEigenvalue", \r
+function (i) {\r
+return this.eigenvector.d[i];\r
+}, "~N");\r
+Clazz.defineMethod (c$, "getComponents", \r
+function (l, n, mm, factor) {\r
+var out = Clazz.newFloatArray (this.m.rows, 3, 0);\r
+for (var i = 0; i < this.m.rows; i++) {\r
+out[i][0] = this.component (i, l) * factor;\r
+out[i][1] = this.component (i, n) * factor;\r
+out[i][2] = this.component (i, mm) * factor;\r
+}\r
+return out;\r
+}, "~N,~N,~N,~N");\r
+Clazz.defineMethod (c$, "component", \r
+function (n) {\r
+var out = Clazz.newDoubleArray (this.m.rows, 0);\r
+for (var i = 0; i < this.m.rows; i++) {\r
+out[i] = this.component (i, n);\r
+}\r
+return out;\r
+}, "~N");\r
+Clazz.defineMethod (c$, "component", \r
+function (row, n) {\r
+var out = 0.0;\r
+for (var i = 0; i < this.symm.cols; i++) {\r
+out += (this.symm.value[row][i] * this.eigenvector.value[i][n]);\r
+}\r
+return out / this.eigenvector.d[n];\r
+}, "~N,~N");\r
+Clazz.defineMethod (c$, "getDetails", \r
+function () {\r
+return this.details.toString ();\r
+});\r
+Clazz.overrideMethod (c$, "run", \r
+function () {\r
+var ps = ((Clazz.isClassDefined ("jalview.analysis.PCA$1") ? 0 : jalview.analysis.PCA.$PCA$1$ ()), Clazz.innerTypeInstance (jalview.analysis.PCA$1, this, null, System.out));\r
+try {\r
+this.details.append ("PCA Calculation Mode is " + (this.jvCalcMode ? "Jalview variant" : "Original SeqSpace") + "\n");\r
+var mt = this.m.transpose ();\r
+this.details.append (" --- OrigT * Orig ---- \n");\r
+if (!this.jvCalcMode) {\r
+this.eigenvector = mt.preMultiply (this.m);\r
+} else {\r
+this.eigenvector = mt.preMultiply (this.m2);\r
+}this.eigenvector.print (ps);\r
+this.symm = this.eigenvector.copy ();\r
+this.eigenvector.tred ();\r
+this.details.append (" ---Tridiag transform matrix ---\n");\r
+this.details.append (" --- D vector ---\n");\r
+this.eigenvector.printD (ps);\r
+ps.println ();\r
+this.details.append ("--- E vector ---\n");\r
+this.eigenvector.printE (ps);\r
+ps.println ();\r
+this.eigenvector.tqli ();\r
+} catch (q) {\r
+if (Clazz.exceptionOf (q, Exception)) {\r
+q.printStackTrace ();\r
+this.details.append ("\n*** Unexpected exception when performing PCA ***\n" + q.getLocalizedMessage ());\r
+this.details.append ("*** Matrices below may not be fully diagonalised. ***\n");\r
+} else {\r
+throw q;\r
+}\r
+}\r
+this.details.append (" --- New diagonalization matrix ---\n");\r
+this.eigenvector.print (ps);\r
+this.details.append (" --- Eigenvalues ---\n");\r
+this.eigenvector.printD (ps);\r
+ps.println ();\r
+});\r
+Clazz.defineMethod (c$, "setJvCalcMode", \r
+function (calcMode) {\r
+this.jvCalcMode = calcMode;\r
+}, "~B");\r
+c$.$PCA$1$ = function () {\r
+Clazz.pu$h ();\r
+c$ = Clazz.declareAnonymous (jalview.analysis, "PCA$1", java.io.PrintStream);\r
+Clazz.defineMethod (c$, "print", \r
+function (x) {\r
+this.b$["jalview.analysis.PCA"].details.append (x);\r
+}, "~S");\r
+Clazz.defineMethod (c$, "println", \r
+function () {\r
+this.b$["jalview.analysis.PCA"].details.append ("\n");\r
+});\r
+c$ = Clazz.p0p ();\r
+};\r
+});\r
git://source.jalview.org / jalviewjs.git / blobdiff